Biosignal-based Stress Assessment Considering Sympathetic Nerve Activation

Abstract

In this study, a stress assessment algorithm was developed based on the electrocardiogram(ECG) and the respiration(RSP). Twenty-eight healthy adults (12 males, 16 females, mean age 33.9 ± 10.7 years) were participated in the stress experiment. The stress experiment was consisted of a reference session (maintaining a static posture for 5 minutes), a stress session (speaking in foreign language, such as English, and mental arithmetic task for 5 minutes), and a relaxation session (resting for 10 minutes with video). The biosignals were recorded using the ECG, RSP and electrodermal activity (EDA) sensors in the Biopac MP160 system with sampling rate of 2 kHz, and Likert scale was used to measure the current stress levels after each session. The signal filtering and the data analysis were conducted using Python 3.10 software and the NeuroKit 2.0 toolkit. The standard deviation of NN intervals were calculated using the RR intervals of ECG and the peak-to-peak intervals (PPI) of RSP and applied to dynamic time warping (DTW) for the stress assessment. The Pearson correlation coefficients between Likert scale, EDA parameter (Skin Conductance Level [SCL], Skin Conductance Response [SCR], EDA Sympathetic component [EDASymp]), and DTW were calculated to evaluate the performance of stress assessment. SCL, SCR, and EDASymp based on EDA showed correlation coefficients of 0.558, 0.541, and 0.656 with the Likert scale, respectively, while the DTW algorithm showed a correlation coefficient of 0.650 with the Likert scale. Furthermore, the DTW algorithm showed high correlation coefficients of 0.748, 0.639, and 0.776 with SCL, SCR, and EDASymp, respectively. These results confirm that the DTW algorithm using ECG and RSP is effective in the stress assessment, comparable to EDA parameters. In the future, we aim to develop the stress quantification algorithm based on the EDA parameters and biosignal-based DTW.

Acknowledgements This research was supported in part by the Smart HealthCare Program(www.kipot.or.kr) funded by the Korean National Police Agency(KNPA, Korea) (220222M03), and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. RS-2023-00275296).