基于心音同态包络的无需分段分类方法  

Non-segment classification method based on heart sound homomorphic envelope

作　　者：成谢锋[1,2] 黄健钟 CHENG Xiefeng;HUANG Jianzhong(College of Electronic and Optical Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,Jiangsu,China;National and Local Joint Engineering Laboratory of RF Integration&Micro-Assembly Technology,Nanjing 210023,Jiangsu,China)

机构地区：[1]南京邮电大学电子光学与工程学院,江苏南京210023 [2]射频集成与微组装技术国家地方联合工程实验室,江苏南京210023

出　　处：《陕西师范大学学报（自然科学版）》2020年第6期33-39,共7页Journal of Shaanxi Normal University：Natural Science Edition

基　　金：国家自然科学基金(61271334)。

摘　　要：针对病理性心音和正常心音差异导致的心音信号准确分段问题,本文提出一种无需对心音信号分段即可分类识别的方法。首先,对心音信号进行滤波处理;之后,将提取的心音同态特征包络取自相关函数,按照心音的特点,设计卷积神经网络(convolutional neural network,CNN)作为分类器;最后,进行训练、验证及测试。实验结果表明:本文方法在验证集上得到的准确率为100%,在测试集上得到的修正准确率为90.21%。In the feature extraction of heart sound signals,most researchers use a method of segmenting heart sounds.However,due to the difference between pathological heart sound and normal heart sound,it is difficult to find a way to accurately segment all kinds of heart sound signals.Therefore,this paper proposes a classification recognition method without segmentation.The signal is filtered and it is extracted the autocorrelation function from the envelope of the heart sound homomorphic feature and saved as an image format.Then,according to the characteristics of the heart sound,the convolutional neural network(CNN)is designed as a classifier.Finally,training,verification,and testing are performed.The experimental results show that the recognition rate is 100%on the verification set and the modify accuracy is 90.21%on the test set.

关 键 词：心音 同态特征包络 自相关函数 卷积神经网络 

分 类 号：R318[医药卫生—生物医学工程] TN911.72[医药卫生—基础医学]