Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis

TLDR: Experimental results demonstrate that the novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.

Abstract: Current clinical practice in diagnosing patients affected by psychiatric disorders such as bipolar disorder is based only on verbal interviews and scores from specific questionnaires, and no reliable and objective psycho-physiological markers are taken into account. In this paper, we propose to use a wearable system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram, and body posture information in order to detect a pattern of objective physiological parameters to support diagnosis. Moreover, we implemented a novel ad hoc methodology of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients (i.e., depression, mixed state, hypomania, and euthymia) continuously monitored up to 18 h, using heart rate variability information exclusively. Mood assessment is intended as an intrasubject evaluation in which the patient's states are modeled as a Markov chain, i.e., in the time domain, each mood state refers to the previous one. As validation, eight bipolar patients were monitored collecting and analyzing more than 400 h of autonomic and cardiovascular activity. Experimental results demonstrate that our novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.