MULTI CLASS STRESS DETECTION THROUGH HEART RATE VARIABILITY A DEEP NEURAL NETWORK BASED STUDY

Abstract

Stress is a natural human reaction to demands or pressure, usually when perceived as
harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it
increases the risk of mental health and physiological uneasiness. Furthermore, chronic
stress raises the likelihood of mental health plagues such as anxiety, depression, and
sleep disorder. Although measuring stress using physiological parameters such as
heart rate variability (HRV) is a common approach, how to achieve ultra-high
accuracy based on HRV measurements remains as a challenging task. HRV is not
equivalent to heart rate. While heart rate is the average value of heartbeats per minute,
HRV represents the variation of the time interval between successive heartbeats. The
HRV measurements are related to the variance of RR intervals which stand for the
time between successive R peaks. In this study, we investigate the role of HRV
features as stress detection bio-markers and develop a machine learning-based model
for multi-class stress detection. More specifically,
a convolution neural network (CNN) based model is developed to detect multi-class
stress, namely, no stress, interruption stress, and time pressure stress, based on both
time- and frequency-domain features of HRV. Validated through a publicly available
dataset, SWELL−KW, the achieved accuracy score of our model has reached 99.9%
(Precision=1, Recall=1, F1−score=1, and MCC=0.99), thus outperforming the
existing methods in the literature. In addition, this study demonstrates the
effectiveness of essential HRV features for stress detection using a feature extraction
technique, i.e., analysis of variance