Decision tree for smart feature extraction from sleep HR in bipolar patients

Abstract

The aim of this work is the creation of a completely automatic method for the extraction of informative parameters from peripheral signals recorded through a sensorized T-shirt. The acquired data belong to patients affected from bipolar disorder, and consist of RR series, body movements and activity type. The extracted features, i.e. linear and non-linear HRV parameters in the time domain, HRV parameters in the frequency domain, and parameters indicative of the sleep quality, profile and fragmentation, are of interest for the automatic classification of the clinical mood state. The analysis of this dataset, which is to be performed online and automatically, must address the problems related to the clinical protocol, which also includes a segment of recording in which the patient is awake, and to the nature of the device, which can be sensitive to movements and misplacement. Thus, the decision tree implemented in this study performs the detection and isolation of the sleep period, the elimination of corrupted recording segments and the checking of the minimum requirements of the signals for every parameter to be calculated.