Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System

A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository https://github.com/shamiktiwari/ PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.