Abnormal heart sound classification using phonocardiography signals

Abstract

An intelligent support system is needed to assist in the identification of abnormalities of a human heart. The integration of signal processing with machine learning techniques is a new research trend in the studies of heart sound analysis. This paper proposes a heart sound feature dimension reduction and classification methods using supervised machine learning algorithms, by utilising the first (S1) and the second (S2) heart sounds, produced due to vibrations during the closure of heart valves. The features of S1 and S2 heart sounds are extracted in both time and frequency domains. Time domain features are based on S1 and S2 sound distance, amplitude, sound peak area, sound peak cycle duration and intensity, whilst 20 Mel Frequency Cepstral Coefficients (MFCCs) filter-bank energy for 12 coefficients represent the frequency domain features. Statistical values of the selected features are further used to increase the number of heart sound features. Due to the size of the extracted features, Linear Discriminant Analysis (LDA) dimensionality reduction technique has been used to select the best features for normal and abnormal heart sound classification using an Artificial Neural Network (ANN) model. It has been shown that the proposed LDA/ANN heart sound classification model achieved 90%, 83.33%, and 93.33% classification accuracies using the time domain, frequency domain and combined time-frequency domain features, respectively. The results using the proposed method are significantly better than previous classification methods by other researchers, with minimal complexity. This work provides a step forward in providing clinical informatics tool to assist clinician in providing early detections of abnormal heart conditions.