A Review on ECG Signal Analysis for Mental Stress Assessment

In current scenario, the mental stress is becoming an unavoidable part of our daily life because of various social and professional responsibilities. In literature, various biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) together with other biomedical parameters such as body temperature, the pulse rate and heartrate variability (HRV) have been used for the detection and classification of mental stress levels in a patient. In the present work, a comprehensive review of various methods used in literature for the assessment and classification of mental stress is presented. The work done in this area mainly during the last decade is studied considering the important aspects of ECG signal analysis such as data acquisition, feature extraction, feature selection and classification. The basic steps required for the detection of mental stress using different features of ECG signals are discussed. Popularly used classification algorithms for mental stress are considered in the work and their results are compared.