Mental stress recognition based on electrocardiogram

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

doi:10.1117/12.2687402

28 August 2023 Mental stress recognition based on electrocardiogram

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

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241Z (2023) https://doi.org/10.1117/12.2687402
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

In this study, we constructed a mental stress recognition model with Electrocardiogram (ECG) signals by machine learning. Firstly, we collected the ECG signals while students are explaining simple math exercises, located the R-wave peak of the QRS wave group, and calculated the R-wave to R-wave (RR) interval. Then we extracted the characteristic parameters of autonomic nervous response from the RR interval, carried out statistical analysis and sequential forward selection and finally constructed a psychological stress recognition model. The accuracy of the model in the test set and the independent subject validation set was 79% and 83%, respectively. The results show that it is feasible to recognize strong or weak psychological stress state through machine learning method.

Citation Download Citation

Jian Qin, Yang Zhao, Feifei Zhang, Manman Wang, Xiangyu Sun, and Wanhui Wen "Mental stress recognition based on electrocardiogram", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241Z (28 August 2023); https://doi.org/10.1117/12.2687402

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