Clinical state assessment in bipolar patients by means of HRV features obtained with a sensorized T-shirt.

The aim of this study is to identify parameters extracted from the Heart Rate Variability (HRV) signal that correlate to the clinical state in patients affected by bipolar disorder. 25 ECG and activity recordings from 12 patients were obtained by means of a sensorized T-shirt and the clinical state of the subjects was assessed by a psychiatrist. Features in the time and frequency domain were extracted from each signal. HRV features were also used to automatically compute the sleep profile of each subject by means of an Artificial Neural Network, trained on a control group of healthy subjects. From the hypnograms, sleep-specific parameters were computed. All the parameters were compared with those computed on the control group, in order to highlight significant differences in their values during different stages of the pathology. The analysis was performed by grouping the subjects first on the basis of the depression-mania level and then on the basis of the anxiety level.