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

Abstract

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 (Poincaré 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.