基于奇异谱分析的心音信号小波包去噪算法研究  

Wavelet packet denoising algorithm for heart sound signal based on singular spectrum analysis

作　　者：卢德林[1] 郭兴明[1] 

机构地区：[1]重庆大学生物工程学院,重庆400044

出　　处：《振动与冲击》2013年第18期63-69,共7页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(30770551)

摘　　要：针对传统心音去噪算法对强噪声下心音信号去噪时,易将部分心音信号视为噪声成分去除,导致有用心音信号能量损失。利用奇异谱分析方法的主成分分析特性,提出多级奇异值分解(Multi-stage Singular Value Decomposition,MS-SVD)算法用于提取心音信号的主分量(Principal Components,PC)信息;采用小波包(Wavelet Packet,WP)分析算法对提取的心音信号进行分解,并对分解所得低频系数进行自适应阈值处理,去除低频噪声;利用小波包多分辨率特性提取高频心音。实验结果表明,该算法能明显改善心音去噪性能指标信噪比(SNR)、信噪比增益(SNRG)及根均方误差(RMSE),且在不同噪声水平下的去噪性能优于传统心音去噪算法。此改进算法既能有效去除心音中噪声成分,亦能保留心音信号细节特征。When a heart sound signal under strong noise levels is denoised using a traditional heart sound denoising algorithm, it is easy for part of heart sound signal to be regarded as noise and then removed, this leads to the energy loss of useful heart sound. A multi-stage singular value decomposition （MS-SVD） algorithm was proposed and used to pick up the principal components of a heart sound based on the features of the principal components of the singular spectrum analysis. In order to remove the low frequency noise, the heart sound extracted was decomposed into low-frequency coefficients and high-frequency ones with wavelet packet, and the adaptive threshold method was used to deal with the low-frequency coefficients. The high-frequency ones were also analyzed to pick up the heart sound with high frequency using the multi-resolution characteristics of wavelet packet. The simulation results illustrated that the proposed algorithm can improve obviously the denoising indexes of SNR, SNRG and RMSE, and its denoising performance under different noise levels is superior to that of the traditional method. The tests also demonstrated that the new algoritnm not only can remove the noise components in a heart sound efficiently, and also reserve the details of the original signal.

关 键 词：心音信号 奇异谱分析(SSA) 小波包算法(WP) 去噪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]