电子测量与仪器学报

张小兰,房玉,刘栋博,王维博,王海滨.肥心病心音时频杂波特征提取识别算法研究[J].电子测量与仪器学报,2020,34(4):20-26

肥心病心音时频杂波特征提取识别算法研究

Research on extraction algorithm of heart murmur features in time frequency domain for HCM recognition

DOI：

英文关键词:hypertrophic cardiomyopathy heart sound scaling factor spectral estimation frequency conversion homomorphic filtering

基金项目:国家自然科学基金（61571371）、国家青年科学基金（61901393）、教育部春晖计划（Z2018118）、西华大学研究生创新基金（ycjj2019052）、西华大学大健康管理促进中心2019年开放课题（DJKG2019-008，DJKG2019-005）资助项目

作者	单位
张小兰	1.西华大学电气与电子信息学院
房玉	1.西华大学电气与电子信息学院
刘栋博	1.西华大学电气与电子信息学院
王维博	1.西华大学电气与电子信息学院
王海滨	1.西华大学电气与电子信息学院

Author	Institution
Zhang Xiaolan	1.School of Electrical and Electronic Information, Xihua University
Fang Yu	1.School of Electrical and Electronic Information, Xihua University
Liu Dongbo	1.School of Electrical and Electronic Information, Xihua University
Wang Weibo	1.School of Electrical and Electronic Information, Xihua University
Wang Haibin	1.School of Electrical and Electronic Information, Xihua University

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中文摘要:

为了便捷有效地识别肥心病与正常心音，提出了基于心音信号的时频域(TFD)特征提取新方法。综合应用小波变换与主成分分析对信号进行降噪预处理；基于变频同态滤波(FCHF)提取信号的时域包络，进行分割定位，提取收缩期杂音能量Es与舒张期杂音能量Ed;通过谱估计提取心杂音缩放因子(SF)对时域Es与Ed进行加权处理，提出用于表征肥心病心杂音的量化指标。对100例正常心音和181例肥心病心音进行分类，验证提出量化指标的有效性，平均识别准确率可达92.97%，最优识别正确率可达95.37%，结果表明提取的特征能有效识别正常心音与肥心病心音。算法提出的量化指标能够有效表征肥心病病理性特征，研究提出的心杂音量化指标提取算法为肥心病心音的分类识别提供技术基础。

英文摘要:

In order to easily and effectively identify HCM and normal heart sound, this paper proposed a new method based on time frequency domain (TFD) feature extraction algorithm of heart murmur for HCM heart sound. Wavelet transform and principle component analysis were applied to preprocessing. The time domain envelope of the signal based on frequency conversion homomorphic filtering (FCHF) was extracted. The segmentation and localization were performed to extract the systolic energy Es and diastolic murmur energy Ed. The heart murmur scaling factor (SF) was extracted by power spectral estimation. The SF was used to weight Es and Ed, gaining the quantitative indicators for representing heart murmurs. 100 normal heart sound and 181 HCM heart sound were classified for verifying the validity of quantitative indicators. The accuracy on average was 92.97%, the best performance was 95.37%. Result represented that the extracted features can effectively classify normal heart sound and HCM heart sound. The algorithm extracted quantitative indicators of representing heart murmurs can effectively represent heart murmur. The proposed algorithm is used to provide technical basis for classification and recognition of HCM heart sounds.

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