Abstract
Heart sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgement. Several studies have shown promising results in the automatic detection of cardiovascular disorders based on heart sound signals. However, the accuracy performance needs to be improved as automated heart sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the Physionet Challenge 2016 datasets, feature extraction using mel-frequency cepstrum coefficients (MFCC), and classification using an artificial neural network (ANN) with one hidden layer that provides low parameter consumption. Ten-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained 94% accuracy and 93% AUC score, which were assessed using 1626 test datasets. Taken together, the results show that the proposed method obtained excellent classification results and provided low parameter consumption, thereby reducing computational time to facilitate a real-time implementation.
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