Psychological Stress Level Detection Based on Heartbeat Mode

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.

Keywords

• autonomic nervous system
• heartbeat mode
• heart rate variability
• k-nearest neighbors
• stress recognition