Deep learning approach of murmur detection using Cochleagram

Abstract

Primary detection of heart abnormalities can reduce sudden death due to heart related diseases to a great extent. A low cost noninvasive phonocardiogram (PCG) signal based heart murmur detection system can serve the purpose partially. In this work, initially the heart sound signal is segmented using an unsupervised technique. After that, deep neural network (DNN) architectures with three hidden layers have been proposed to classify the input heart sound signal as normal or murmur class using 64 dimensional acoustic features. Total 3482 heart sound cycles from a standard database have been used to train and test the proposed DNN model. The usual evaluation matrices of sensitivity, specificity, precision, F1-score and accuracy have been derived to evaluate the performance of the classifier. After studying five different DNN models we have achieved the highest F1-score of 98.31% and accuracy of 98.33% for the murmur class. This murmur detection system can be extremely useful in rural health cares and small hospitals to assist the general physicians in assessing heart diseases without having expertise in cardiology.