Self-organizing Maps Using Acoustic Features for Prediction of State Change in Bipolar Disorder

Bipolar disorder (BD) is a serious mental disorder characterized by manic episodes of elevated mood and overactivity, interspersed with periods of depression. Typically, the psychiatric assessment of affective state is carried out by a psychiatrist during routine check-up visits. However, diagnostics of a phase change can be facilitated by monitoring data collected by the patient's smartphone. Previous studies concentrated primarily on the phase detection formulated as a classification task. In this study, we introduce a new approach to predict the phase change of BD patients using acoustic features and a combination of the Kohonen's self-organizing maps and random forests. The primary goal is to predict the forthcoming change of patient's state. We report on preliminary results that confirm the existence of a relation between the outcome of unsupervised learning (clustering) and the psychiatric assessment. Next, we evaluate the out-of-sample accuracy to predict the patient's state with random forests. Finally, we discuss the potential of unsupervised learning for monitoring BD patients.