Mental Stress Assessment of ECG Signal Using Statistical Analysis of Bio-orthogonal Wavelet Coefficients: Part-2

In recent scenario, mental stress analysis is important task. There are different methods used in the literature survey to extract the mental stress. Wavelet transform as transformation, Power, Energy, entropy, Co-variance, Standard deviation and Mean, as features and K nearest neighbor (KNN) or Back Propagation are commonly used to build mental stress system. In this paper, a modified approach to mental stress level detection in a person has been proposed. In this method two lead ECG data extraction and bior 3.9 (bioorthogonal) wavelet transform has been used for decomposition of ECG signal data up to level three. Features Such as Power, Energy, Entropy, Co-variance, Standard deviation and Mean are used for stress detection and analysis. Finally the Back Propagation classification algorithm is used for classification of mental stress level and normal level. The result from this approach is more favorable and acceptable.