Mental Stress estimation is an important feature to be derived in health related diagnostic activity. It has been observed that the stress has a major effect on heart functioning. And therefore, ecg should be the major source of stress variation and can be analyzed in various ways in order to extract the effect of mental stress. In the presented work, the ecg is analyzed using the statistical parameters set (energy, entropy, power, standard deviation and covariance). The parameters are not directly computed form the ecg itself. The ecg is first decomposed to level-2 using BIOR-3.9 wavelet transform to reduce the dimensionality of the ecg sample size. The level-1 and level-2 parameters are used to derive the mental stress levels as normal (N), hyper-1 (H-1), hyper-2 (H-2), depression-1 (D-1) and depression-2 (D-2). On parameter analysis, it has been observed that the energy and entropy are the two parameters that show an effective variation in values when normal to depression or normal to hyper case is observed. Therefore, the energy and entropy values are used for rule making and learning of the system in order to derive the mental stress levels

Mental stress quantification using fuzzy analysis of ecg parameters is presented here. ECG signal is decomposed using the BIOR-3.9 wavelet family upto three levels. The approximates signals are used for computation ecg parameters like energy, entropy, power, standard deviation, mean and covariance. A fuzzy classifier is designed using trimf function as associate membership in fuzzy analysis. The ecg data base is taken from MIT data base web site.