Entropy-based machine learning model for diagnosis and monitoring of
  Parkinson's Disease in smart IoT environment
Maksim Belyaev Murugappan Murugappan Andrei Velichko Dmitry Korzun
The study presents the concept of a computationally efficient machine
learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in
an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG).
We computed different types of entropy from EEG signals and found that Fuzzy
Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We
also investigated different combinations of signal frequency ranges and EEG
channels to accurately diagnose PD. Finally, with a fewer number of features
(11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%.
The most prominent frequency range of EEG signals has been identified, and we
have found that high classification accuracy depends on low-frequency signal
components (0-4 Hz). Moreover, the most informative signals were mainly
received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore,
we assessed the accuracy of the diagnosis of PD using three different lengths
of EEG data (150-1000 samples). Because the computational complexity is reduced
by reducing the input data. As a result, we have achieved a maximum mean
accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with
a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By
reducing the number of features and segment lengths, the computational cost of
classification can be reduced. Lower-performance smart ML sensors can be used
in IoT environments for enhances human resilience to PD.