%0 Journal Article
%T 基于高斯混合模型的心音分类研究<br>Gaussian Mixture Model-Based Diagnostic Research for Combined Heart Diseases Shuping Sun, Tingting Huang, Yarui Pan, Biqiang Zhang, Baojin Liu, Jie Wu
%A 孙树平
%A 黄婷婷
%A 潘亚瑞
%A 张弼强
%A 刘保进
%A 吴杰
%J Computer Science and Application
%P 483-492
%@ 2161-881X
%D 2020
%I Hans Publishing
%R 10.12677/CSA.2020.103051
%X <div style=\"text-align:justify;\">
	<span style=\"font-size:14px;\">为表示复合心脏病的复杂诊断信息，本研究提出一种基于高斯混合模型结合概率诊断心脏病的方法。本文结构如下：首先，利用电子听诊器采集心音信号；其次，采用小波分解对心音信号进行预处理，以保留心音信号有效成分，然后采用功率谱分析结合阈值线方法提取心音信号频率域特征；最后，基于精度建立最优高斯混合模型数量并结合后验概率得出诊断结果。为验证本研究有效性，以2560秒心音数据作为研究对象，实验结果表明，该方法可以概率模式描述不同类别所属程度得出最终诊断结果。<br />
To express the diagnosis information for combined heart diseases by diagnosing heart sound, this study proposes a Gaussian mixture model (GMM)-based classification method combined with the probability diagnostic results to diagnose heart diseases. This paper is organized as follows. Firstly, heart sound is collected using an electronic stethoscope. And then, wavelet decomposition is employed to preprocess the heart sound signal and retain the effective components of the heart sound signal, and the power spectrum density method combined with threshold line method is proposed to extract the features for the heart sound signal in the frequency-domain. Finally, the optimal GMM is determined based on the highest accuracy, and the posterior probabilities are proposed to express the diagnostic results. The performance evaluation is verified by the 2560 seconds sounds, and the research results show that the sounds with complex heart diseases can be directly recognized using the different probabilities to describe which kind of heart disease it belongs to.</span> 
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%K 心音信号，高斯混合模型，特征提取，分类<br>Heart Sound
%K Gaussian Mixture Model
%K Feature Extraction
%K Posterior Probability
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=34586