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

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.