Performance analysis of DWT at different levels for feature extraction of PCG signals

The present work proposes a Bio Medical-based system for normal and abnormal heart sound identification based on Discrete Wavelet Transform (DWT), it is useful for Detection of heart diseases. Due to the presence of sampling Frequency components, the wavelets have a different Decomposition level and it´s very much important to find, from which level we get the best performance for a particular Heart Sound. DWT is applied up to 10 levels to extract the features for the individual Heart Signal. In this present approach, one dimensional feature extraction is obtained by evaluating parameters like mean energy, variance, standard deviation and maximum entropy etc. Thus we get the proper level of wavelet and using that, we can extract the best feature for Heart Sounds.