基于自适应锥形核的心音信号时频特征提取与分析  

Extraction and Analysis of Time-frequency Features for Heart Sound Signal Based on Adaptive Cone-kernel

作　　者：陈健[1] 王海滨[1] 孙树平[1] 

机构地区：[1]西华大学电气信息学院,四川成都610039

出　　处：《西华大学学报（自然科学版）》2010年第5期4-6,16,共4页Journal of Xihua University:Natural Science Edition

基　　金：四川省教育厅自然科学基金重点项目(09209025)

摘　　要：特征提取及其维数大小的确定是实现心音信号有效分类的重要环节,本文提出了一种基于自适应锥形核的心音信号时频特征提取的新方法。首先选用心音信号时频分析归一化能量的10%E、50%E、90%E和能量最大处所对应的频率点Fm ax构成第一、第二心音信号的八维特征向量,其次采用F isher降维方法的检验参数对其相关性进行了比较研究,选取相关性相对较小的参数F-10%和F-Fm ax构成二维特征向量,并将其应用于五种心音信号特征参数散度的对比分析。结果表明:降维后的二维特征向量较全面的反映了心音信号的特征,提高了分类算法的效率,具有临床实用价值。Feature extraction and the size of dimension are important factors for implementing effective classification of heart sound signals.This paper presents a new method of extracting time-frequency features for heart sound signals based on adaptive cone-kernel.Firstly,time-frequency analysis normalized energy 10%E,50%E,90%E of heart sound signals and the largest energy frequency points Fmax are used to constitute the eight-dimensional feature vector of first and second heart sound signals.Secondly,the test parameters of Fisher dimension reduction method is adopted to conduct the comparative study for the correlation.Relatively small parameters F-10% and F-Fmax are selected to form the two-dimensional feature vector and apply them to the analysis of characteristic parameter divergence.The results show that the two-dimensional feature vector reflects the characteristics of heart sound signals comprehensively,improves the algorithm effectively and has some clinical values.

关 键 词：心音信号 自适应锥形核 时频分析 特征向量 降维 

分 类 号：R318.6[医药卫生—生物医学工程]