A novel method to monitor human stress states using ultra-short-term ECG spectral feature

Abstract

Electrocardiogram (ECG) signal represents autonomous nervous system responses to human emotional states. This research demonstrates that the spectral ECG features within ultra-short-term window duration (10-sec) could be utilized to monitor human emotional states. Experiments were conducted with five different stress protocols including mental and physical tasks. Experimental results showed feasible classification performance of ECG spectral features compared to that of HRV parameters. The averaged classification accuracy across 13 subjects and all stress protocols was 81.16% using Naïve Bayes algorithm. In addition, the results showed stress responses in mental arithmetic tasks was the most separable from those in resting states (87.31%) compared to the other stress situations.

DOI: 10.1109/EMBC.2017.8037335

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Cite this paper

@article{Hwang2017ANM, title={A novel method to monitor human stress states using ultra-short-term ECG spectral feature}, author={Bosun Hwang and Jiwoo Ryu and Cheolsoo Park and Byoung-Tak Zhang}, journal={Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference}, year={2017}, volume={2017}, pages={2381-2384} }