Chronic Stress Recognition Based on Time-Slot Analysis of Ambulatory Electrocardiogram and Tri-Axial Acceleration
Jiayu Li, Manman Wang, Feifei Zhang, Guangyuan Liu, Wanhui Wen
Abstract
Stress, especially chronic stress, is a high risk factor of many physical and mental health problems. This work acquired 702 days of full-day ambulatory electrocardiogram (ECG) and Tri-axial acceleration (T-ACC) data from 104 healthy college students and realized chronic stress recognition through signal processing, statistical test and machine learning. We divided the 24 hours of a day into 153 time slots, and calculated 30 features from ECG and T-ACC data in each time slot. Statistical test of the above 30 features of the subjects with chronic stress and no chronic stress labels showed that chronic stress altered the autonomic nervous control of the heart not only in the daily activity time but also in the rest time at night, leading to smaller heart rate variability, faster heart rate and less complexity of the heartbeat rhythm. More specifically, the parasympathetic nervous activity at night was weakened by chronic stress. We expressed the ECG and T-ACC data of a day as a 30×153 data matrix, applied a four-layer fully connected neural network to classify the data of 702 days with chronic stress and no chronic stress labels, and obtained 88.17% chronic stress detection accuracy in the leave-one-subject-out cross test.