Hilbert domain characterizations of wavelet packets for automated heart sound abnormality detection

Abstract

Heart valve disease (HVD) is a common disease that affects millions of people worldwide. Early detection and treatment are essential for improving the prognosis of patients with HVD. Phonocardiogram (PCG) signals are a non-invasive and inexpensive way to assess the mechanical activity of the heart. In this study, a novel method for HVD detection using Hilbert domain mapping of wavelet packet of PCG signals is proposed. Two standard PCG databases are used to evaluate the proposed method. Packet instantaneous frequency deviation (PIFD) and packet instantaneous energy deviation (PIED) features are extracted from the PCG signals and used for classification. A support vector machine (SVM) and K-nearest neighbour (KNN) based error-correcting output code (ECOC) approach is used to handle multiclass classification and minimize classification error. The proposed method achieves an unweighted average recall (UAR) of 99.8% on database 1 and 99.32% on database 2, which outperforms other baseline methods. The results suggest that the proposed method is a promising approach for HVD detection using PCG signals.