基于样本熵快速算法的心音信号动力学分析  

Dynamic analysis of heart sound signal with a sample entropy fast algorithm

作　　者：王新沛[1] 杨静[2] 李远洋[3] 刘常春[1] 李丽萍[1] 

机构地区：[1]山东大学控制科学与工程学院,济南250061 [2]山东大学计算机科学与技术学院,济南250101 [3]山东大学附属省立医院,济南250101

出　　处：《振动与冲击》2010年第11期115-118,共4页Journal of Vibration and Shock

基　　金：国家"863"计划资助项目(2006AA02Z4D9)

摘　　要：为了准确刻画冠状动脉狭窄引起的血流动力学状态改变,提出了一种基于样本熵快速算法的舒张期心音分析方法。首先利用小波变换去除心音中的呼吸干扰,然后采用改进的香农能量算法自动分割出舒张期段,最后对分割出的舒张期心音用快速算法估计样本熵。对25例健康人和25例冠心病人的分析结果显示,冠心病人和健康人在舒张期心音的样本熵值上具有显著性差异。利用该方法检测冠状动脉狭窄,敏感性为80%,特异性为84%。The diastolic heart sound analysis method based on a sample entropy fast algorithm was proposed to exactly describe the change of hemodynamic character caused by coronary artery stenosis.Firstly,the heart sound signal was preprocessed by using wavelet to eliminate the respiratory interference.Then,the diastolic segments were segmented automatically using segmentation algorithm based on 3-order Shannon entropy.Finally,the average sample entropy of the diastolic heart sounds was estimated by the sample entropy fast algorithm.The nonlinear dynamic analysis (sample entropy) for the diastolic heart sounds recorded from 25 normal persons and 25 patients with coronary artery disease (CAD) were done.Results showed that there are significant differences between the sample entropy values of persons with and without CAD.This method led to the sensitivity of 80% and the specificity of 84% in detecting CAD.

关 键 词：心音信号 样本熵 冠心病 非线性动力学分析 

分 类 号：TN911[电子电信—通信与信息系统]