基于CEEMD的心音信号小波包去噪算法研究
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摘要
针对传统心音去噪方法易将其部分高频有用信息作为噪声滤除而造成滤波后的心音信号失真及信息丢失的问题,提出了一种基于互补总体经验模态分解(CEEMD)的小波包变换去噪算法。首先通过互补总体经验模态分解将心音信号分解为从高频到低频的不同固有模态函数分量(IMFs),并利用自相关函数客观界定信号的模态分量范围;然后对噪声主导模态分量和混叠模态分量采用小波包变换进行滤波提取有用信息后,与剩余固有模态分量进行重构得到去噪后的信号。实验结果表明,改进的算法不仅可以去除心音中的噪声成分,明显改善心音信号的信噪比和均方根误差,而且能够有效保留信号的高频有用信息,且在不同噪声水平下的去噪性能均优于传统算法,鲁棒性较好。
Here, aiming at problems of traditional heart sound signals' de-noising method being easy to eliminate parts of high frequency useful information and cause distortion of heart sound signals and loss of information, a wavelet packet de-noising algorithm based on the complementary ensemble empirical mode decomposition(CEEMD) was proposed. Firstly, heart sound signals were decomposed into different intrinsic mode functions(IMFs) with CEEMD. Autocorrelation function was used to objectively define the range of modal components of a signal. Then, the useful information was extracted from noise dominant modal components and aliasing ones using the wavelet packet transformation, and it was used for reconstruction of the de-noised signal together with residual IMFs. The results showed that the proposed method can be used not only to eliminate noise components in heart sounds, and improve heart sounds' ratio of signal to noise and the root mean square error, but also to effectively retain heart sound signals' high-frequency useful information; compared with traditional algorithms, it has better de-noising performance and robustness under different noise levels.
Here, aiming at problems of traditional heart sound signals' de-noising method being easy to eliminate parts of high frequency useful information and cause distortion of heart sound signals and loss of information, a wavelet packet de-noising algorithm based on the complementary ensemble empirical mode decomposition(CEEMD) was proposed. Firstly, heart sound signals were decomposed into different intrinsic mode functions(IMFs) with CEEMD. Autocorrelation function was used to objectively define the range of modal components of a signal. Then, the useful information was extracted from noise dominant modal components and aliasing ones using the wavelet packet transformation, and it was used for reconstruction of the de-noised signal together with residual IMFs. The results showed that the proposed method can be used not only to eliminate noise components in heart sounds, and improve heart sounds' ratio of signal to noise and the root mean square error, but also to effectively retain heart sound signals' high-frequency useful information; compared with traditional algorithms, it has better de-noising performance and robustness under different noise levels.
引文
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[15] 林助军,严志军,肖敏,等.基于经验模式分解与小波软阈值的联合去噪法[J].青岛科技大学学报(自然科学版),2015,36(4):463-467.LIN Zhujun,YAN Zhijun,XIAO Min,et al.Signal de-noising based on empirical mode decomposition and wavelet soft threshold[J].Journal of Qingdao University of Science and Technology(Natural Science Edition),2015,36(4):463-467.
[16] CHEN X Y,WANG M,ZHANG Y L,et al.Detecting signals from data with noise:theory and applications[J].Journal of the Atmospheric Sciences,2013,70(5):1489-1504.
[2] TUZLUKOV V.Signal processing by generalized receiver in DS-CDMA wireless communication systems with frequency-selective channels[J].Circ Syst Signal Process,2011,330(6):1197-1230.
[3] ZENG K,HUANG J,DONG M.White gaussian noise energy estimation and wavelet multi-threshold de-noising for heart sound signals[J].Circ Syst Signal Process,2014,33:2987-3002.
[4] 高清河,刚晶,王和禹.基于平稳小波变换心音信号阈值去噪的对比分析[J].计算机与数字工程,2015,43(6):957-959.GAO Qinghe,GANG Jing,WANG Heyu.Comparison and analysis of heart sound signal threshold denoising based on stationary wavelet transform[J].Computer and Digital Engineering,2015,43(6):957-959.
[5] 郭兴明,袁志会.基于小波变换和经验模式分解的心音信号研究[J].中国生物医学工程学报,2012,31(1):39-44.GUO Xingming,YUAN Zhihui.Research on heart sounds signal based on wavelet transform and empirical mode decomposition[J].Chinese Journal of Biomedical Engineering,2012,31(1):39-44.
[6] WANG S T,ZHAO Z D.Adaptive denoising of the heart sound signal with EMD Threshold[C]//International Conference on Intelligent Computation and Industrial Application.Hong Kong,2011.
[7] 陈新华,成谢锋.一种改进型综合去噪算法在心音信号预处理上的研究[J].南京邮电大学学报(自然科学版),2010,30(6):96-100.CHEN Xinhua,CHENG Xiefeng.An improved compositive algorithm for PCG signals de-noising[J].Journal of Nanjing University of Postsand Telecommunications (Natural Science),2010,30(6):96-100.
[8] 卢德林,郭兴明.基于奇异谱分析的心音信号小波包去噪算法研究[J].振动与冲击,2013,32(18):63-69.LU Delin,GUO Xingming.Wavelet packet denoising algorithm for heart sound signal based on singular spectrum analysis[J].Journal of Vibration and Shock,2013,32(18):63-69.
[9] YEH J R,SHIEH J S,HUANG N E.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2011,2(2):135-156.
[10] HUANG N E.The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis[J].The Royal Society,1998,454(1971):903-995.
[11] ARI S,SAHA G.Classification of heart sounds using empirical mode decomposition based features[J].International Journal of Medical Engineering & Informatics,2008,1(1):91-107.
[12] WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise assisted date analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[13] ZHENG Y.Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics.[J].Computer Methods & Programs in Biomedicine,2015,122(3):372-383.
[14] 张国华,袁中凡,李彬彬.心音信号特征提取小波包算法研究[J].振动与冲击,2008,27(7):47-49.ZHANG Guohua,YUAN Zhongfan,LI Binbin.Research on wavelet packet algorithm for feature extraction of heart sound signal[J].Journal of Vibration and Shock,2008,27(7):47-49.
[15] 林助军,严志军,肖敏,等.基于经验模式分解与小波软阈值的联合去噪法[J].青岛科技大学学报(自然科学版),2015,36(4):463-467.LIN Zhujun,YAN Zhijun,XIAO Min,et al.Signal de-noising based on empirical mode decomposition and wavelet soft threshold[J].Journal of Qingdao University of Science and Technology(Natural Science Edition),2015,36(4):463-467.
[16] CHEN X Y,WANG M,ZHANG Y L,et al.Detecting signals from data with noise:theory and applications[J].Journal of the Atmospheric Sciences,2013,70(5):1489-1504.