Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

Yi He,Wangqi Zhang,Hongying Liu,Xitian Pi,Wuyou Li,Sheng Zhang

doi:10.3390/app11020651

Abstract

The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

Highlights

The heart sound results from myocardial movement and the valve opening and closing; it is greatly affected by the hemodynamics and electrical activity of the heart muscle [1]
This paper proposed a method of using convolutional neural network (CNN) to study heart sound signals, which mainly involved segmentation and classification
U-net network composed of deep CNN to the segmentation step, and determined the relevant parameters of the model and trained the model that can segment heart sounds well through optimizing the relevant network structure and comparing the segmentation results under different optimizers and different input data lengths

Summary

Introduction

The heart sound results from myocardial movement and the valve opening and closing; it is greatly affected by the hemodynamics and electrical activity of the heart muscle [1]. In the early stage of cardiovascular disease, heart sound auscultation, as a means of preliminary screening for cardiovascular diseases, can help differentiate abnormal signals from normal heart sound signals, and, provide effective information for the auxiliary diagnosis of cardiovascular diseases. Though the electrocardiogram (ECG) signal contains a lot of physiological information on the cardiovascular system, it cannot reveal a lesion in the early stage of cardiovascular disease, for a lesion is not clear enough. This can be achieved by heart sounds during the early stage of the lesion. Heart sound signals contain very important physiological information, and the study of heart sound signals possesses very important clinical value for the early diagnosis of cardiovascular diseases. The segmentation and classification of heart sound signals are currently the most commonly used methods for studying the heart sound signal

Methods

Results

Discussion

Conclusion