IEEE J Biomed Health Inform - Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis.

Tópicos

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Resumo

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%.

Resumo Limpo

current clinic practic diagnos patient affect psychiatr disord bipolar disord base verbal interview score specif questionnair reliabl object psychophysiolog marker taken account paper propos use wearabl system base comfort tshirt integr fabric electrod sensor abl acquir electrocardiogram respirogram bodi postur inform order detect pattern object physiolog paramet support diagnosi moreov implement novel ad hoc methodolog advanc biosign process abl effect recogn four possibl clinic mood state bipolar patient ie depress mix state hypomania euthymia continu monitor h use heart rate variabl inform exclus mood assess intend intrasubject evalu patient state model markov chain ie time domain mood state refer previous one valid eight bipolar patient monitor collect analyz h autonom cardiovascular activ experiment result demonstr novel concept person pervas monitor constitut viabl robust clinic decis support system bipolar disord recogn mood state total classif accuraci

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