Quantifying neurophysiological mechanism through heart rate variability: the case of cognitive stress

Abstract In the modern contemporary world, our life has become a warehouse of stress, consisting more of mental stress than physical stress. It can be shown that prolonged mental stress can lead to serious dysfunctionality in the autonomous nervous system (ANS), which, in turn, is reflected in heart rate variability (HRV). In this study, we have discussed the effectiveness of different metrics obtained from the analysis of HRV signals. The HRV analyses are being conducted in majorly three domains by the research community, namely, time, frequency, and non-linear. We have compiled metrics for all the three techniques and compared their effectiveness. To assess the effect of cognitive stress, a comparative study is typically developed where two sets of HRV data are procured from the same subject, one in a completely healthy and stressless condition and the other under a simulated, controlled cognitive stress. Many of the datasets are pretty comprehensive, having a vast number of subjects and the analysis comprising up to 22 different parameters. A comparative study of these datasets revealed a significant drop in HRV during stress when analyzed in time and non-linear domains. On the other hand, a substantial increase in HRV is spotted when measured with the help of high-to-low-frequency methodology. Overall, the non-linear metrics were found to be more robust and gave more consistent results than the time or frequency domain metrics.