Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

There is growing amount of scientific evidence that motor activity is the most consistent indicator of bipolar disorder. Motor activity includes several areas such as body movement, motor response time, level of psychomotor activity, and speech related motor activity. Studies of motor activity in bipolar disorder have typically used self-reported questionnaires with clinical observer-rated scales, which are therefore subjective and have often limited effectiveness. Motor activity information can be used to classify episode type in bipolar patients, which is highly relevant, since severe depression and manic states can result in mortality. This paper introduces a system able to classify the state of patients suffering from bipolar disorder using sensed information from smartphones. We collected audio, accelerometer and self-assessment data from five patients over a time-period of 12 weeks during their real-life activities. In this research we evaluated the performance of several classifiers, different sets of features and the role of the questionnaires for classifying bipolar disorder episodes. In particular, we have shown that it is possible to classify with high confidence (≈85%) the course of mood episodes or relapse in bipolar patients. To our knowledge, no research to date has focused on naturalistic observation of day-to-day phone conversation to classify impaired life functioning in individuals with bipolar disorder.