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

Manman Wang; Feifei Zhang; Jian Qin; Yang Zhao; Xiangyu Sun; Wanhui Wen

doi:10.1117/12.2681156

8 June 2023 Daily stress detection by using electrocardiogram in the first hour of sleep at night

Manman Wang, Feifei Zhang, Jian Qin, Yang Zhao, Xiangyu Sun, Wanhui Wen

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071T (2023) https://doi.org/10.1117/12.2681156
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

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.

Citation Download Citation

Manman Wang, Feifei Zhang, Jian Qin, Yang Zhao, Xiangyu Sun, and Wanhui Wen "Daily stress detection by using electrocardiogram in the first hour of sleep at night", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071T (8 June 2023); https://doi.org/10.1117/12.2681156

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KEYWORDS

Data modeling

Education and training

Electrocardiography

Feature selection

Machine learning

Statistical modeling

Statistical analysis

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