HMM-Based Multi-Heartbeat Phonocardiogram Classification Using Wavelet Cepstral Coefficients

Abstract

Heart sound classification systems often rely on analyzing a single heartbeat to classify phonocardiogram (PCG) signals. This study introduces a novel approach for classifying multi-heartbeat PCG signals as normal or abnormal, leveraging Wavelet Cepstral Coefficients (WCC) extracted from the Discrete Wavelet Transform (DWT). A Hidden Markov Model (HMM) classifier, associated with a Gaussian Mixture Model (GMM), is bases this system on the modeling of each class. The aim of this work is to develop an effective system for classification of multi-heartbeat PCG signals. The proposed system was evaluated on a subset of the PASCAL heart sounds classification challenge, using the Classification Rate (Acc HTK) as the primary performance metric. The optimal configuration was obtained with an HMM model comprising 8 states, each associated with 3 Gaussians. A 20 ms analysis window was used. The WCC descriptor, computed using the db7 wavelet with a decomposition level of 6, further improved performance, achieving a classification rate of 97.73 %. These results highlight the effectiveness of WCC descriptors in PCG signal classification and demonstrate the potential of HMM-based multi-heartbeat classification for improved heart sound analysis.