Neural network classification of homomorphic segmented heart sounds

Authors:
Cota Navin Gupta;Ramaswamy Palaniappan;Sundaram Swaminathan;Shankar M. Krishnan
Affiliations:
Biomedical Engineering Research Center, Nanyang Technological University, Singapore;Department of Computer Science, University of Essex, Colchester, UK;Biomedical Engineering Research Center, Nanyang Technological University, Singapore;Biomedical Engineering Research Center, Nanyang Technological University, Singapore
Venue:
Applied Soft Computing
Year:
2007

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Abstract

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S"1-Systole-S"2-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs (Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.