Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal

Abstract

Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data.

Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods

“…Additionally, the COVID-19 epidemic has made things worse. Depressive symptoms rose from 8.5% to 27.8% throughout the quarantine period, according to [1,2]. Depression might become the most common disease as a result of this phenomenon, which would have a significant impact on healthcare costs and public health.…”

“…This drives cyclical biological rhythms in connection with repetitive daily social rhythms [9][10][11]. Key symptoms of mood episodes [10] have been proposed as biological rhythmic rhythms that are out of sync [1]. Complex dynamical systems are time series of repeating biological rhythms and daily activities [11].…”

Feature Selection of Motor Activity in Intervals of Time with Genetics Algorithms for Depression Detection

“…Experimental results demonstrated the model efficacy in identifying episodes of depression and schizophrenia, as well as healthy controls, surpassing prior studies that employed computationally expensive algorithms such as CNN and Bidirectional Recurrent Neural Networks (BRNN), resulting in a noteworthy boost in accuracy. [17] . Jakobsen et al, [18] conducted a study to investigate the potential of various machine learning algorithms in distinguishing between depressed patients and healthy controls using motor activity time series.…”