Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls.

ABSTRACT: Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features an

SUBMITTER: Jakobsen P 

PROVIDER: S-EPMC7446864 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature