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

Highlights

• The mental load induction experiment was designed as an inductive method of the energetic material detonation test experiment with stress and fear as the main incentives.

• Based on linear and nonlinear analysis methods (Poincaré plot, sample entropy and scatter plot), the HRV signal is analyzed under mental load.

• A combination of subjective measurement and objective measurement methods is used to explore the indices of HRV signal 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.

Introduction

Mental load refers to the demands placed on one’s limited psychological resources to achieve a desired level of performance on a task that puts strain on one’s attention, emotion, or reaction [1]. Due to the role of human emotions, perception, fatigue and other psychological states, there will be an impact on enterprise production safety. In 2010, Foxconn’s “13 consecutive jumps” reflected that workers under a mental load can cause large production safety hazards to a company. In contrast, the “psychological massage” smiley wall proposed by the China Chint Group has stimulated the productivity of enterprises. At the same time, working in some high-risk industries, such as underground coal mining, high-altitude operations, and hazardous chemical industries, will cause an increase in the mental load of operators and even an overload phenomenon. Excessive mental load will affect the work efficiency and physical and mental health of personnel and subsequently the efficiency and reliability of the entire human–machine system. Therefore, it is of great practical significance to carry out research on mental load.

The study of mental load began in the 1960s. Since the late 1970s, researchers have carried out many studies on the phenomenon of mental load, internal mechanisms, evaluation methods and countermeasures. During the research period, quantification of mental load was the main research goal of researchers, but the progress was relatively slow due to the complex factors that affect mental load and inadequate technical means. Many scholars have conducted research on mental load measurement methods. To date, the commonly used mental load measurement methods mainly include three categories: dual-task measurement, physiological measurement and subjective measurement [2]. Subjective measurement uses questionnaires to assess the subjective feelings of operators during the work process, which subjectively rate the mental load quantitatively and mainly includes the National Aeronautics and Space Administration-task load index (NASA-TLX) scale, subjective workload assessment technique (SWAT) scale, and modified Cooper-Harper (MCH) scale [3], [4], [5], [6]. However, subjective measurement methods are susceptible to individual subjective factors. Dual-task measurement methods refer to the addition of additional subtasks after the main task is completed, and the mental load can be evaluated by the performance on the subtask [7]. However, the main task measurement is not sensitive under a low load, and the secondary task is very disturbing. Compared with the above two methods, a physiological measurement method can objectively and sensitively measure the degree of mental load. Electrooculogram (EOG) [8], electrocardiogram (ECG) [9], electromyogram (EMG) [10], electroencephalography (EEG) [11], photoplethysmography (PPG) [12] and galvanic skin response (GSR) [13] are several physiological signals commonly used in the physiological assessment of mental load. To date, several scholars have carried out valuable research on mental load through physiological measures. Arsalan et al. [14] studied the physiological signals of people in a resting state and public speaking. The research showed that EEG, GSR and PPG signals are reliable indices of psychological stress during public speaking. Singh et al. [15] and Healey et al. [16] proposed that the GSR and heart rate (HR) can be used to reflect a driver’s mental load level. Many researchers have conducted sensitive index research on EEG under mental load based on linear and nonlinear methods. Gao et al. [17] indicated that time-domain features (Hjorth), frequency-domain features (power spectral density) and nonlinear features (differential entropy and sample entropy) of the EEG could serve as features for mental load recognition. Ma et al. [18] selected sample entropy, power spectral density and energy as EEG features that identified mental load. Jie et al. [19] used sample entropy as an EEG feature index and established a mental load identification method for multiple important EEG channels, such as F3, CP5, FP2, FZ, and FC2. However, the acquisition of EEG signals requires electrodes to be attached to the head. EEG signals are easily interfered by external factors, the degree of physiological accuracy varies between individuals, and the price is too high, so it has not been put into practical use. Cardiac activity is the most common physiological index used for mental load assessment [20], [21]. Heart rate variability (HRV) is an index of cardiac activity used in mental load studies. Richter et al. [22] examined whether cardiac activity indicated the driver's mental load while driving on rural highways. The research showed that HR and HRV can well reflect the mental load of people and evaluate their reliability. Mulder et al. [23] and Veltma et al. [24] studied mental load and found that an increase in mental load will lead to an increase in HR and a decrease in HRV. Therefore, HRV is a useful index of mental load. The HRV signal contains a large amount of information about cardiovascular regulation, such as HR and RR interval, which can be extracted and analyzed as an index to evaluate the function of the autonomic nervous system (ANS) [25]. Analytical methods for HRV indices include three main categories, namely, time-domain, frequency-domain and nonlinear indices [26]. Time-domain analysis and frequency-domain analysis are linear analysis methods for evaluating the HRV and can be used to quantitatively evaluate the regulatory action of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) [27]. İşler et al. [28], Xu et al. [29] and Cinaz et al. [30] selected time-domain analysis and frequency-domain analysis indices when carrying out mental load assessment through HRV signals. However, the heart is a complex nonlinear dynamic system, and normal cardiac activity has a chaotic dynamic law [31]. It is found that HRV signal is nonlinear [32]. Therefore, time-domain and frequency-domain analysis cannot reflect the nonlinear nature of the HRV. At the same time, the heartbeat is regulated by multiple factors and has mutations. Compared with a linear method, a nonlinear method may better reflect the comprehensive effect of the heart’s own autonomic nerve regulation [33].

This paper adopts a combination of a subjective measurement method (mental load questionnaire) and an objective measurement method (physiological signal measurement and time perception test) to carry out research on mental load. The HRV signal under mental load was analyzed linearly and nonlinearly, and the change rule of the HRV signal index under mental load was comprehensively studied. The experimental data were verified by Pearson’s correlation analysis and t-tests, and the sensitive indices of mental load and their changing laws were obtained. The results of this paper can provide theoretical support for the physiological measurement of mental load and the study of human behavior under mental load.