Abstract
The growing prevalence and high mortality rate due to valvular heart diseases (VHD) are concerned. Therefore, its accurate, rapid, and early diagnosis is important. This study processes the phonocardiogram (PCG) signals for automatic detection of VHD associated with aortic stenosis (AS), mitral stenosis (MS), mitral valve prolapse (MVP) and mitral regurgitation (MR) which is originated by the MVP. In the proposed approach, we have used Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) for capturing the non-stationary nature of PCG signals. It follows by selecting significant Fourier-Bessel intrinsic mode functions (FBIMFs) and extraction of geometrical features from two-dimensional phase space reconstruction (2D-PSRs) of the FBIMFs. To reduce the real-time processing load, the feature set dimension is reduced by using metaheuristics optimization based features selection (FS) algorithms, namely, the salp swarm optimization algorithm (SSOA), emperor penguin optimization algorithm (EPOA), and tree growth optimization algorithm (TGOA). These FS methods have been tested and compared with machine learning classifiers. The result indicates the effectivity of FBSEEWT based features extraction and used FS methods in classifying the intended categories of PCG signals. With the reduced features set, obtained with SSOA, the proposed approach has resulted in the highest classification accuracies of 98.53 %, 98.84 %, 99.07 % and 99.70 % respectively for the five classes, four classes, three classes, and two classes problems. Thus, besides aiding the cardiologists, this approach is also useful for developing wearable cardiac devices (as it uses a reduced feature set) for heart health monitoring and diagnosis purposes.