A personalized wearable monitoring system using history-dependent long-term heart rate variability analysis recognized mood states in bipolar patients with a total classification accuracy of up to 95.81%, significantly outperforming standard independent processing.
Observational (n=8)
Yes
Does a wearable monitoring system using history-dependent HRV analysis accurately recognize mood states in bipolar patients?
A wearable monitoring system utilizing history-dependent heart rate variability analysis can accurately classify mood states in bipolar disorder, providing a potential objective physiological marker for psychiatric management.
p-value: p=<0.01
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%.
Valenza et al. (Tue,) conducted a observational in Bipolar disorder (n=8). Wearable monitoring system with history-dependent long-term HRV analysis (Markov model) vs. Standard approach (independently processed feature set) was evaluated on Total classification accuracy of mood states (p=<0.01). A personalized wearable monitoring system using history-dependent long-term heart rate variability analysis recognized mood states in bipolar patients with a total classification accuracy of up to 95.81%, significantly outperforming standard independent processing.