摘要
为了准确提取心音信号的病理性信息,提出了一种基于提升小波变换的改进的特征提取方法。针对性地分析第一心音(S1)和第二心音(S2)及其时限,并对不同心音信号进行分类。首先利用提升小波软阈值降噪法对不同心音信号作去噪预处理;然后利用提升小波时间熵法检测心音信号在不同时刻的分布情况,并提取其熵值;通过香农能量优化双阈值法提取心音包络信号及S1、S2时限;最后改进选取心率、S1和S2时限、心动周期、包络面积、熵值6个特征参数,利用支持向量机算法对不同心音信号进行分类。分析和仿真结果表明,该算法对正常和心脏病患者的心音准确分类率达到98%,能有效识别不同心音信号。
In order to extract the pathological information of heart sounds accurately,an improved method of feature extraction based on lifting wavelet transform analysis was proposed to analyze the first and second heart sounds and recognition different heart sounds purposefully.Firstly,lifting wavelet transform was applied to decrease the noises of different heart sounds by soft threshold method.Secondly,lifting wavelet-time entropy was used to describe the distribution on time domain and extract the entropy.The Shannon energy and improved dual-threshold were then applied to extract the envelope and time of heart sounds.Finally,the best feature elements were analyzed by using SVM,which was used for the classification of sixty different heart sounds.The result showed that these heart sounds are successfully classified with the accuracy of 98%.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2013年第S1期123-127,共5页
Journal of Sichuan University (Engineering Science Edition)
关键词
心音
提升小波
香农能量双阈值
特征提取
SVM
heart sounds
lifting wavelet transform
dual-threshold of Shannon energy
feature extraction
SVM
