An efficient heart murmur recognition and cardiovascular disorders classification system

The problem addressed in this work is the detection of a heart murmur and the classification of the associated cardiovascular disorder based on the heart sound signal. For this purpose, a dataset of Phonocardiogram (PCG) signals is acquired using baseline conditions. The dataset is acquired from 283 volunteers using Littman 3200 electronic stethoscope for a normal and four different types of heart murmurs. The samples are labelled and validated through echocardiography test of each participating volunteer. For feature extraction, normalized average Shannon energy with time-domain characteristics of heart sound signal is exploited to segment the PCG signal into its components. To improve the quality of the features, in contrast to the previous methods, all systole and diastole intervals are utilized to extract 50 Mel-Frequency Cepstrum Coefficients (MFCC) based features. Then, the iterative backward elimination method is used to identify and remove the redundant features to reduce the complexity in order to conceive a computationally tractable system. An MFCC feature vector of dimension 26 is selected for training seven different types of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) based classifiers for detection and classification of cardiovascular disorders. Fivefold cross-validation and 20% data holdout validation schemes are used for testing the classifiers. Classification accuracy of 92.6% is achieved using selected features and medium Gaussian SVM classifier. The learning curves show a good bias-variance trade-off indicating a well-fitted and generalized model for making future predictions.