Linear and nonlinear analyses of heart rate variability signals under mental load

Mental load has an important effect on the efficiency and reliability of human-machine systems. This study discussed in this paper looked at the heart rate variability (HRV) signal changes of subjects under a mental load state, which was used to explore physiological indices of a mental load. An ErgoLAB smart wearable human factor physiological recorder was used to collect the photoplethysmography (PPG) signals of 30 people in a resting state and while implementing the detonation of energetic materials, and HRV signals were extracted from the PPG. First, we used a subjective questionnaire and time perception test to judge the induction of mental load. Then, linear (time-domain and frequency-domain) and nonlinear (Poincare ' plot, scatter plot and sample entropy (SampEn)) analysis was performed on the subjects' HRV signals, and Pearson's correlation analysis and t-tests were conducted. In a state of mental load, the score of the subjective questionnaire increased significantly (p < 0.01), and the time perception error value (p < 0.01) and the relative error rate (p < 0.05) increased significantly, which proved that the subjects were under mental load. The results show that HR, RRn, SDNN, RMSSD, pNN50, CV, HF, SD1, SD2 and B- are useful sensitivity parameters to reliably detect whether there is mental load personnel. The research in this paper provides a theoretical basis for the effective identification of mental load. It can also serve as a reference for the study of people's job reliability under the influence of mental load.

Automated summary

Mental load has a significant impact on the efficiency and reliability of human-machine systems. This study investigated the changes in heart rate variability (HRV) signals under a mental load state and identified several HRV parameters that can reliably detect the presence of mental load. The findings provide a theoretical basis for effectively identifying mental load and contribute to the study of job reliability under the influence of mental load.