ChronoPscychosis: Temporal Segmentation and Its Impact on Schizophrenia
  Classification Using Motor Activity Data
Pradnya Rajendra Jadhav Raviprasad Aduri
Schizophrenia is a complicated mental illness characterized by a broad
spectrum of symptoms affecting cognition, behavior, and emotion. The task of
identifying reliable biomarkers to classify Schizophrenia accurately continues
to be a challenge in the field of psychiatry. We investigate the temporal
patterns within the motor activity data as a potential key to enhancing the
categorization of individuals with Schizophrenia, using the dataset having
motor activity recordings of 22 Schizophrenia patients and 32 control subjects.
The dataset contains per-minute motor activity measurements collected for an
average of 12.7 days in a row for each participant. We dissect each day into
segments (Twelve, Eight, six, four, three, and two parts) and evaluate their
impact on classification. We employ sixteen statistical features within these
temporal segments and train them on Seven machine learning models to get deeper
insights. LightGBM model outperforms the other six models. Our results indicate
that the temporal segmentation significantly improves the classification, with
AUC-ROC = 0.93, F1 score = 0.84( LightGBM- without any segmentation) and
AUC-ROC = 0.98, F1 score = 0.93( LightGBM- with segmentation). Distinguishing
between diurnal and nocturnal segments amplifies the differences between
Schizophrenia patients and controls. However, further subdivisions into smaller
time segments do not affect the AUC- ROC significantly. Morning, afternoon,
evening, and night partitioning gives similar classification performance to
day-night partitioning. These findings are valuable as they indicate that
extensive temporal classification beyond distinguishing between day and night
does not yield substantial results, offering an efficient approach for further
classification, early diagnosis, and monitoring of Schizophrenia.