基于Hilbert边际能量的舒张期心音诊断算法  

Diagnosis algorithm for diastolic heart sound based on Hilbert marginal energy

作　　者：任静 赵治栋 REN Jing;ZHAO Zhi-dong(School of Communication Engineering, Hangzhou Dianzi University, Itangzhou 310018, China;HangDian Smart City Research Center of Zhejiang Province, Hangzhou 310018, China)

机构地区：[1]杭州电子科技大学通信工程学院,浙江杭州310018 [2]浙江省杭电智慧城市研究中心,浙江杭州310018

出　　处：《传感器与微系统》2018年第5期128-131,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61102133);浙江省公益性技术应用研究计划资助项目(2015C33188);浙江省智慧城市区域协同创

摘　　要：针对心音信号的病理特性及傅里叶频谱分析的局限性,提出了以多频率阈值下的Hilbert边际能量比值作为特征集区分正常和异常心音的方法。利用希尔伯特—黄变换(HHT)获取边际能量谱,将高频能量占总能量的比值作为特征。对比正常和异常心音能量谱以及各频率点能量比值的差异,确定频率阈值范围,构造特征集,通过主成分分析(PCA)降维去除冗余特征,结合支持向量机(SVM)分类。对18例正常人和18例冠心病人进行分析,结果表明:在多频率阈值80~160 Hz下,构造的特征集在两类样本中具有显著性差异。Aimming at the problem of physiological and pathological characteristics and limitations of Fourier spectral analysis, put forward a method of Hilbert marginal energy radio as feature set under multiple frequency thresholds. Use the Hilbert Huang transform（HHT） ,get Hilbert marginal spectrum energy and make the the ratio of high frequency energy to total energy as feature. Compare to the energy spectrum of the Hilbert marginal difference of the normal and abnormal heart sounds, the range of frequency threshold is determined, and construct feature set, through the principal component analysis （PCA）to reduce dimension, so as to remove redundancy feature, combined with support vector machine （ SVM ） classification. 18 cases of normal people and 18 cases of patients are analyzed, and the results show that under the 80 - 160 Hz multiple frequency thresholds, the characteristic set has significant difference in the two classes of samples.

关 键 词：舒张期心音信号 希尔伯特—黄变换 边际能量比值 主成分分析 支持向量机 

分 类 号：R318.04[医药卫生—生物医学工程]