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MENTAL STRESS LEVEL MEASUREMENT USING HRV ANALYSIS AND KNN CLASSIFIER WITH EFFICIENT VOTING SCHEME

Stress is rooted in our life, being stressed by our family circumstances, financial concerns, our job, etc. Stress leads to physical disorders, such as heart attacks and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. The most reliable and efficient time and frequency domain parameters obtained from Heart Rate Variability (HRV) determined from ECG signal is used to detect the stress level in this work. The features are given as input to the KNearest Neighbour (KNN) classifier which classifies the data into one of the four stress levels as no stress, low stress, medium stress and high stress. The KNN classifier combines the strength of adjacent neighbours to make decisions which improves the efficiency of classification. The Physionet database of drivers stress is used to check the validity of the proposed KNN classifier. Performance of the proposed combination scheme in KNN is better compared to the other combination scheme.