Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System

Shamik Tiwari,Khaled Mohamad Almustafa,Akhilesh Kumar Sharma,Anurag Jain

doi:10.1109/access.2021.3103316

Abstract

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.

Highlights

As per the fact sheet available with WHO, CVD claims the lives of around 17.9 million people each year, and it is 31% of total death in a year, which makes CVD disease the number one cause of death
EXPERIMENT & RESULTS Four separate Convolutional Neural Network (ConvNet) models termed ConvNet-MEL FREQUENCY CEPSTRAL COEFFICIENTS (MFCCs), ConvNet-Constant-Q Transform (CQT), ConvNet-Variable-Q Transform (VQT), and ConvNet-Hybrid Constant-Q Transform (HCQT) are designed with MFCC, CQT, VQT, and HCQT spectrograms, respectively
The Librosa library in Python is used for generating MFCC, CQT, VQT, and HCQT spectrograms

Summary

Introduction

As per the fact sheet available with WHO, CVD claims the lives of around 17.9 million people each year, and it is 31% of total death in a year, which makes CVD disease the number one cause of death. Most deaths due to CVD occur in middle and low-income countries where medical facilities are either not available or very costly [1]. Diagnose at an early stage is the only way to decrease the death rate due to CVD. There are many invasive and non-invasive methods to diagnose CVD. All Invasive techniques are costly, painful, and readily unavailable at all places, especially in remote areas. Usage of a non-invasive method to diagnose CVD at an early stage is less expensive and painless.

Methods

Results

Conclusion