Abstract
The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there is no consensus on the optimal HRV metrics in psychological assessments. This study proposes an HRV analysis method that is based on heartbeat modes to detect drivers’ stress. We used statistical tools for linguistics to detect and quantify the structure of the heart rate time series and summarized different heartbeat modes in the time series. Based on the k-nearest neighbors (k-NN) classification algorithm, the probability of each heartbeat mode was used as a feature to detect and recognize stress caused by the driving environment. The results indicated that the stress from the driving environment changed the heartbeat mode. Stress-related heartbeat modes were determined, facilitating detection of the stress state with an accuracy of 93.7%. We also concluded that the heartbeat mode was correlated to the galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. The proposed method revealed HRV characteristics that made quantifying and detecting different mental conditions possible. Thus, it would be feasible to achieve personalized analyses to further study the interaction between physiology and psychology.
Highlights
Human health is closely related to mental stress, which is subjective and continuously variable
We identified 10 single driving datasets and classified them based on two stress levels
McCraty et al [16] mentioned that the synchronization between emotional and physiological dynamics is directly related to rhythmic patterns in the heart rate, not the heart rate itself at any point in time
Summary
Human health is closely related to mental stress, which is subjective and continuously variable. Determining the onset, duration, and severity of stressful events is a difficult and worthy research topic [1,2]. It is a challenging task for researchers and clinicians to obtain biomarkers of stress and health [3]. Stress may lead to dynamic changes in the autonomic nervous system (ANS), which are characterized by an increase in sympathetic nervous system (SNS) activity and a decrease in parasympathetic nervous system (PNS) activity [4]. Heart-rate variability (HRV) features are considered to be viable physiological markers for evaluating ANS activity when detecting stress [5,6]. HRV represents the changes in the time intervals between consecutive heartbeats, which can be extracted from electrocardiograph (ECG) signals [7]
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