ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases

The spontaneous classification of cardiovascular diseases is a challenging task and can be made more feasible with proper ECG fluctuation analysis. In this contribution we perform a qualitative analysis of the ECG data using complex Gaussian wavelets to investigate the multi-scale, self similar behaviour and deviation via phase plots of the wavelet cross spectrum of the ECG signals. We further analyze ECG signals using S transform to overcome the limitations of continuous wavelet transform and make the results more consistent and reliable. The results obtained are promising and the inferences drawn to aid in disease classification using the ECG signals are also discussed.