摘要
为了准确地提取心音信号的病理特征信息,在研究小波包分析的基础上,提出一种心音信号分频带能量特征提取的算法。基于心音信号频谱分析,采用能量集中度高、局部特性好的db6小波函数作为小波包母函数并选取适合心音信号分析的最优基,对不同的心音信号进行4层小波包分解,得到最优基的小波包系数。根据小波包系数与信号能量在时域上的等价关系,提取最优基频带的归一化能量作为心音信号的特征向量。采用类别可分离性判据,计算出该算法对正常和心脏疾病患者的心音特征的可分性测度均值为3.934 9,表明该算法能有效地识别不同的心音信号。
In order to extract pathological features of heart sound signal accurately, an algorithm for extracting the sub-band energy is developed on the basis of the wavelet packet analysis. In the light of the spectrum analysis of heart sound signal, the db6 wavelet, with high energy concentration and good local characteristics, is taken as the mother function , and the optimal wavelet packet basis of heart sound signal is picked out. Then, various heart sound signals are decomposed into four layers and the wavelet packet coefficients of the optimal basis are obtained. According to the equal-value relation in time domain between wavelet packet coefficients and signal energy, the normalized sub-band energy of the optimal basis is extracted as the feature vector. The average value of separability measure is 3.9349 by using the sort separability criterion, which indicates that the algorithm is effective for feature extraction of heart sound signal.
出处
《振动与冲击》
EI
CSCD
北大核心
2008年第7期47-49,共3页
Journal of Vibration and Shock
基金
国家科技部创新基金(02c26225120242)
关键词
心音信号
特征提取
小波包算法
类别可分离性判据
heart sound signal
feature extraction
wavelet packet algorithm
sort separability criterion
