Hierarchical SVM to detect mental stress in human beings using Heart Rate Variability

Abstract

Stress has become an embedded part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. The Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used in this work. The features extracted from HRV are given as input, to the Hierarchical classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. The performance of the hierarchical structure is better and the efficiency of classification is 92 %.