Mental stress scoring based on Bayesian network using improved heart rate variability

People cope with mental stress from time to time, which can lead to physical and mental health issues. Respiratory sinus arrhythmia has been shown to be an expressive tool for measuring the heart-brain communication. In this paper, we present a method to score the mental stress of human being by respiratory related Heart Rate Variability and non-respiratory related Heart Rate Variability based on Bayesian Networks. Through the learning of Bayesian Networks, this study find out the indicators related to mental stress. These indicators are related to the chronic stress and acute stress of the human being respectively. Compared with the results of the stress scale completed by the subjects, the method proposed in this paper can calculate the current mental stress state of the human body accurately.