Daily stress detection by using electrocardiogram in the first hour of sleep at night

Stress refers to a series of physiological reactions to help the human body overcome threats. In this study, the machine learning method was used to analyze the stress in students' daily life environment. The purpose is to explore the neurophysiological model of sleep under stress, so that they can effectively adjust their personal stress and improve their sleep quality. First, we collected ECG and triple-axis acceleration data of 33 college students in their daily life, calculated the inter-beat intervals from every two consecutive R waves in these data. Secondly, we extracted 39 linear and non-linear RR parameters as stress physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Finally, a binary classification model of stressed and non-stressed states recognition in the first hour of sleep at night was constructed, and the generalization accuracy of the model was 75.00% on the validation data set independent of model training and feature selection. The results show that it is feasible to monitor students' stress state at night by machine learning.