心音信号分类识别算法综述  

A Review of Classification Methods of Heart Sound Signal

作　　者：丁思吉 丁皓[1,2] 阚孟菲 庄逸 夏冬阳 盛诗梦 徐欣茹 DING Si-ji;DING Hao;KAN Meng-fei;ZHUANG Yi;XIA Dong-yang;SHENG Shi-meng;XU Xin-ru(School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Medical Devices,Shanghai University of Medicine and Health Sciences,Shanghai 201318,China)

机构地区：[1]上海理工大学健康科学与工程学院,上海200093 [2]上海健康医学院医疗器械学院,上海201318

出　　处：《软件导刊》2022年第12期272-278,共7页Software Guide

摘　　要：心脏听诊相较于心脏超声、磁共振成像等诊断方式,具有快速、低成本的特点。通过心音对心脏瓣膜病进行诊断需要丰富的临床经验,学习周期长,且主观性较强。随着计算机技术以及机器学习算法的发展,利用机器学习模型对心脏瓣膜病进行诊断受到了越来越多关注。利用机器学习模型分类心音信号的4个主要步骤是:心音信号数据收集、信号预处理、特征提取和模型训练。对主流心音数据库以及心音信号分类步骤进行介绍,阐述分类算法的效果及优缺点,并对未来进行展望。Compared with cardiac ultrasound, magnetic resonance imaging and other diagnostic methods, cardiac auscultation has the characterisitic of high speed and low cost. The diagnosis of valvular disease by heart sound needs large clinical experience, long training period of a physician. However, the results are subjective. With the development of computer technology and machine learning algorithm, the use of machine learning model to complete the diagnosis of valvular heart disease has attracted more and more attention. The main steps of machine learning classification of heart sound signal are data acquisition, signal preprocessing, feature extraction and model training. This paper will introduce the main heart sound database and the steps of heart sound signal classification. Then summarize the effects and advantages of classification algorithms. Finally, the future will be prospected.

关 键 词：心音信号 机器学习 神经网络 信号分割 特征提取 分类算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]