A pattern recognition approach based on electrodermal response for pathological mood identification in bipolar disorders

This paper reports on results of a pattern recognition technique for classifying pathological mental states of bipolar disorders using information gathered from the electrodermal response. The rationale behind this work is that the autonomic nervous system dynamics, non-invasively quantified through the electrodermal response processing, is altered by the specific mood state. Starting from the hypothesis that bipolar disorders are associated with affective dysfunctions, we processed data gathered from four bipolar patients through eleven experimental trials while an ad-hoc emotional stimulation is administered. Intra- and inter-subject variability were investigated. We show that, using a deconvolution-based approach to estimate sympathetic ANS markers and simple k-Nearest Neighbor algorithms, the proposed methodology is able to discern up to three mood states such as depression, hypo-mania, and euthymia with an average intra-subject accuracy greater than 98% and inter-subject accuracy greater than 82%.