Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review
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paper_title: Analysis of Feature Space for Monitoring Persons with Parkinson's Disease With Application to a Wireless Wearable Sensor System

paper_content:

We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors. Optimal window length for feature extraction and feature ranking were calculated, based on their ability to capture motor fluctuations in persons with PD. Results from this study will be employed to develop a software platform for the wireless system, to efficiently process on-board data.
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paper_title: Parkinson's disease: clinical features and diagnosis

paper_content:

Objective: Parkinson’s disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. Methods: A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included “Parkinson’s disease”, “diagnosis” and “signs and symptoms”. Results: Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. Conclusions: A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.
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paper_title: Analysis of the severity of dyskinesia in patients with Parkinson's disease via wearable sensors

paper_content:

The aim of this study is to identify movement characteristics associated with motor fluctuations in patients with Parkinson's disease by relying on wearable sensors. Improved methods of assessing longitudinal changes in Parkinson's disease would enable optimization of treatment and maximization of patient function. We used eight accelerometers on the upper and lower limbs to monitor patients while they performed a set of standardized motor tasks. A video of the subjects was used by an expert to assign clinical scores. We focused on a motor complication referred to as dyskinesia, which is observed in association with medication intake. The sensor data were processed to extract a feature set responsive to the motor fluctuations. To assess the ability of accelerometers to capture the motor fluctuation patterns, the feature space was visualized using PCA and Sammon's mapping. Clustering analysis revealed the existence of intermediate clusters that were observed when changes occurred in the severity of dyskinesia. We present quantitative evidence that these intermediate clusters are the result of the high sensitivity of the proposed technique to changes in the severity of dyskinesia observed during motor fluctuation cycles.
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paper_title: Use of technological tools for Parkinson's disease early detection: A review

paper_content:

Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the severity of motor fluctuations in patients with Parkinson (PWP).
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paper_title: Online monitoring of dyskinesia in patients with Parkinson's disease

paper_content:

Discusses utilizing wearable movement sensors to successfully detect and assess severity of Parkinsonion symptoms in daily life. The authors describe the present state-of-the-art of online monitoring of motor behavior. They focus on modern monitoring equipment and on new analysis tools, which allow a quantitative detection and assessment of dyskinesias throughout the day, by an objective, unsupervised rating of dyskinesias using clinical rating scores.
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paper_title: Levodopa motor complications in Parkinson's disease

paper_content:

Parkinson's disease (PD) is an age-related neurodegenerative disorder with an average onset age of 60 years. In the United States, approximately one million persons suffer from PD, and there are 60,000 newly diagnosed cases every year. The estimated cost of PD to society is $27 billion per year. Based on United States Census Bureau projections, it is estimated that the frequency of PD will increase fourfold by the year 2040, making it an even larger burden on patients, their families and society.
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paper_title: The Mobile Sensing Platform: An Embedded Activity Recognition System

paper_content:

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.
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paper_title: Analysis of the severity of dyskinesia in patients with Parkinson's disease via wearable sensors

paper_content:

The aim of this study is to identify movement characteristics associated with motor fluctuations in patients with Parkinson's disease by relying on wearable sensors. Improved methods of assessing longitudinal changes in Parkinson's disease would enable optimization of treatment and maximization of patient function. We used eight accelerometers on the upper and lower limbs to monitor patients while they performed a set of standardized motor tasks. A video of the subjects was used by an expert to assign clinical scores. We focused on a motor complication referred to as dyskinesia, which is observed in association with medication intake. The sensor data were processed to extract a feature set responsive to the motor fluctuations. To assess the ability of accelerometers to capture the motor fluctuation patterns, the feature space was visualized using PCA and Sammon's mapping. Clustering analysis revealed the existence of intermediate clusters that were observed when changes occurred in the severity of dyskinesia. We present quantitative evidence that these intermediate clusters are the result of the high sensitivity of the proposed technique to changes in the severity of dyskinesia observed during motor fluctuation cycles.
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paper_title: Use of technological tools for Parkinson's disease early detection: A review

paper_content:

Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the severity of motor fluctuations in patients with Parkinson (PWP).
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paper_title: Human Activity Recognition from Accelerometer Data Using a Wearable Device

paper_content:

Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
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paper_title: Mobile Systems as a Challenge for Neurological Diseases Management – The Case of Parkinson's Disease

paper_content:

Nowadays the importance of bio-medical engineering and mobile applications for healthcare is amazingly growing. During the last decades many devices and technological solutions have become available on the market and the interest in applying those technologies to the treatment of several kinds of pathologies has consequently increased. This chapter addresses the problem of continuous monitoring of patients affected by Parkinson’s Disease (PD) and proposes a set of technologies to improve the following and management of such subjects. PD is a neurodegenerative disorder of the central nervous system that affects motor skills and speech (Tolosa, 1998). The primary biochemical abnormality in PD is a deficiency of dopamine due to degeneration of neurons in the substantia nigra pars compact (D. G. Standaert & Young, 2001). The characteristic motor features of the disease include bradykinesia (i.e. slowness of movement), tremor, rigidity (i.e. resistance to externally imposed movements), flexed posture, postural instability and freezing of gait. Furthermore, PD is usually characterised by the loss of normal prosody of the speech (Darkins et al., 1988). According to the World Health Organisation [WHO], 2002), there are more than six million people worldwide affected by PD. The syndrome typically appears around the age of 60. It affects Europeans and North Americans more often than Asians or Africans and it is more common in men than in women. PD affects about 2% of the population over the age of 65 years, figure that is expected to double by 2020 (de Lau & Breteler, 2006). For those reasons, PD poses a significant public health burden, which is likely to increase in the coming years. Annual medical care, including doctors’ visits, physical therapies and treatment for cooccurring illnesses -such as depressionis estimated at $2,000 to $7,000 for people in early stages of the disease, and it is probably much higher for advanced stages. Surgical treatments for PD can cost $25,000 or more. As the disease progresses, institutional care at an assisted-living facility or nursing home may be required, and the related costs can exceed $100,000, per person annually. Technology in general and specifically ICT might be an affordable alternative for PD’s patients’ treatment and management. The development of platforms for remote health
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paper_title: Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Process, format, and clinimetric testing plan

paper_content:

This article presents the revision process, major innovations, and clinimetric testing program for the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS), known as the MDS-UPDRS. The UPDRS is the most widely used scale for the clinical study of Parkinson's disease (PD). The MDS previously organized a critique of the UPDRS, which cited many strengths, but recommended revision of the scale to accommodate new advances and to resolve problematic areas. An MDS-UPDRS committee prepared the revision using the recommendations of the published critique of the scale. Subcommittees developed new material that was reviewed by the entire committee. A 1-day face-to-face committee meeting was organized to resolve areas of debate and to arrive at a working draft ready for clinimetric testing. The MDS-UPDRS retains the UPDRS structure of four parts with a total summed score, but the parts have been modified to provide a section that integrates nonmotor elements of PD: I, Nonmotor Experiences of Daily Living; II, Motor Experiences of Daily Living; III, Motor Examination; and IV, Motor Complications. All items have five response options with uniform anchors of 0 = normal, 1 = slight, 2 = mild, 3 = moderate, and 4 = severe. Several questions in Part I and all of Part II are written as a patient/caregiver questionnaire, so that the total rater time should remain approximately 30 minutes. Detailed instructions for testing and data acquisition accompany the MDS-UPDRS in order to increase uniform usage. Multiple language editions are planned. A three-part clinimetric program will provide testing of reliability, validity, and responsiveness to interventions. Although the MDS-UPDRS will not be published until it has successfully passed clinimetric testing, explanation of the process, key changes, and clinimetric programs allow clinicians and researchers to understand and participate in the revision process.
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paper_title: Evaluation of Dyskinesias in a Pilot, Randomized, Placebo-Controlled Trial of Remacemide in Advanced Parkinson Disease

paper_content:

CONTEXT ::: Long-term levodopa therapy for Parkinson disease commonly results in motor complications including "on-off" fluctuations and dyskinesias, but it is still unclear how best to assess treatment effects on dyskinesias in clinical trials. ::: ::: ::: OBJECTIVE ::: To compare several methods of rating levodopa-induced dyskinesias to evaluate the effect of remacemide hydrochloride treatment in patients with advanced Parkinson disease. ::: ::: ::: DESIGN ::: Two-week multicenter randomized, double-blind, placebo-controlled, parallel-group study. ::: ::: ::: SETTING ::: Five academic sites of the Parkinson Study Group. ::: ::: ::: PATIENTS ::: Thirty-nine subjects at least 30 years old with idiopathic Parkinson disease and disabling dyskinesias. ::: ::: ::: INTERVENTIONS ::: Randomly received daily doses of 150 mg, 300 mg, or 600 mg of remacemide hydrochloride or matching placebo for 2 weeks. ::: ::: ::: MAIN OUTCOME MEASURES ::: The dyskinesia rating scales used were the Modified Goetz Dyskinesia Rating scale (MGDRS), a newly created Lang-Fahn Activities of Daily Living Dyskinesia scale (LFADLDS), and diary dyskinesia ratings. ::: ::: ::: RESULTS ::: Patient and investigator diaries showed excellent agreement in dyskinesia ratings. The MGDRS score correlated with clinic diary ratings of the percentage of "on" time with dyskinesias, and the LFADLDS score correlated with home and clinic diary assessments of percentage of on time with severe dyskinesias. The MGDRS score did not correlate highly with the LFADLDS score. This pilot study also validated previous results demonstrating the safety and tolerability of remacemide treatment for advanced Parkinson disease but did not result in any demonstrable improvement or worsening in dyskinesia measures. ::: ::: ::: CONCLUSIONS ::: Diaries may provide a valid means of evaluating dyskinesias in clinical trials for Parkinson disease, but there remain other aspects of dyskinesias, as assessed by the MGDRS and LFADLDS, that are not reflected in diary ratings.
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paper_title: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results

paper_content:

We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79–0.93 across parts) and correlated with the original UPDRS (ρ = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD. © 2008 Movement Disorder Society
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paper_title: Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach

paper_content:

Abstract Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson’s disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
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paper_title: Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

paper_content:

We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson’s disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.
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paper_title: Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG

paper_content:

The aim of this study was to investigate the influence of high degrees of motor unit synchronization on surface EMG variables extracted by linear and non-linear analysis techniques. For this purpose, spectral and recurrent quantification analysis (RQA) were applied to both simulated and experimental EMG signals. Synthetic surface EMG signals were generated with a model of volume conductor comprising muscle, fat, and skin tissues. The synchronization was quantified by the percent of discharges of each motor unit synchronized with discharges of other motor units. The simulated signals presented degrees of synchronization in the range 0–80% (10% increments) and three mean values of motor unit conduction velocity distribution (3, 4 and 5 m/s). Experimental signals were collected from the first dorsal interosseous muscle of five patients with Parkinson disease during 10 s of rest and 10 s of isometric voluntary contraction at 50% of the maximal force. Mean power spectral frequency (MNF) and percent of determinism (%DET) of the surface EMG were computed from the simulated and experimental signals. In the simulated signals, %DET was linearly related to the level of synchronization in the entire range considered while MNF was sensitive to changes in synchronization in a smaller range (0–20%), outside which it levelled off. The experimental results indicated that %DET was significantly higher in the resting condition (with presence of tremor; mean ± S.E., 85.4 ± 0.8%) than during the voluntary contraction (which partly suppressed tremor; 60.0 ± 2.3%; P < 0.01). On the contrary, MNF did not depend on the condition (114.3 ± 1.5 Hz and 118.0 ± 0.8 Hz for the resting and voluntary contraction, respectively), confirming the simulation results. Overall, these results indicated that linear and non-linear analyses of the surface EMG may have different sensitivities to the underlying physiological mechanisms in specific conditions, thus their joint use provides a more complete view of the muscle status than spectral analysis only.
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paper_title: Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls.

paper_content:

The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinson's disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.
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paper_title: EMG signal morphology in essential tremor and Parkinson's disease

paper_content:

The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.
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paper_title: Analysis of surface EMG signal morphology in Parkinson's disease

paper_content:

A novel approach is presented for the analysis of surface electromyogram (EMG) morphology in Parkinson's disease (PD). The method is based on histogram and crossing rate (CR) analysis of the EMG signal. In the method, histograms and CR values are used as high-dimensional feature vectors. The dimensionality of them is then reduced using the Karhunen-Loève transform (KLT). Finally, the discriminant analysis of feature vectors is performed in low-dimensional eigenspace. Histograms and CR values were chosen for analysis, because Parkinsonian EMG signals typically involve patterns of EMG bursts. Traditional methods of EMG amplitude and spectral analysis are not effective in analyzing impulse-like signals. The method, which was tested with EMG signals measured from 25 patients with PD and 22 healthy controls, was promising for discriminating between these two groups of subjects. The ratio of correct discrimination by augmented KLT was 86% for the control group and 72% for the patient group. On the basis of these results, further studies are suggested in order to evaluate the usability of this method in early stage diagnostics of PD.
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paper_title: Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach

paper_content:

Abstract Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson’s disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
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paper_title: Ambulatory motor assessment in Parkinson's disease

paper_content:

We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty-three patients were monitored continuously in a home-like situation for approximately 3 hours while they carried out normal daily-life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on-off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa-induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions.
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paper_title: Detecting freezing-of-gait during unscripted and unconstrained activity

paper_content:

We present a dynamic neural network (DNN) solution for detecting instances of freezing-of-gait (FoG) in Parkinson's disease (PD) patients while they perform unconstrained and unscripted activities. The input features to the DNN are derived from the outputs of three triaxial accelerometer (ACC) sensors and one surface electromyographic (EMG) sensor worn by the PD patient. The ACC sensors are placed on the shin and thigh of one leg and on one of the forearms while the EMG sensor is placed on the shin. Our FoG solution is architecturally distinct from the DNN solutions we have previously designed for detecting dyskinesia or tremor. However, all our DNN solutions utilize the same set of input features from each EMG or ACC sensor worn by the patient. When tested on experimental data from PD patients performing unconstrained and unscripted activities, our FoG detector exhibited 83% sensitivity and 97% specificity on a per-second basis.
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paper_title: In vitro evaluation of finger's hemodynamics for vein graft surveillance using electrical bio-impedance method

paper_content:

Electrical bio-impedance measurement has great potential in many biomedical applications including vein graft surveillance. Studies have shown that thrombosis was the major cause of the vein graft failure. The meticulous skills of the surgeon and effective postoperative surveillance of vein graft remain the cornerstones of clinical success in the current surgical management of vein graft survival. Vascular blood flow is the key clinical indicators for the evaluation of patency of the vein graft and ensuring the patient's quality of life. In this work, electrical bio-impedance method has been proposed as an alternative to the existing surveillance method as it is non-invasive, portable, easy applicable in practice, fast response, radiation free, and required only low-cost instrumentation. It was employed to measure pulsatile changes in longitudinal bio-impedance to quantify arterial blood flow and blood volume. We expect that by measuring the changes in tissue bio-impedance which can be used to evaluate important peripheral hemodynamic, it allows the detection of early stage stenosis within vascular and vein graft as well as estimate its severity with predetermined normative data provided.
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paper_title: Analysis of Feature Space for Monitoring Persons with Parkinson's Disease With Application to a Wireless Wearable Sensor System

paper_content:

We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors. Optimal window length for feature extraction and feature ranking were calculated, based on their ability to capture motor fluctuations in persons with PD. Results from this study will be employed to develop a software platform for the wireless system, to efficiently process on-board data.
---
paper_title: Parkinson's disease: clinical features and diagnosis

paper_content:

Objective: Parkinson’s disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. Methods: A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included “Parkinson’s disease”, “diagnosis” and “signs and symptoms”. Results: Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. Conclusions: A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.
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paper_title: Epidemiology of Parkinson's disease

paper_content:

The causes of Parkinson's disease (PD), the second most common neurodegenerative disorder, are still largely unknown. Current thinking is that major gene mutations cause only a small proportion of all cases and that in most cases, non-genetic factors play a part, probably in interaction with susceptibility genes. Numerous epidemiological studies have been done to identify such non-genetic risk factors, but most were small and methodologically limited. Larger, well-designed prospective cohort studies have only recently reached a stage at which they have enough incident patients and person-years of follow-up to investigate possible risk factors and their interactions. In this article, we review what is known about the prevalence, incidence, risk factors, and prognosis of PD from epidemiological studies.
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paper_title: Introduction to Wavelets and Wavelet Transforms: A Primer

paper_content:

1. Introduction to Wavelets. 2. A Multiresolution Formulation of Wavelet Systems. 3. Filter Banks and the Discrete Wavelet Transform. 4. Bases, Orthogonal Bases, Biorthogonal Bases, Frames, Tight Frames, and Unconditional Bases. 5. The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients. 6. Regularity, Moments, and Wavelet System Design. 7. Generalizations of the Basic Multiresolution Wavelet System. 8. Filter Banks and Transmultiplexers. 9. Calculation of the Discrete Wavelet Transform. 10. Wavelet-Based Signal Processing and Applications. 11. Summary Overview. 12. References. Bibliography. Appendix A. Derivations for Chapter 5 on Scaling Functions. Appendix B. Derivations for Section on Properties. Appendix C. Matlab Programs. Index.
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paper_title: Use of technological tools for Parkinson's disease early detection: A review

paper_content:

Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the severity of motor fluctuations in patients with Parkinson (PWP).
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paper_title: Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging

paper_content:

BACKGROUND ::: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. ::: ::: ::: MATERIALS AND METHODS ::: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method. ::: ::: ::: RESULTS ::: Using the proposed methods to classify the data set, good results (accuracy = 86.96%, sensitivity = 78.95%, specificity = 92.59%) were obtained. ::: ::: ::: CONCLUSIONS ::: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.
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paper_title: Levodopa therapy: consequences of the nonphysiologic replacement of dopamine.

paper_content:

Normal motor function is dependent on the highly regulated synthesis and release of the transmitter dopamine by neurons projecting from the substantia nigra to the corpus striatum. Parkinson's disease involves the progressive degeneration of these neurons. Its core symptoms are a direct consequence of a striatal insufficiency of intrasynaptic dopamine. Levodopa, the standard of care for the treatment of PD, acts after its conversion to dopamine by restoring striatal dopaminergic transmission. However, there are significant differences between the normally functioning dopamine system and the restoration of function provided by standard levodopa treatment. Increasing clinical and preclinical evidence suggests that the intermittent stimulation of dopamine receptors resulting from current therapeutic regimens contributes to the response complications that ultimately affect most parkinsonian patients. It now appears that chronic nonphysiologic stimulation of dopaminergic receptors on striatal GABAergic neurons activates characteristic signaling pathways, leading to a potentiation of the synaptic efficacy of adjacent glutamatergic receptors of the N-methyl-D-aspartate (NMDA) subtype. As a result, function of these GABAergic efferent neurons changes in ways that favor the appearance of motor complications. Conceivably, use of dopaminomimetic replacement strategies that provide more continuous dopamine receptor stimulation will act to prevent or alleviate these disabling complications. A number of promising approaches to achieving this goal are now under development.
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paper_title: Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring

paper_content:

An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r=-0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.
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paper_title: Online monitoring of dyskinesia in patients with Parkinson's disease

paper_content:

Discusses utilizing wearable movement sensors to successfully detect and assess severity of Parkinsonion symptoms in daily life. The authors describe the present state-of-the-art of online monitoring of motor behavior. They focus on modern monitoring equipment and on new analysis tools, which allow a quantitative detection and assessment of dyskinesias throughout the day, by an objective, unsupervised rating of dyskinesias using clinical rating scores.
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paper_title: Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

paper_content:

We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson’s disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.
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paper_title: Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG

paper_content:

The aim of this study was to investigate the influence of high degrees of motor unit synchronization on surface EMG variables extracted by linear and non-linear analysis techniques. For this purpose, spectral and recurrent quantification analysis (RQA) were applied to both simulated and experimental EMG signals. Synthetic surface EMG signals were generated with a model of volume conductor comprising muscle, fat, and skin tissues. The synchronization was quantified by the percent of discharges of each motor unit synchronized with discharges of other motor units. The simulated signals presented degrees of synchronization in the range 0–80% (10% increments) and three mean values of motor unit conduction velocity distribution (3, 4 and 5 m/s). Experimental signals were collected from the first dorsal interosseous muscle of five patients with Parkinson disease during 10 s of rest and 10 s of isometric voluntary contraction at 50% of the maximal force. Mean power spectral frequency (MNF) and percent of determinism (%DET) of the surface EMG were computed from the simulated and experimental signals. In the simulated signals, %DET was linearly related to the level of synchronization in the entire range considered while MNF was sensitive to changes in synchronization in a smaller range (0–20%), outside which it levelled off. The experimental results indicated that %DET was significantly higher in the resting condition (with presence of tremor; mean ± S.E., 85.4 ± 0.8%) than during the voluntary contraction (which partly suppressed tremor; 60.0 ± 2.3%; P < 0.01). On the contrary, MNF did not depend on the condition (114.3 ± 1.5 Hz and 118.0 ± 0.8 Hz for the resting and voluntary contraction, respectively), confirming the simulation results. Overall, these results indicated that linear and non-linear analyses of the surface EMG may have different sensitivities to the underlying physiological mechanisms in specific conditions, thus their joint use provides a more complete view of the muscle status than spectral analysis only.
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paper_title: An ambulatory system to quantify bradykinesia and tremor in Parkinson's disease

paper_content:

Two groups of 10 Parkinson's disease (PD) patients and 10 age-matched normal subjects performing normal life activities were recorded using a portable recorder and 3D gyroscopes. PD patients had sub-thalamic stimulation implantation for 20/spl plusmn/3 months. The PD patients performed the test twice: with and without stimulation. Bradykinesia and tremor of hands in PD patients was quantified and strong correlation between this objective method and unified Parkinson's disease rating scale (UPDRS) clinical score was found.
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paper_title: Mobile Systems as a Challenge for Neurological Diseases Management – The Case of Parkinson's Disease

paper_content:

Nowadays the importance of bio-medical engineering and mobile applications for healthcare is amazingly growing. During the last decades many devices and technological solutions have become available on the market and the interest in applying those technologies to the treatment of several kinds of pathologies has consequently increased. This chapter addresses the problem of continuous monitoring of patients affected by Parkinson’s Disease (PD) and proposes a set of technologies to improve the following and management of such subjects. PD is a neurodegenerative disorder of the central nervous system that affects motor skills and speech (Tolosa, 1998). The primary biochemical abnormality in PD is a deficiency of dopamine due to degeneration of neurons in the substantia nigra pars compact (D. G. Standaert & Young, 2001). The characteristic motor features of the disease include bradykinesia (i.e. slowness of movement), tremor, rigidity (i.e. resistance to externally imposed movements), flexed posture, postural instability and freezing of gait. Furthermore, PD is usually characterised by the loss of normal prosody of the speech (Darkins et al., 1988). According to the World Health Organisation [WHO], 2002), there are more than six million people worldwide affected by PD. The syndrome typically appears around the age of 60. It affects Europeans and North Americans more often than Asians or Africans and it is more common in men than in women. PD affects about 2% of the population over the age of 65 years, figure that is expected to double by 2020 (de Lau & Breteler, 2006). For those reasons, PD poses a significant public health burden, which is likely to increase in the coming years. Annual medical care, including doctors’ visits, physical therapies and treatment for cooccurring illnesses -such as depressionis estimated at $2,000 to $7,000 for people in early stages of the disease, and it is probably much higher for advanced stages. Surgical treatments for PD can cost $25,000 or more. As the disease progresses, institutional care at an assisted-living facility or nursing home may be required, and the related costs can exceed $100,000, per person annually. Technology in general and specifically ICT might be an affordable alternative for PD’s patients’ treatment and management. The development of platforms for remote health
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paper_title: Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls.

paper_content:

The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinson's disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.
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paper_title: The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

paper_content:

Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as “feeling like their feet have been glued to the floor while trying to walk” and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG.
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paper_title: Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System

paper_content:

An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinson's disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinson's Disease Rating Scale (UPDRS) tremor subscore (e.g., r=0.87, p<0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r=-0.83, p<0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r=-0.74, p<0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities
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paper_title: Risk tables for parkinsonism and Parkinson's disease.

paper_content:

We applied the incidence rates of parkinsonism and Parkinson's disease (PD) from Olmsted County, MN (1976-1990) to a hypothetical cohort undergoing the mortality rates observed in Minnesota, and computed the lifetime risk and the remaining lifetime risk of developing parkinsonism and PD. These risks were compared to cumulative incidences that do not take competing risks of death into account. The lifetime risk of developing parkinsonism from birth was 4.4% for men and 3.7% for women (ratio = 1.2). The corresponding risk of developing PD was 2.0% for men and 1.3% for women (ratio = 1.5). Because of the opposite effect of higher incidence and higher mortality rates in men, the lifetime risks were only slightly higher in men than in women. Lifetime cumulative incidences were consistently higher than lifetime risks; this difference was more pronounced in men and in older subjects. Lifetime risk estimates are useful in clinical practice, epidemiologic research, and public health.
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paper_title: Freezing of gait: moving forward on a mysterious clinical phenomenon

paper_content:

Freezing of gait (FoG) is a unique and disabling clinical phenomenon characterised by brief episodes of inability to step or by extremely short steps that typically occur on initiating gait or on turning while walking. Patients with FoG, which is a feature of parkinsonian syndromes, show variability in gait metrics between FoG episodes and a substantial reduction in step length with frequent trembling of the legs during FoG episodes. Physiological, functional imaging, and clinical-pathological studies point to disturbances in frontal cortical regions, the basal ganglia, and the midbrain locomotor region as the probable origins of FoG. Medications, deep brain stimulation, and rehabilitation techniques can alleviate symptoms of FoG in some patients, but these treatments lack efficacy in patients with advanced FoG. A better understanding of the phenomenon is needed to aid the development of effective therapeutic strategies.
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paper_title: Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Process, format, and clinimetric testing plan

paper_content:

This article presents the revision process, major innovations, and clinimetric testing program for the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS), known as the MDS-UPDRS. The UPDRS is the most widely used scale for the clinical study of Parkinson's disease (PD). The MDS previously organized a critique of the UPDRS, which cited many strengths, but recommended revision of the scale to accommodate new advances and to resolve problematic areas. An MDS-UPDRS committee prepared the revision using the recommendations of the published critique of the scale. Subcommittees developed new material that was reviewed by the entire committee. A 1-day face-to-face committee meeting was organized to resolve areas of debate and to arrive at a working draft ready for clinimetric testing. The MDS-UPDRS retains the UPDRS structure of four parts with a total summed score, but the parts have been modified to provide a section that integrates nonmotor elements of PD: I, Nonmotor Experiences of Daily Living; II, Motor Experiences of Daily Living; III, Motor Examination; and IV, Motor Complications. All items have five response options with uniform anchors of 0 = normal, 1 = slight, 2 = mild, 3 = moderate, and 4 = severe. Several questions in Part I and all of Part II are written as a patient/caregiver questionnaire, so that the total rater time should remain approximately 30 minutes. Detailed instructions for testing and data acquisition accompany the MDS-UPDRS in order to increase uniform usage. Multiple language editions are planned. A three-part clinimetric program will provide testing of reliability, validity, and responsiveness to interventions. Although the MDS-UPDRS will not be published until it has successfully passed clinimetric testing, explanation of the process, key changes, and clinimetric programs allow clinicians and researchers to understand and participate in the revision process.
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paper_title: The symptom experience of patients with Parkinson's disease.

paper_content:

Studying the symptom experience of persons with Parkinson's disease (PD) could aid in determining the extent to which particular symptoms and symptom dimensions pose a problem and could assist in determining which of these to target for interventions. A descriptive correlational design with face-to-face interviews was used to study the extent of distress related to 13 symptoms of PD and relationships among symptom intensity, frequency, duration, and distress in 85 persons with PD. The mean number of symptoms reported was 7. The most distressing symptom was off time, followed by freezing gait, postural instability, sleep disturbance, and difficulty concentrating. Mean symptom distress scores ranged from slightly less to more than moderately. Symptom intensity and distress were strongly correlated, whereas the relationships of symptom frequency and duration to distress varied by symptom. Symptoms found in this study to be most distressing should be considered when interventions are being developed. It is likely that distress does make symptoms worse, so it is important to assess symptom intensity, duration, and frequency as well as distress when interventions to reduce distress are evaluated.
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paper_title: Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease

paper_content:

A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.
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paper_title: Evaluation of Dyskinesias in a Pilot, Randomized, Placebo-Controlled Trial of Remacemide in Advanced Parkinson Disease

paper_content:

CONTEXT ::: Long-term levodopa therapy for Parkinson disease commonly results in motor complications including "on-off" fluctuations and dyskinesias, but it is still unclear how best to assess treatment effects on dyskinesias in clinical trials. ::: ::: ::: OBJECTIVE ::: To compare several methods of rating levodopa-induced dyskinesias to evaluate the effect of remacemide hydrochloride treatment in patients with advanced Parkinson disease. ::: ::: ::: DESIGN ::: Two-week multicenter randomized, double-blind, placebo-controlled, parallel-group study. ::: ::: ::: SETTING ::: Five academic sites of the Parkinson Study Group. ::: ::: ::: PATIENTS ::: Thirty-nine subjects at least 30 years old with idiopathic Parkinson disease and disabling dyskinesias. ::: ::: ::: INTERVENTIONS ::: Randomly received daily doses of 150 mg, 300 mg, or 600 mg of remacemide hydrochloride or matching placebo for 2 weeks. ::: ::: ::: MAIN OUTCOME MEASURES ::: The dyskinesia rating scales used were the Modified Goetz Dyskinesia Rating scale (MGDRS), a newly created Lang-Fahn Activities of Daily Living Dyskinesia scale (LFADLDS), and diary dyskinesia ratings. ::: ::: ::: RESULTS ::: Patient and investigator diaries showed excellent agreement in dyskinesia ratings. The MGDRS score correlated with clinic diary ratings of the percentage of "on" time with dyskinesias, and the LFADLDS score correlated with home and clinic diary assessments of percentage of on time with severe dyskinesias. The MGDRS score did not correlate highly with the LFADLDS score. This pilot study also validated previous results demonstrating the safety and tolerability of remacemide treatment for advanced Parkinson disease but did not result in any demonstrable improvement or worsening in dyskinesia measures. ::: ::: ::: CONCLUSIONS ::: Diaries may provide a valid means of evaluating dyskinesias in clinical trials for Parkinson disease, but there remain other aspects of dyskinesias, as assessed by the MGDRS and LFADLDS, that are not reflected in diary ratings.
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paper_title: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results

paper_content:

We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79–0.93 across parts) and correlated with the original UPDRS (ρ = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD. © 2008 Movement Disorder Society
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paper_title: EMG signal morphology in essential tremor and Parkinson's disease

paper_content:

The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.
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paper_title: A system for analysis of tremor in patients with Parkinson's disease based on motion capture technique

paper_content:

Resting tremor is one of the primary motor symptoms of Parkinson's Disease. In this paper we analyze the occurrence of tremor in Parkinson's disease by using a system for measurements of kinematic data of upper limbs. The experimental group includes seven PD patients during standing. Analysis is based on kinematic measurements done by using multimodal motion capture (MOCAP) system for registration of 3D positions of body markers, ground reaction forces and electromyography signals. All patients taking part in examination have undergone deep brain stimulation surgical treatment. Examination involved comparisons of tremor parameters across four conditions, where stimulator was turned ON/OFF and medication was ON/OFF. Obtained results confirm statistically significant differences of certain tremor parameters between different experimental conditions.
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paper_title: Upper Extremity Vein Graft Monitoring Device after Surgery Procedure: A Preliminary Study

paper_content:

In most cases, surgical vein bypass or interposition vein grafting was used in both primary management of crush-avulsion amputations and on intervention for rehabilitating the patency of occluded arteries via microvascular surgery. However, surgical revascularization has significant shortcomings, principal among which is the high rate of accelerated thrombosis that develops in arterialised vein graft which renders the vein graft susceptible to acute occlusion and eventually give rise to graft failure. Evaluaion and detection of vein graft failure is essential as that will be the starting point for the clinician to make the diagnosis and safeguard patency of implanted vein graft which would otherwise fail. Unfortunately, most of the available diagnostic and monitoring tools available in the market are expensive, hence not all the hospital, private clinic and others medical centers that fully-equipped with these type of equipments. The objective of this study is to design and develop a low-cost and non-invasive vein graft monitoring prototype that able to provide high accuracy in predicting the vein graft patency and meanwhile providing the short-term monitoring on vein graft right after surgery procedure. Impedance plethysmography (IPG) was employed to measure pulsatile changes in longitudinal impedace to quantify arterial blood flow and pulsatile blood volume. Tetra-polar electrode measurement system was implemented by introduce a constant 1-mA AC current (I) at frequency of 100 kHz in the two outer electrodes. The voltage (V) is measured between the two inner electrodes, and the resulting impedance (Z) is calculated using Ohm’s Law. Arterial blood flow and pulsatile blood volume can then be estimated using impedance related volume conduction equation. By measuring the changes in electrical bioimpedance which can be used to derive important hemodynamic variables, it allows the postoperative graft surveillance and early detection atherosclerosis and thrombosis as well as estimate its severity that leads to the vein graft failure.
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paper_title: Wearable feedback systems for rehabilitation

paper_content:

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine.
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paper_title: Analysis of surface EMG signal morphology in Parkinson's disease

paper_content:

A novel approach is presented for the analysis of surface electromyogram (EMG) morphology in Parkinson's disease (PD). The method is based on histogram and crossing rate (CR) analysis of the EMG signal. In the method, histograms and CR values are used as high-dimensional feature vectors. The dimensionality of them is then reduced using the Karhunen-Loève transform (KLT). Finally, the discriminant analysis of feature vectors is performed in low-dimensional eigenspace. Histograms and CR values were chosen for analysis, because Parkinsonian EMG signals typically involve patterns of EMG bursts. Traditional methods of EMG amplitude and spectral analysis are not effective in analyzing impulse-like signals. The method, which was tested with EMG signals measured from 25 patients with PD and 22 healthy controls, was promising for discriminating between these two groups of subjects. The ratio of correct discrimination by augmented KLT was 86% for the control group and 72% for the patient group. On the basis of these results, further studies are suggested in order to evaluate the usability of this method in early stage diagnostics of PD.
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paper_title: Signal background estimation and baseline correction algorithms for accurate DNA sequencing

paper_content:

Accurate identification of a DNA sequence depends on the ability to precisely track the time varying signal baseline in all parts of the electrophoretic trace. We propose a statistical learning formulation of the signal background estimation problem that can be solved using an Expectation-Maximization type algorithm. We also present an alternative method for estimating the background level of a signal in small size windows based on a recursive histogram computation. Both background estimation algorithms introduced here can be combined with regression methods in order to track slow and fast baseline changes occurring in different regions of a DNA chromatogram. Accurate baseline tracking improves cluster separation and thus contributes to the reduction in classification errors when the Bayesian EM (BEM) base-calling system, developed in our group (Pereira et al., Discrete Applied Mathematics, 2000), is employed to decide how many bases are "hidden" in every base-call event pattern extracted from the chromatogram.
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paper_title: Levodopa motor complications in Parkinson's disease

paper_content:

Parkinson's disease (PD) is an age-related neurodegenerative disorder with an average onset age of 60 years. In the United States, approximately one million persons suffer from PD, and there are 60,000 newly diagnosed cases every year. The estimated cost of PD to society is $27 billion per year. Based on United States Census Bureau projections, it is estimated that the frequency of PD will increase fourfold by the year 2040, making it an even larger burden on patients, their families and society.
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paper_title: Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging

paper_content:

BACKGROUND ::: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. ::: ::: ::: MATERIALS AND METHODS ::: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method. ::: ::: ::: RESULTS ::: Using the proposed methods to classify the data set, good results (accuracy = 86.96%, sensitivity = 78.95%, specificity = 92.59%) were obtained. ::: ::: ::: CONCLUSIONS ::: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.
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paper_title: A system for analysis of tremor in patients with Parkinson's disease based on motion capture technique

paper_content:

Resting tremor is one of the primary motor symptoms of Parkinson's Disease. In this paper we analyze the occurrence of tremor in Parkinson's disease by using a system for measurements of kinematic data of upper limbs. The experimental group includes seven PD patients during standing. Analysis is based on kinematic measurements done by using multimodal motion capture (MOCAP) system for registration of 3D positions of body markers, ground reaction forces and electromyography signals. All patients taking part in examination have undergone deep brain stimulation surgical treatment. Examination involved comparisons of tremor parameters across four conditions, where stimulator was turned ON/OFF and medication was ON/OFF. Obtained results confirm statistically significant differences of certain tremor parameters between different experimental conditions.
---
paper_title: Signal background estimation and baseline correction algorithms for accurate DNA sequencing

paper_content:

Accurate identification of a DNA sequence depends on the ability to precisely track the time varying signal baseline in all parts of the electrophoretic trace. We propose a statistical learning formulation of the signal background estimation problem that can be solved using an Expectation-Maximization type algorithm. We also present an alternative method for estimating the background level of a signal in small size windows based on a recursive histogram computation. Both background estimation algorithms introduced here can be combined with regression methods in order to track slow and fast baseline changes occurring in different regions of a DNA chromatogram. Accurate baseline tracking improves cluster separation and thus contributes to the reduction in classification errors when the Bayesian EM (BEM) base-calling system, developed in our group (Pereira et al., Discrete Applied Mathematics, 2000), is employed to decide how many bases are "hidden" in every base-call event pattern extracted from the chromatogram.
---
paper_title: Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System

paper_content:

An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinson's disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinson's Disease Rating Scale (UPDRS) tremor subscore (e.g., r=0.87, p<0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r=-0.83, p<0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r=-0.74, p<0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities
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paper_title: Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease

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A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.
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paper_title: Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring

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An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r=-0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.
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paper_title: An ambulatory system to quantify bradykinesia and tremor in Parkinson's disease

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Two groups of 10 Parkinson's disease (PD) patients and 10 age-matched normal subjects performing normal life activities were recorded using a portable recorder and 3D gyroscopes. PD patients had sub-thalamic stimulation implantation for 20/spl plusmn/3 months. The PD patients performed the test twice: with and without stimulation. Bradykinesia and tremor of hands in PD patients was quantified and strong correlation between this objective method and unified Parkinson's disease rating scale (UPDRS) clinical score was found.
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paper_title: Ambulatory motor assessment in Parkinson's disease

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We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty-three patients were monitored continuously in a home-like situation for approximately 3 hours while they carried out normal daily-life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on-off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa-induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions.
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paper_title: Analysis of Feature Space for Monitoring Persons with Parkinson's Disease With Application to a Wireless Wearable Sensor System

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We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors. Optimal window length for feature extraction and feature ranking were calculated, based on their ability to capture motor fluctuations in persons with PD. Results from this study will be employed to develop a software platform for the wireless system, to efficiently process on-board data.
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paper_title: Analysis of the severity of dyskinesia in patients with Parkinson's disease via wearable sensors

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The aim of this study is to identify movement characteristics associated with motor fluctuations in patients with Parkinson's disease by relying on wearable sensors. Improved methods of assessing longitudinal changes in Parkinson's disease would enable optimization of treatment and maximization of patient function. We used eight accelerometers on the upper and lower limbs to monitor patients while they performed a set of standardized motor tasks. A video of the subjects was used by an expert to assign clinical scores. We focused on a motor complication referred to as dyskinesia, which is observed in association with medication intake. The sensor data were processed to extract a feature set responsive to the motor fluctuations. To assess the ability of accelerometers to capture the motor fluctuation patterns, the feature space was visualized using PCA and Sammon's mapping. Clustering analysis revealed the existence of intermediate clusters that were observed when changes occurred in the severity of dyskinesia. We present quantitative evidence that these intermediate clusters are the result of the high sensitivity of the proposed technique to changes in the severity of dyskinesia observed during motor fluctuation cycles.
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paper_title: Online monitoring of dyskinesia in patients with Parkinson's disease

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Discusses utilizing wearable movement sensors to successfully detect and assess severity of Parkinsonion symptoms in daily life. The authors describe the present state-of-the-art of online monitoring of motor behavior. They focus on modern monitoring equipment and on new analysis tools, which allow a quantitative detection and assessment of dyskinesias throughout the day, by an objective, unsupervised rating of dyskinesias using clinical rating scores.
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paper_title: Movement parameters that distinguish between voluntary movements and levodopa-induced dyskinesia in Parkinson s disease

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Abstract It is well known that long-term use of levodopa by patients with Parkinson’s disease causes dyskinesia. Several methods have been proposed for the automatic, unsupervised detection and classification of levodopa induced dyskinesia. Recently, we have demonstrated that neural networks are highly successful to detect dyskinesia and to distinguish dyskinesia from voluntary movements. The aim of this study was to use the trained neural networks to extract parameters, which are important to distinguish between dyskinesia and voluntary movements. Thirteen patients were continuously monitored in a home-like situation performing in about 35 daily life tasks for a period of approximately 2.5 h. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions of the body. A neural network was trained to assess the severity of dyskinesia. The neural network was able to assess the severity of dyskinesia and could distinguish dyskinesia from voluntary movements in daily life. For the trunk and the leg, the important parameters appeared to be the percentage of time that the trunk or leg was moving and the standard deviation of the segment velocity of the less dyskinetic leg. For the arm, the combination of the percentage of time, that the wrist was moving, and the percentage of time, that a patient was sitting, explained the largest part of the variance of the output. Dyskinesia differs from voluntary movements in the fact that dyskinetic movements tend to have lower frequencies than voluntary movements and in the fact that movements of different body segments are not well coordinated in dyskinesia.
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paper_title: Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors

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This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.
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paper_title: Detection and assessment of the severity of Levodopa-induced dyskinesia in patients with Parkinson's disease by neural networks

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Levodopa-induced dyskinesias (LID) in Parkinson's disease (PD) have remained a clinical challenge. We evaluated the feasibility of neural networks to detect LID and to quantify their severity in 16 patients with PD at rest and during various activities of daily living. The movements of the patients were measured using four pairs of accelerometers mounted on the wrist, upper arm, trunk, and leg on the most affected side. Using parameters obtained from the accelerometer signals, neural networks were trained to detect and to classify LID corresponding to the modified Abnormal Involuntary Movement Scale. Important parameters for classification appeared to be the mean segment velocity and the cross-correlation between accelerometers on the arm, trunk, and leg. Neural networks were able to distinguish voluntary movements from LID and to assess the severity of LID in various activities. Based on the results in this study, we conclude that neural networks are a valid and reliable method to detect and to assess the severity of LID corresponding to the modified Abnormal Involuntary Movement Scale.
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paper_title: Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application

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Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a web-based application provide reliable quantitative information that can be used for clinical decision making.
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paper_title: Gait assessment in Parkinson's disease patients through a network of wearable accelerometers in unsupervised environments

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Parkinson's disease (PD) predominantly alters the motor performance of the affected individuals. In particular, the loss of dopaminergic neurons compromises the speed, the automaticity and fluidity of movements. As the disease evolves, PD patient's motion becomes slower and tremoric and the response to medication fluctuates along the day. In addition, the presence of involuntary movements deteriorates voluntary movement in advanced state of the disease. These changes in the motion can be detected by studying the variation of the signals recorded by accelerometers attached in the limbs and belt of the patients. The analysis of the most significant changes in these signals make possible to build an individualized motor profile of the disease, allowing doctors to personalize the medication intakes and consequently improving the response of the patient to the treatment. Several works have been done in a laboratory and supervised environments providing solid results; this work focused on the design of unsupervised method for the assessment of gait in PD patients. The development of a reliable quantitative tool for long-term monitoring of PD symptoms would allow the accurate detection of the clinical status during the different PD stages and the evaluation of motor complications. Besides, it would be very useful both for routine clinical care as well as for novel therapies testing.
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paper_title: A Web-Based System for Home Monitoring of Patients With Parkinson's Disease Using Wearable Sensors

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This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.
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paper_title: Support Vector Machines for Multiple-Instance Learning

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This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristic ally. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharmaceutical data set and on applications in automated image indexing and document categorization.
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paper_title: Solving the multiple-instance problem with axis-parallel rectangles

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The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.
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paper_title: A Framework for Multiple-Instance Learning

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Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.
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paper_title: Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

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In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.
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paper_title: ACCELEROMETERS FOR QUANTIFICATION OF GAIT AND MOVEMENT DISORDERS: A PERSPECTIVE REVIEW

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Accelerometers have become increasingly integrated in the biomedical field, as they are highly portable and capable of objectively and reliably quantifying motion. Two specific applications for accelerometers are the quantification of gait and movement disorders, such as Parkinson's disease and essential tremor. The evolution of accelerometers to their present status is discussed. Accelerometry is contrasted with more traditional means for accessing gait and movement disorders. Advances in the research validation of accelerometers for the characterization of gait and movement disorders, such as essential tremor and Parkinson's disease, are addressed. The review concludes with the advancement of three-dimensional (3D) wireless accelerometers and pertinent future implications.
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paper_title: Quantification of Parkinson's disease characteristics using wireless accelerometers

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Parkinson's disease is classified as a chronic movement disorder. The incidence of Parkinson's disease is proportional to age. The status of Parkinson's disease is characterized through the Unified Parkinson's Disease Rating Scale. The Unified Parkinson's Disease Rating Scale is an ordinal scale, for which the scale is qualitatively evaluated. The inherent issue of the ordinal scale is the lack of a temporal parameter to evaluate the attributes of the movement disorder. The evaluation of the Parkinson's Disease Rating Scale requires clinical specialization, occurring in a clinical environment. Accelerometers, through the advent of miniaturization, have reached a capacity to advance the evaluation of Parkinson's disease. Tremor characteristics and temporal attributes of Parkinson's disease can be readily quantified. Accelerometer systems have been tested and evaluated for ascertaining general status, drug therapy efficacy, and amelioration of Parkinson's disease based on deep brain stimulation parameter settings. Further advance of the accelerometer characterization of Parkinson's disease attributes involves the incorporation of a fully wearable system. Such a system is possible with the integration of wireless 3D MEMS accelerometers. The device proposed incorporates a wireless and potentially wearable 3D MEMS accelerometer mounted on the dorsum of the hand. The wireless 3D MEMS accelerometer system can potentially track the Parkinson's disease status through out real time for a subject at the home-based setting of the subject. An implication of the device is a subject database can be generated quantifying the progression of Parkinson's disease. Drug therapy dosage may be optimized with quantified feedback from the wireless 3D MEMS accelerometer system. Deep brain stimulation parameters may be further refined, and the conceptual foundation for real time deep brain stimulation parameter optimization is established. Enclosed is the initial test and evaluation of the wireless 3D MEMS accelerometer system through the quantification of simulated tremor.
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paper_title: Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application

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Parkinson's disease represents a chronic movement disorder, which is generally proportionally to age. The status of Parkinson's disease is traditionally classified through ordinal scale strategies, such as the Unified Parkinson's Disease Rating Scale. However, the application of the ordinal scale strategy inherently requires highly specialized and limited medical resources for interpretation. An alternative strategy involves the implementation of an iPhone application that enables the device to serve as a functional wireless accelerometer system. The Parkinson's disease tremor attributes may be recorded in either an effectively autonomous public or private setting, for which the resultant accelerometer signal of the tremor can be conveyed wireless and through email to a remote location for data post-processing. The initial testing and evaluation of the iPhone wireless accelerometer application for quantifying Parkinson's disease tremor successfully demonstrates the capacity to acquire tremor characteristics in an effectively autonomous environment, while potentially alleviating strain on limited and highly specialized medical resources.
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paper_title: Wireless accelerometer configuration for monitoring Parkinson’s disease hand tremor

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Parkinson’s disease is neurodegenerative in nature and associated with characteristic movement disorders, such as hand tremor. Wireless accelerometer applications may advance the quality of care for Parkinson’s disease patients. The acceleration waveform of the respective hand tremor can be recorded and stored for post-processing and progressive status tracking. A wireless accelerometer configuration for monitoring Parkinson’s disease hand tremor is presented. The proposed configuration is envisioned to be conducted with the assistance of a caregiver. For initial engineering proof of concept simulated Parkinson’s disease tremor is recorded through a wireless accelerometer node and contrasted to a statically positioned and tandem activated wireless accelerometer node. Statistical significance is acquired regarding the quantification of the simulated Parkinson’s disease tremor acceleration waveform and statically positioned acceleration waveform, while demonstrating a considerable degree of accuracy, consistency, and reliability.
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paper_title: A Wearable System to Assist Walking of Parkinson´s Disease Patients

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Background: About 50% of the patients with advanced Parkinson’s disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest. Objectives: The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides asistance based on pre-determined specifications. Methods: We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient. Results: Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation. Conclusions: With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients’ and physiotherapists’ perspectives on wear-abilty and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.
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paper_title: Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom

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In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.
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paper_title: Parkinsons disease patients perspective on context aware wearable technology for auditive assistance

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In this paper we present a wearable assistive technology for the freezing of gait (FOG) symptom in patients with Parkinsons disease (PD), with emphasis on subjective user appreciation. Patients with advanced PD often suffer from FOG, which is a sudden and transient inability to move. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non+pharmacologic treatments are of special interest.
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paper_title: About detection and diagnosis of Freezing of Gait

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This paper is about the initiative of detecting Freezing of Gait (FoG) in Parkinson Disease (PD). Summarizing more than twelve years of accumulated data, presenting the techniques used, and evaluating their results would create a reference paper for future works. We traced the development of diverse researches and sensor-based systems (acceleration, force resistive, angle, EMG, etc…) to expand and refine the results, pointing out flaws and demonstrating the advantages of each one. The paper sums up the investigations in one of the most unpredictable and impairing symptoms of PD, specifically the area of FoG detection and prevention.
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paper_title: Online detection of freezing of gait with smartphones and machine learning techniques

paper_content:

Freezing of gait (FoG) is a common gait deficit in advanced Parkinson's disease (PD). FoG events are associated with falls, interfere with daily life activities and impair quality of life. FoG is often resistant to pharmacologic treatment; therefore effective non-pharmacologic assistance is needed.
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paper_title: Predictors of freezing in Parkinson's disease: A survey of 6,620 patients

paper_content:

Data from a survey of 6,620 Parkinson's disease patients were examined for correlation of freezing with age, sex, duration, subjective severity of Parkinson's disease, and antiparkinsonian medication. Forty-seven percent of the patients reported experiencing freezing regularly. Logistic regression analysis showed that freezing was significantly associated with a longer disease duration and a more advanced stage of the disease. Freezing episodes were more likely in men than in women and in patients taking, in addition to levodopa, Entacapone, Amantadine, or dopamine agonists. Finally, patients considering tremor as their main symptom reported freezing less frequently. Common antiparkinsonian drugs given in combination with levodopa were not negatively correlated with freezing. The results underline the necessity to develop appropriate countermeasures against this phenomenon, which is widely known to cause significant impairment of patients' quality of life and—as our data also showed—may cause traffic accidents in licensed patients. © 2007 Movement Disorder Society
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paper_title: Falls and freezing of gait in Parkinson's disease: A review of two interconnected, episodic phenomena

paper_content:

Falls and freezing of gait are two "episodic" phe- nomena that are common in Parkinson's disease. Both symp- toms are often incapacitating for affected patients, as the asso- ciated physical and psychosocial consequences have a great impact on the patients' quality of life, and survival is dimin- ished. Furthermore, the resultant loss of independence and the treatment costs of injuries add substantially to the health care expenditures associated with Parkinson's disease. In this clin- ically oriented review, we summarise recent insights into falls and freezing of gait and highlight their similarities, differences, and links. Topics covered include the clinical presentation, recent ideas about the underlying pathophysiology, and the possibilities for treatment. A review of the literature and the current state-of-the-art suggests that clinicians should not feel deterred by the complex nature of falls and freezing of gait; a careful clinical approach may lead to an individually tailored treatment, which can offer at least partial relief for many affected patients. © 2004 Movement Disorder Society
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paper_title: Freezing of gait in patients with advanced Parkinson's disease

paper_content:

BACKGROUND ::: Freezing of Gait (FOG) is one of the most disturbing and least understood symptom in advanced stage of Parkinson's disease (PD). The contribution of the underlying pathological process and the antiparkinsonian treatment to the development of FOG are controversial. ::: ::: ::: OBJECTIVE ::: To study the relationships between clinical features of PD and therapeutic modalities in patients with advanced PD and FOG. ::: ::: ::: METHODS ::: Consecutive patients with 5 years or more of PD symptoms (n = 172) (99 men) with mean age at symptoms onset of 58.3 +/- 13.2 years and mean symptoms duration of 11.8 +/- 5.6 years were studied. Clinical data were collected during the last office visit through physical examination, detailed history, review of patients' charts, and other documents. A patient was considered as "freezer" if he/she reported recent experience that the legs got stuck to the ground while trying to walk. The presence of dyskinesia, early morning dystonia or significant postural reflex abnormalities were assessed through history and neurological examination. Duration of treatment with antiparkinsonian drugs was calculated from history charts. Chi square and t test were used to compare the patients with and without FOG. Logistic regression was used for the comparison of association between the presence of FOG (dependent variable) disease duration and disease stage (explanatory variables) and duration of treatment with anti-parkinsonian drugs. ::: ::: ::: RESULTS ::: The study population consisted of 45 patients at Hoehn and Yahr (H&Y) stage 2.5 (26%), 104 patients at stage 3 (60.5%), and 23 patients at H&Y stages 4-5 (13.5%). Ninety one patients (53%) reported FOG at the time of the study. Severity of the disease expressed by H&Y stage at "off" was a significant contributing factor for FOG with a significant trend (z = 4.38, p < 0.0001), as was longer duration of levodopa treatment, and confirmed by FOG using the multivariate logistic regression (p = 0.01 and p = 0.004, respectively). Using a univariate model, longer duration of treatment with dopamine agonists contribute to the appearance of FOG (p = 0.07) while longer duration of amantadine treatment decreased the appearance of FOG (p = 0.09). There was a significant association between FOG and the presence of dyskinesia (p < 0.002), early morning foot dystonia (p < 0.003) and significant postural instability (p < 0.0005). ::: ::: ::: CONCLUSION ::: FOG is a common symptom in advanced PD. It is mainly related to disease progression and levodopa treatment.
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paper_title: Potentials of Enhanced Context Awareness in Wearable Assistants for Parkinson's Disease Patients with the Freezing of Gait Syndrome

paper_content:

Freezing of gait (FOG) is a common gait deficit in advanced Parkinson’s disease (PD). It is often a cause of falls, interferes with daily activities and significantly impairs quality of life. Gait deficits in PD patients are often resistant to pharmacologic treatment; therefore effective nonpharmacologic assistance is needed.In this paper we show the potential of context aware assistance for PD patients with FOG and present our first results on start and turn FOG assistance using our modular wearable research platform. We developed a real-time FOG detection system which provides external acoustic cues when FOG is detected from on-body motion sensors, until the subject resumes walking. In an evaluation study, ten PD patients tested our device. We recorded over 8h of data. Eight patients experienced FOG during the study, and 237 FOG events have been identified by physiotherapists in a post video analysis. For the first time PD patients with the FOG syndrome were assisted by a context-aware wearable system. We report a high accuracy of freeze detection (73.1% sensitivity, 81.6% specificity, user independent). Based on subjective reports, the majority of patients indicated a benefit from the automatic cueing.We discuss how additional sensor modalities can paint a more complete view of the user’s context and may increase the system’s accuracy, decrease its latency, and eventually allow going from freeze detection to freeze preemption.
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paper_title: Feature learning for detection and prediction of freezing of gait in parkinson's disease

paper_content:

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impact the patient's quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field - Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. ::: ::: We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.
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paper_title: Ambulatory monitoring of freezing of gait in Parkinson's disease

paper_content:

Freezing of gait (FOG) is common in advanced Parkinson’s disease (PD), is resistant to treatment and negatively impacts quality of life. In this study an ambulatory FOG monitor was validated in 11 PD patients. The vertical linear acceleration of the left shank was acquired using an ankle-mounted sensor array that transmitted data wirelessly to a pocket PC at a rate of 100 Hz. Power analysis showed high-frequency components of leg movement during FOG in the 3–8 Hz band that were not apparent during volitional standing, and power in this ‘freeze’ band was higher (p = 0.00003) during FOG preceded by walking (turning or obstacles) than FOG preceded by rest (gait initiation). A freeze index (FI) was defined as the power in the ‘freeze’ band divided by the power in the ‘locomotor’ band (0.5–3 Hz) and a threshold chosen such that FI values above this limit were designated as FOG. A global threshold detected 78% of FOG events and 20% of stand events were incorrectly labeled as FOG. Individual calibration of the freeze threshold improved accuracy and sensitivity of the device to 89% for detection of FOG with 10% false positives. Ambulatory monitoring may significantly improve clinical management of FOG. © 2007 Elsevier B.V. All rights reserved.
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paper_title: Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering

paper_content:

This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California – Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstr...
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paper_title: A hybrid expert system for automatic detection of voice disorders

paper_content:

Pathological voice analysis is a challenging task and an important area of research in voice disorder identification. Until now, the long-time acoustic (LTA) parameters are used primitively to classify the disordered voices into pathological and normal. Selection of such optimal LTA features is a disputing task. Previous researchers have used various data projection methods like principle component analysis (PCA), linear discriminant analysis (LDA) and sub-optimal searching techniques like sequential forward selection (SFS), sequential backward selection (SBS), and individual feature selection (IFS) methods for this purpose. But, these methods work efficiently for linearly separable datasets only. In order to overcome these issues, we propose a hybrid expert system in this paper, which includes the optimal selection of LTA parameters using genetic algorithm (GA), followed by non-linear classification algorithms to classify the two classes of voice samples. Nowadays, though many non-linear and high-order spectral parameters of voices have been used in this application, LTA features are scoring more importance because their clinical diagnosis is of more ease. Within this context, the GA-based feature vector quantisation combined with SVM classification is demonstrated to be more reliable, yielding 96.86% of classification accuracy for a feature vector of length 10.
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paper_title: A new hybrid intelligent system for accurate detection of Parkinson's disease

paper_content:

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
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paper_title: Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

paper_content:

Tracking Parkinson's disease (PD) symptom progression often uses the unified Parkinson's disease rating scale (UPDRS) that requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and noninvasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques that include classical least squares and nonparametric classification and regression trees. We verify our findings on the largest database of PD speech in existence (~6000 recordings from 42 PD patients, recruited to a six-month, multicenter trial). These findings support the feasibility of frequent, remote, and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.
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paper_title: Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

paper_content:

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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paper_title: Classification of Parkinson's disease based on Multilayer Perceptrons (MLPs) Neural Network and ANOVA as a feature extraction

paper_content:

Parkinson's disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer's disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD with Analysis of Variance (ANOVA) as a feature selection. The dataset information of this project has been taken from the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of above 90% before and after feature selection whilst SSG attained above 85% subsequent to implementation of ANOVA as feature selection.
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paper_title: Parametric Quantitative Acoustic Analysis of Conversation Produced by Speakers With Dysarthria and Healthy Speakers

paper_content:

Purpose This study’s main purpose was to (a) identify acoustic signatures of hypokinetic dysarthria (HKD) that are robust to phonetic variation in conversational speech and (b) determine specific c...
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paper_title: Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning

paper_content:

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.
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paper_title: Clinical Measurement Of Speech And Voice

paper_content:

Thank you very much for reading clinical measurement of speech and voice. As you may know, people have look hundreds times for their chosen readings like this clinical measurement of speech and voice, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their desktop computer. clinical measurement of speech and voice is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the clinical measurement of speech and voice is universally compatible with any devices to read.
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paper_title: Phonatory impairment in Parkinson's disease: evidence from nonlinear dynamic analysis and perturbation analysis.

paper_content:

Summary Many persons with Parkinson's disease (PD) will eventually experience vocal impairment as their condition advances. Using standard perturbation analyses (parameters like jitter and shimmer) to measure fluctuations in phonatory signal may inhibit researchers from recognizing severely disordered patterns that seem to be present in the voices of some PD patients. Nonlinear dynamic analysis can quantify these aperiodic patterns, which indicate severe pathology that is usually characterized perceptually by hoarseness. Here, sustained vowel phonations of a heterogeneous group of PD subjects (20 women and 21 men) were compared with those of a control group (22 women and 18 men) based on results of nonlinear dynamic analyses (D 2 ) and perturbation analyses. Results showed PD subjects as a whole to have significantly higher D 2 values than control subjects ( P = 0.016), which indicates increased signal complexity in PD vocal pathology. Differences in the comparison of these two groups were significant in jitter ( P = 0.014) but nonsignificant in shimmer ( P = 0.695). Furthermore, the performance on these three measures was affected by subject sex. Nonlinear dynamic analysis showed significantly higher D 2 in the female PD group than in the female control group ( P = 0.001), but jitter and shimmer did not show such a difference. The male PD group had statistically higher jitter than the male control group ( P = 0.036), but these groups did not differ in D 2 or shimmer. Overall, nonlinear dynamic analysis may be a valuable method for the diagnosis of Parkinsonian laryngeal pathology.
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paper_title: Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease

paper_content:

Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.
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paper_title: Speech and swallowing symptoms associated with Parkinson's disease and multiple sclerosis: a survey.

paper_content:

A survey of approximately 460 patients with Parkinson's disease (PD) or multiple sclerosis (MS) shows that speech and swallowing difficulties are very frequent within these groups. Seventy percent of the PD patients and 44% of the MS patients had experienced impairment of speech and voice after the onset of their disease. Forty-one percent of the PD patients and 33% of the MS patients indicated impairment of chewing and swallowing abilities. The speech disorder was regarded as one of their greatest problems by 29% of the PD patients and by 16% of the MS patients. Only a small number of patients, 3% of the PD and 2% of the MS group, had received any speech therapy.
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paper_title: Feature selection using fuzzy entropy measures with similarity classifier

paper_content:

Feature selection plays an important role in classification for several reasons. First it can simplify the model and this way computational cost can be reduced and also when the model is taken for practical use fewer inputs are needed which means in practice that fewer measurements from new samples are needed. Second by removing insignificant features from the data set one can also make the model more transparent and more comprehensible, providing better explanation of suggested diagnosis, which is an important requirement in medical applications. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy entropy measures is introduced and it is tested together with similarity classifier. Model was tested with four medical data sets which were, dermatology, Pima-Indian diabetes, breast cancer and Parkinsons data set. With all the four data sets, we managed to get quite good results by using fewer features that in the original data sets. Also with Parkinsons and dermatology data sets, classification accuracy was managed to enhance significantly this way. Mean classification accuracy with Parkinsons data set being 85.03% with only two features from original 22. With dermatology data set, mean accuracy of 98.28% was achieved using 29 features instead of 34 original features. Results can be considered quite good.
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paper_title: An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach

paper_content:

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest neighbor (FKNN) for Parkinson's disease (PD) diagnosis. The proposed FKNN-based system is compared with the support vector machines (SVM) based approaches. In order to further improve the diagnosis accuracy for detection of PD, the principle component analysis was employed to construct the most discriminative new feature sets on which the optimal FKNN model was constructed. The effectiveness of the proposed system has been rigorously estimated on a PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results have demonstrated that the FKNN-based system greatly outperforms SVM-based approaches and other methods in the literature. The best classification accuracy (96.07%) obtained by the FKNN-based system using a 10-fold cross validation method can ensure a reliable diagnostic model for detection of PD. Promisingly, the proposed system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.
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paper_title: A new feature constituting approach to detection of vocal fold pathology

paper_content:

In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.
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paper_title: Speech impairment in a large sample of patients with Parkinson's disease.

paper_content:

This study classified speech impairment in 200 patients with Parkinson's disease (PD) into five levels of overall severity and described the corresponding type (voice, articulation, fluency) and extent (rated on a five-point scale) of impairment for each level. From two-minute conversational speech samples, parameters of voice, fluency and articulation were assessed by two trained-raters. Voice was found to be the leading deficit, most frequently affected and impaired to a greater extent than other features in the initial stages. Articulatory and fluency deficits manifested later, articulatory impairment matching voice impairment in frequency and extent at the `Severe' stage. At the final stage of `Profound' impairment, articulation was the most frequently impaired feature at the lowest level of performance. This study illustrates the prominence of voice and articulatory speech motor control deficits, and draws parallels with deficits of motor set and motor set instability in skeletal controls of gait and handwriting.
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paper_title: A parallel neural network approach to prediction of Parkinson's Disease

paper_content:

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson's Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson's Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.
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paper_title: Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

paper_content:

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.
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paper_title: Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression

paper_content:

Dysphonia measures are signal processing algorithms that offer an objective method for characterizing voice disorders from recorded speech signals. In this paper, we study disordered voices of people with Parkinson's disease (PD). Here, we demonstrate that a simple logarithmic transformation of these dysphonia measures can significantly enhance their potential for identifying subtle changes in PD symptoms. The superiority of the log-transformed measures is reflected in feature selection results using Bayesian Least Absolute Shrinkage and Selection Operator (LASSO) linear regression. We demonstrate the effectiveness of this enhancement in the emerging application of automated characterization of PD symptom progression from voice signals, rated on the Unified Parkinson's Disease Rating Scale (UPDRS), the gold standard clinical metric for PD. Using least squares regression, we show that UPDRS can be accurately predicted to within six points of the clinicians' observations.
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paper_title: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

paper_content:

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.
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paper_title: Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine

paper_content:

Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).
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paper_title: Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach

paper_content:

Abstract Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson’s disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
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paper_title: Detecting freezing-of-gait during unscripted and unconstrained activity

paper_content:

We present a dynamic neural network (DNN) solution for detecting instances of freezing-of-gait (FoG) in Parkinson's disease (PD) patients while they perform unconstrained and unscripted activities. The input features to the DNN are derived from the outputs of three triaxial accelerometer (ACC) sensors and one surface electromyographic (EMG) sensor worn by the PD patient. The ACC sensors are placed on the shin and thigh of one leg and on one of the forearms while the EMG sensor is placed on the shin. Our FoG solution is architecturally distinct from the DNN solutions we have previously designed for detecting dyskinesia or tremor. However, all our DNN solutions utilize the same set of input features from each EMG or ACC sensor worn by the patient. When tested on experimental data from PD patients performing unconstrained and unscripted activities, our FoG detector exhibited 83% sensitivity and 97% specificity on a per-second basis.
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paper_title: Cram's Introduction to Surface Electromyography

paper_content:

A comprehensive resource for your health professions students, including physical and occupational therapists, chiropractors, and behavioral medicine students, Introduction to Surface Electromyography clearly explains how to use SEMG to treat a variety of neuromuscular conditions. It covers the development of SEMG, instrumentation, assessment, and treatment, and features a detailed atlas for electrode placement. * Provides a comprehensive introduction to surface electromyography * Features a detailed electrode atlas with illustrations of 68 electrode placement sites * Includes helpful "quick reference" items throughout the text * Addresses the growing uses for surface electromyography * Includes data from the latest research studies on the use of SEMG in work performance, sports performance, rehabilitation, and movement analysis
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paper_title: The diagnosis of Parkinson's disease

paper_content:

Summary The correct diagnosis of Parkinson's disease is important for prognostic and therapeutic reasons and is essential for clinical research. Investigations of the diagnostic accuracy for the disease and other forms of parkinsonism in community-based samples of patients taking antiparkinsonian medication confirmed a diagnosis of parkinsonism in only 74% of patients and clinically probable Parkinson's disease in 53% of patients. Clinicopathological studies based on brain bank material from the UK and Canada have shown that clinicians diagnose the disease incorrectly in about 25% of patients. In these studies, the most common reasons for misdiagnosis were presence of essential tremor, vascular parkinsonism, and atypical parkinsonian syndromes. Infrequent diagnostic errors included Alzheimer's disease, dementia with Lewy bodies, and drug-induced parkinsonism. Increasing knowledge of the heterogeneous clinical presentation of the various parkinsonisms has resulted in improved diagnostic accuracy of the various parkinsonian syndromes in specialised movement-disorder units. Also genetic testing and various other ancillary tests, such as olfactory testing, MRI, and dopamine-transporter single-photon-emission computed-tomography imaging, help with clinical diagnostic decisions.
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paper_title: A hybrid expert system for automatic detection of voice disorders

paper_content:

Pathological voice analysis is a challenging task and an important area of research in voice disorder identification. Until now, the long-time acoustic (LTA) parameters are used primitively to classify the disordered voices into pathological and normal. Selection of such optimal LTA features is a disputing task. Previous researchers have used various data projection methods like principle component analysis (PCA), linear discriminant analysis (LDA) and sub-optimal searching techniques like sequential forward selection (SFS), sequential backward selection (SBS), and individual feature selection (IFS) methods for this purpose. But, these methods work efficiently for linearly separable datasets only. In order to overcome these issues, we propose a hybrid expert system in this paper, which includes the optimal selection of LTA parameters using genetic algorithm (GA), followed by non-linear classification algorithms to classify the two classes of voice samples. Nowadays, though many non-linear and high-order spectral parameters of voices have been used in this application, LTA features are scoring more importance because their clinical diagnosis is of more ease. Within this context, the GA-based feature vector quantisation combined with SVM classification is demonstrated to be more reliable, yielding 96.86% of classification accuracy for a feature vector of length 10.
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paper_title: A new hybrid intelligent system for accurate detection of Parkinson's disease

paper_content:

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
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paper_title: Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application

paper_content:

Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a web-based application provide reliable quantitative information that can be used for clinical decision making.
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paper_title: Introduction to Wavelets and Wavelet Transforms: A Primer

paper_content:

1. Introduction to Wavelets. 2. A Multiresolution Formulation of Wavelet Systems. 3. Filter Banks and the Discrete Wavelet Transform. 4. Bases, Orthogonal Bases, Biorthogonal Bases, Frames, Tight Frames, and Unconditional Bases. 5. The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients. 6. Regularity, Moments, and Wavelet System Design. 7. Generalizations of the Basic Multiresolution Wavelet System. 8. Filter Banks and Transmultiplexers. 9. Calculation of the Discrete Wavelet Transform. 10. Wavelet-Based Signal Processing and Applications. 11. Summary Overview. 12. References. Bibliography. Appendix A. Derivations for Chapter 5 on Scaling Functions. Appendix B. Derivations for Section on Properties. Appendix C. Matlab Programs. Index.
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paper_title: Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning

paper_content:

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.
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paper_title: Diagnosis of Parkinson's Disease by Electrophysiological Methods

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Effective differential diagnosis of Parkinson’s disease (PD) needs in informative indices that objectively reflect the functional state of the extrapyramidal system. And also, when evaluating an efficacy of antiparkinsonian therapy, it is essential to have both, qualitative and quantitative characteristics, permitting to correct treatment and predict a disease course. One of informative diagnostic method in PD is surface (interference) electromyography. As know, a nigrostriatal dopamine deficit results in disturbances of the central supraspinal control over the muscle tonic activity and voluntary movements (Valls-Sole & Valldeoriola, 2002). Electromyographically, the extrapyradimal insufficiency shows itself by a high level of bioelectrical activity of muscles at rest, changes of motor unit conduction velocity and synchronization (Farina et al., 2004; Semmler & Nordstrom, 1999). The traditional methods to evaluate surface electromyograms (EMGs) are based on amplitude and spectral analysis. However, myoelectric signals are nonlinear by its nature (Nieminen & Takala, 1996). A surface EMG is formed by the summation of a number of single muscle fiber action potentials. Therefore different world clinics have been searching for new relevant methods based on nonlinear time-series analyses of EMG to quantify the motor features of the disorder in PD (Del Santo et al., 2007; Meigal et al., 2009). Some other novel EMG characteristics, such as dimensionality based on fractal analysis or higher order statistics of EMG distribution have also proved to be sensitive to neuromuscular status (Swie et al., 2005). Although the cardinal symptoms of the disease are movement disorders the manifestations of PD also comprise a variety of diverse abnormalities including disturbance of sensory gating and cognitive decline (Lewis & Byblow, 2002). Several authors suggested that movement disorders in PD might be also developed because of dysregulation of sensory processing that affects sensorimotor integration (Abbruzzese & Berardelli, 2003). This is an important issue because one of the proposed key functions of basal ganglia is the gating of sensory input for motor control (Kaji, 2001). Numerous studies have demonstrated marked changes in the somatosensory (Rossini et al., 1998), acoustic (Teo et al., 1997;) and visual (Sadekov, 1997) evoked potentials in PD patients. Evaluation of brain evoked potentials may have potential in the assessment of the severity of PD. In contemporary neurophysiology,
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paper_title: Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging

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BACKGROUND ::: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. ::: ::: ::: MATERIALS AND METHODS ::: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method. ::: ::: ::: RESULTS ::: Using the proposed methods to classify the data set, good results (accuracy = 86.96%, sensitivity = 78.95%, specificity = 92.59%) were obtained. ::: ::: ::: CONCLUSIONS ::: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.
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paper_title: Gait assessment in Parkinson's disease patients through a network of wearable accelerometers in unsupervised environments

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Parkinson's disease (PD) predominantly alters the motor performance of the affected individuals. In particular, the loss of dopaminergic neurons compromises the speed, the automaticity and fluidity of movements. As the disease evolves, PD patient's motion becomes slower and tremoric and the response to medication fluctuates along the day. In addition, the presence of involuntary movements deteriorates voluntary movement in advanced state of the disease. These changes in the motion can be detected by studying the variation of the signals recorded by accelerometers attached in the limbs and belt of the patients. The analysis of the most significant changes in these signals make possible to build an individualized motor profile of the disease, allowing doctors to personalize the medication intakes and consequently improving the response of the patient to the treatment. Several works have been done in a laboratory and supervised environments providing solid results; this work focused on the design of unsupervised method for the assessment of gait in PD patients. The development of a reliable quantitative tool for long-term monitoring of PD symptoms would allow the accurate detection of the clinical status during the different PD stages and the evaluation of motor complications. Besides, it would be very useful both for routine clinical care as well as for novel therapies testing.
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paper_title: Effect of medication on EMG patterns in individuals with Parkinson's disease

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Individuals with Parkinson's disease show dramatic improvements in their ability to move when medicated. How- ever, the neural cause of this improvement is unclear. One hypothesis is that neural activation patterns, as measured by surface electromyography (EMG), are normalized by medica- tion. We tested this hypothesis by investigating the effect of medication on the electromyographic (EMG) patterns recorded when individuals with idiopathic Parkinson's disease per- formed elbow flexion movements over three movement dis- tances while off and on antiparkinsonian medication. When the subjects were off medication, they lacked the ability to modu- late the agonist EMG burst duration with changes in movement distance. The ability to modulate agonist EMG burst duration is characteristic of the EMG patterns observed in healthy subjects. Also, multiple agonist bursts were exhibited during the accel- eration phase. As expected, medication diminished the clinical signs of Parkinson's disease, increased movement speed, and increased the magnitude of the first agonist burst. Medication did not restore agonist burst duration modulation with move- ment distance, did not change the frequency of agonist burst- ing, and did not alter the timing of the antagonist activation. These results show that medication does not alter the temporal profile of EMG activation. © 2002 Movement Disorder Society
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paper_title: Quantification of Parkinson's disease characteristics using wireless accelerometers

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Parkinson's disease is classified as a chronic movement disorder. The incidence of Parkinson's disease is proportional to age. The status of Parkinson's disease is characterized through the Unified Parkinson's Disease Rating Scale. The Unified Parkinson's Disease Rating Scale is an ordinal scale, for which the scale is qualitatively evaluated. The inherent issue of the ordinal scale is the lack of a temporal parameter to evaluate the attributes of the movement disorder. The evaluation of the Parkinson's Disease Rating Scale requires clinical specialization, occurring in a clinical environment. Accelerometers, through the advent of miniaturization, have reached a capacity to advance the evaluation of Parkinson's disease. Tremor characteristics and temporal attributes of Parkinson's disease can be readily quantified. Accelerometer systems have been tested and evaluated for ascertaining general status, drug therapy efficacy, and amelioration of Parkinson's disease based on deep brain stimulation parameter settings. Further advance of the accelerometer characterization of Parkinson's disease attributes involves the incorporation of a fully wearable system. Such a system is possible with the integration of wireless 3D MEMS accelerometers. The device proposed incorporates a wireless and potentially wearable 3D MEMS accelerometer mounted on the dorsum of the hand. The wireless 3D MEMS accelerometer system can potentially track the Parkinson's disease status through out real time for a subject at the home-based setting of the subject. An implication of the device is a subject database can be generated quantifying the progression of Parkinson's disease. Drug therapy dosage may be optimized with quantified feedback from the wireless 3D MEMS accelerometer system. Deep brain stimulation parameters may be further refined, and the conceptual foundation for real time deep brain stimulation parameter optimization is established. Enclosed is the initial test and evaluation of the wireless 3D MEMS accelerometer system through the quantification of simulated tremor.
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paper_title: Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease

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Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.
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paper_title: Wearable sensor network for health monitoring: the case of Parkinson disease

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The aim of this paper is to show how wearable sensors can be useful in health solutions, improving the continuous monitoring and management of patients. This paper is focused on the available solution for motion analysis, providing a description of human motion features which can be measured through the use of wearable sensors. Moreover, this paper presents an example of wearable solution used for the objective assessment of Parkinson's disease symptoms. Results indicate that wearable sensors are useful for the objective evaluation of motor fluctuation and clinicians can benefit from these tools in order to adjust and personalise the treatment.
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paper_title: Mobile Systems as a Challenge for Neurological Diseases Management – The Case of Parkinson's Disease

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Nowadays the importance of bio-medical engineering and mobile applications for healthcare is amazingly growing. During the last decades many devices and technological solutions have become available on the market and the interest in applying those technologies to the treatment of several kinds of pathologies has consequently increased. This chapter addresses the problem of continuous monitoring of patients affected by Parkinson’s Disease (PD) and proposes a set of technologies to improve the following and management of such subjects. PD is a neurodegenerative disorder of the central nervous system that affects motor skills and speech (Tolosa, 1998). The primary biochemical abnormality in PD is a deficiency of dopamine due to degeneration of neurons in the substantia nigra pars compact (D. G. Standaert & Young, 2001). The characteristic motor features of the disease include bradykinesia (i.e. slowness of movement), tremor, rigidity (i.e. resistance to externally imposed movements), flexed posture, postural instability and freezing of gait. Furthermore, PD is usually characterised by the loss of normal prosody of the speech (Darkins et al., 1988). According to the World Health Organisation [WHO], 2002), there are more than six million people worldwide affected by PD. The syndrome typically appears around the age of 60. It affects Europeans and North Americans more often than Asians or Africans and it is more common in men than in women. PD affects about 2% of the population over the age of 65 years, figure that is expected to double by 2020 (de Lau & Breteler, 2006). For those reasons, PD poses a significant public health burden, which is likely to increase in the coming years. Annual medical care, including doctors’ visits, physical therapies and treatment for cooccurring illnesses -such as depressionis estimated at $2,000 to $7,000 for people in early stages of the disease, and it is probably much higher for advanced stages. Surgical treatments for PD can cost $25,000 or more. As the disease progresses, institutional care at an assisted-living facility or nursing home may be required, and the related costs can exceed $100,000, per person annually. Technology in general and specifically ICT might be an affordable alternative for PD’s patients’ treatment and management. The development of platforms for remote health
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paper_title: Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls.

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The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinson's disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.
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paper_title: The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

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Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as “feeling like their feet have been glued to the floor while trying to walk” and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG.
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paper_title: Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System

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An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinson's disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinson's Disease Rating Scale (UPDRS) tremor subscore (e.g., r=0.87, p<0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r=-0.83, p<0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r=-0.74, p<0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities
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paper_title: Advances in the treatment of Parkinson's disease

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Parkinson's disease (PD) affects one in every 100 persons above the age of 65 years, making it the second most common neurodegenerative disease after Alzheimer's disease. PD is a disease of the central nervous system that leads to severe difficulties with body motions. The currently available therapies aim to improve the functional capacity of the patient for as long as possible; however they do not modify the progression of the neurodegenerative process. The need for newer and more effective agents is consequently receiving a great deal of attention and consequently being subjected to extensive research. This review concisely compiles the limitations of currently available therapies and the most recent research regarding neuroprotective agents, antioxidants, stem cell research, vaccines and various surgical techniques available and being developed for the management of PD.
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paper_title: Advances in wearable technology for rehabilitation.

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Assessing the impact of rehabilitation interventions on the real life of individuals is a key element of the decision-making process required to choose a rehabilitation strategy. In the past, therapists and physicians inferred the effectiveness of a given rehabilitation approach from observations performed in a clinical setting and self-reports by patients. Recent developments in wearable technology have provided tools to complement the information gathered by rehabilitation personnel via patient's direct observation and via interviews and questionnaires. A new generation of wearable sensors and systems has emerged that allows clinicians to gather measures in the home and community settings that capture patients' activity level and exercise compliance, the effectiveness of pharmacological interventions, and the ability of patients to perform efficiently specific motor tasks. Available unobtrusive sensors allow clinical personnel to monitor patients' movement and physiological data such as heart rate, respiratory rate, and oxygen saturation. Cell phone technology and the widespread access to the Internet provide means to implement systems designed to remotely monitor patients' status and optimize interventions based on individual responses to different rehabilitation approaches. This chapter summarizes recent advances in the field of wearable technology and presents examples of application of this technology in rehabilitation.
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paper_title: Multisensor integration and fusion in intelligent systems

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The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered. >
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paper_title: Movement parameters that distinguish between voluntary movements and levodopa-induced dyskinesia in Parkinson s disease

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Abstract It is well known that long-term use of levodopa by patients with Parkinson’s disease causes dyskinesia. Several methods have been proposed for the automatic, unsupervised detection and classification of levodopa induced dyskinesia. Recently, we have demonstrated that neural networks are highly successful to detect dyskinesia and to distinguish dyskinesia from voluntary movements. The aim of this study was to use the trained neural networks to extract parameters, which are important to distinguish between dyskinesia and voluntary movements. Thirteen patients were continuously monitored in a home-like situation performing in about 35 daily life tasks for a period of approximately 2.5 h. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions of the body. A neural network was trained to assess the severity of dyskinesia. The neural network was able to assess the severity of dyskinesia and could distinguish dyskinesia from voluntary movements in daily life. For the trunk and the leg, the important parameters appeared to be the percentage of time that the trunk or leg was moving and the standard deviation of the segment velocity of the less dyskinetic leg. For the arm, the combination of the percentage of time, that the wrist was moving, and the percentage of time, that a patient was sitting, explained the largest part of the variance of the output. Dyskinesia differs from voluntary movements in the fact that dyskinetic movements tend to have lower frequencies than voluntary movements and in the fact that movements of different body segments are not well coordinated in dyskinesia.
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paper_title: Electroencephalography: Basic Principles, Clinical Applications and Related Fields

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This book has 44 chapters written by 23 authors from Europe and the United States. It represents a commendable attempt at producing a one-volume, comprehensive textbook on EEG. Many chapters are good, but some are especially clear and didactic. "Biophysical Aspects of EEG and MEG Generation" deals with its subject in a clear and well-organized fashion understandable to all readers, leaving the mathematical foundations in an appendix for those whose interests are more basic. The chapter on the EEG laboratory goes into a discussion of laboratory design and organization, including comments on reporting EEGs and handling the reports. This is a useful feature that is not commonly seen in texts on EEG. The contingent negative variation is amply discussed in a review that includes techniques and physiologic and psychological correlates, updating previous reviews by the authors of the chapters. Some of the chapters are outstanding. The one on psychiatric disorders
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paper_title: Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Process, format, and clinimetric testing plan

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This article presents the revision process, major innovations, and clinimetric testing program for the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS), known as the MDS-UPDRS. The UPDRS is the most widely used scale for the clinical study of Parkinson's disease (PD). The MDS previously organized a critique of the UPDRS, which cited many strengths, but recommended revision of the scale to accommodate new advances and to resolve problematic areas. An MDS-UPDRS committee prepared the revision using the recommendations of the published critique of the scale. Subcommittees developed new material that was reviewed by the entire committee. A 1-day face-to-face committee meeting was organized to resolve areas of debate and to arrive at a working draft ready for clinimetric testing. The MDS-UPDRS retains the UPDRS structure of four parts with a total summed score, but the parts have been modified to provide a section that integrates nonmotor elements of PD: I, Nonmotor Experiences of Daily Living; II, Motor Experiences of Daily Living; III, Motor Examination; and IV, Motor Complications. All items have five response options with uniform anchors of 0 = normal, 1 = slight, 2 = mild, 3 = moderate, and 4 = severe. Several questions in Part I and all of Part II are written as a patient/caregiver questionnaire, so that the total rater time should remain approximately 30 minutes. Detailed instructions for testing and data acquisition accompany the MDS-UPDRS in order to increase uniform usage. Multiple language editions are planned. A three-part clinimetric program will provide testing of reliability, validity, and responsiveness to interventions. Although the MDS-UPDRS will not be published until it has successfully passed clinimetric testing, explanation of the process, key changes, and clinimetric programs allow clinicians and researchers to understand and participate in the revision process.
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paper_title: Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease

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A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.
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paper_title: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results

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We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79–0.93 across parts) and correlated with the original UPDRS (ρ = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD. © 2008 Movement Disorder Society
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paper_title: A Web-Based System for Home Monitoring of Patients With Parkinson's Disease Using Wearable Sensors

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This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.
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paper_title: Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors

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This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.
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paper_title: A new feature constituting approach to detection of vocal fold pathology

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In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.
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paper_title: Multisensor Fusion and Integration: A Review on Approaches and Its Applications in Mechatronics

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The objective of this paper is to review the theories and approaches of multisensor fusion and integration (MFI) with its application in mechatronics. MFI helps the system perceiving changes of the environment and monitoring the system itself. Since each individual sensor has its own inherent defects and limitations, MFI merges the redundant information acquired by multiple sensors synergistically to provide a more accurate perception and make an optimal decision in further. The wide application spectrum of MFI in mechatronics includes the industrial automation, the development of intelligent robots, military applications, biomedical applications, etc. In this paper, the architecture and algorithms of MFI are reviewed, and some implementation examples in industrial automation and robotic applications are presented. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, the most frequently used algorithms in previous researches with their advantages and limitations are summarized. Applications of MFI in robotics and mechatronics are discussed. Future perspectives of MFI deployment are included in the concluding remarks.
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paper_title: Advances in wearable technology and applications in physical medicine and rehabilitation

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The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation.
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paper_title: EMG signal morphology in essential tremor and Parkinson's disease

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The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.
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paper_title: A system for analysis of tremor in patients with Parkinson's disease based on motion capture technique

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Resting tremor is one of the primary motor symptoms of Parkinson's Disease. In this paper we analyze the occurrence of tremor in Parkinson's disease by using a system for measurements of kinematic data of upper limbs. The experimental group includes seven PD patients during standing. Analysis is based on kinematic measurements done by using multimodal motion capture (MOCAP) system for registration of 3D positions of body markers, ground reaction forces and electromyography signals. All patients taking part in examination have undergone deep brain stimulation surgical treatment. Examination involved comparisons of tremor parameters across four conditions, where stimulator was turned ON/OFF and medication was ON/OFF. Obtained results confirm statistically significant differences of certain tremor parameters between different experimental conditions.
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paper_title: Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

paper_content:

In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.
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paper_title: Multisensor Fusion and Integration: Algorithms, Applications, and Future Research Directions

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Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion algorithms and applications of multisensor fusion in localization and tracking, robotics, identification and classification, vehicle sensing, and so on. Finally, future research directions of multisensor fusion technologies including microsensors, smart sensors, and adaptive fusion techniques are presented.
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paper_title: Analysis of surface EMG signal morphology in Parkinson's disease

paper_content:

A novel approach is presented for the analysis of surface electromyogram (EMG) morphology in Parkinson's disease (PD). The method is based on histogram and crossing rate (CR) analysis of the EMG signal. In the method, histograms and CR values are used as high-dimensional feature vectors. The dimensionality of them is then reduced using the Karhunen-Loève transform (KLT). Finally, the discriminant analysis of feature vectors is performed in low-dimensional eigenspace. Histograms and CR values were chosen for analysis, because Parkinsonian EMG signals typically involve patterns of EMG bursts. Traditional methods of EMG amplitude and spectral analysis are not effective in analyzing impulse-like signals. The method, which was tested with EMG signals measured from 25 patients with PD and 22 healthy controls, was promising for discriminating between these two groups of subjects. The ratio of correct discrimination by augmented KLT was 86% for the control group and 72% for the patient group. On the basis of these results, further studies are suggested in order to evaluate the usability of this method in early stage diagnostics of PD.
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paper_title: Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

paper_content:

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.
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paper_title: Detection and assessment of the severity of Levodopa-induced dyskinesia in patients with Parkinson's disease by neural networks

paper_content:

Levodopa-induced dyskinesias (LID) in Parkinson's disease (PD) have remained a clinical challenge. We evaluated the feasibility of neural networks to detect LID and to quantify their severity in 16 patients with PD at rest and during various activities of daily living. The movements of the patients were measured using four pairs of accelerometers mounted on the wrist, upper arm, trunk, and leg on the most affected side. Using parameters obtained from the accelerometer signals, neural networks were trained to detect and to classify LID corresponding to the modified Abnormal Involuntary Movement Scale. Important parameters for classification appeared to be the mean segment velocity and the cross-correlation between accelerometers on the arm, trunk, and leg. Neural networks were able to distinguish voluntary movements from LID and to assess the severity of LID in various activities. Based on the results in this study, we conclude that neural networks are a valid and reliable method to detect and to assess the severity of LID corresponding to the modified Abnormal Involuntary Movement Scale.
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paper_title: Multisensor Fusion and Integration: Theories, Applications, and its Perspectives

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The decision-making processes in an autonomous mechatronic system rely on data coming from multiple sensors. An optimal fusion of information from distributed multiple sensors requires robust fusion approaches. The science of multisensor fusion and integration (MFI) is formed to treat the information merging requirements. MFI aims to provide the system a more accurate perception enabling an optimal decision to be made. The wide application spectrum of MFI in mechatronic systems includes industrial automation, the development of intelligent robots, military applications, biomedical applications, and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS). This paper reviews the theories and approaches of MFI with its applications. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, are the most frequently used algorithms. Future perspectives of MFI deployment are included in the concluding remarks.
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Title: Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

Section 1: Introduction
Description 1: Introduce the background of Parkinson's Disease (PD), current treatment methods, and the need for accurate symptom severity assessment.

Section 2: Assessment of Parkinson Disease-State of Art
Description 2: Discuss the current tools and conventional methods used for monitoring motor fluctuations and PD symptoms, including their limitations.

Section 3: Monitoring on Progression of PD Using Electromyography (EMG)
Description 3: Explore various EMG-based techniques used to differentiate PD patients from healthy controls and other motor disorders.

Section 4: Monitoring PD Using Electroencephalogram (EEG)
Description 4: Summarize the use of EEG signals for detecting early symptoms of PD and its application in studying brain activity and dysfunctions related to PD.

Section 5: Monitoring PD Using Brain Imaging Modalities or 3D Motion Analysis
Description 5: Outline the application of non-invasive brain imaging technologies and 3D motion analysis for diagnosing and monitoring PD symptoms.

Section 6: PD Symptoms Assessment - Tremor and Bradykinesia
Description 6: Detail methods and algorithms developed to quantify tremor and bradykinesia in PD patients using wearable sensors.

Section 7: PWP Physical Activities Monitoring
Description 7: Present studies on the ambulatory monitoring of physical activities in PD patients using body-fixed inertial sensors.

Section 8: Levodopa-Induced Dyskinesia (LID) Detection in PD
Description 8: Describe techniques using wearable sensors and neural networks to detect and measure the severity of LID in PD patients.

Section 9: Detection of PD Motor Symptoms: Uncontrolled Home Environment
Description 9: Discuss methods and algorithms for detecting PD motor symptoms in everyday life using weakly supervised learning and sensor data.

Section 10: PD Hand Tremor Monitoring
Description 10: Review the use of wireless accelerometers for the quantification and monitoring of hand tremor in PD patients.

Section 11: Detection of Freezing of Gait (FoG) in PD
Description 11: Discuss methodologies for detecting FoG in PD patients using gait analysis, accelerometers, and gyroscopes.

Section 12: Monitoring PD Using Audio Sensors
Description 12: Explore the application of speech signal processing algorithms for assessing vocal impairment related to PD and monitoring disease progression.

Section 13: Discussion and Conclusions
Description 13: Summarize the necessity for effective PD symptom monitoring, limitations of current methods, and potential improvements offered by technological advancements in wearable and audio sensors.