Phonocardiogram Classification Based on Machine Learning with Multiple Sound Features

Abstract

In this study the heartbeat sound signals were tackled by classifying them into heart disease categories such as normal, artifact, murmur and extrahals in an attempt for early detection of heart defects. Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sounds using an electronic stethoscope or mobile device. Multiple features are extracted from the digital recording dataset such as MFCC, Delta MFCC, FBANK and a combination between MFCC and FBANK features. Moreover, to classify the heartbeat sound signals, multiple well-known machine learning classifiers were used such as Naive Bays (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The evaluation processes went through five metrics: Confusion matrix, accuracy, F1 score, precision and recall evaluating the recognition rate. Comparative experimental results show that the correctness of the feature with a best accuracy 99.2% adopted by MFCC and FBANK combination features which reduce false detection.