Classification of heart sounds using empirical mode decomposition based features

Abstract

A novel method is presented to extract robust features for automatic classification of heart sounds based on Empirical Mode Decomposition (EMD). The work decomposes segmented heart sound cycles with EMD to generate certain intrinsic mode functions (IMFs). It is seen that the first IMF contains mostly high frequency noise, the second and third IMFs carry higher frequency components of our signal of interest and residue contains its low frequency components. A twenty five dimensional feature vector is generated from average energy of the segmented IMFs and residue which serve as input to classifier models. Two different classifiers, Artificial Neural Network (ANN) and Grow and Learn (GAL) network, are used to show the performance of the proposed feature extraction technique. Experiments are conducted on 104 different recordings of heart sound comprising of normal and 12 different pathological cases against three different additive background noises – white Gaussian, hospital and body noise. It is found that the EMD based feature extraction always performs better than benchmark wavelet based feature extraction technique.