[1]WANG Xiaoyan,LU Huaxiang,JIN Min,et al.Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation[J].CAAI Transactions on Intelligent Systems,2016,11(6):827-834.[doi:10.11992/tis.201611017]
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Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 6 Page number: 827-834 Column: 学术论文—机器学习 Public date: 2017-01-20
Title:
Wavelet entropy denoising algorithm of electrocardiogram signals based on correlation
Author(s):
WANG Xiaoyan1;  LU Huaxiang1; 2;  JIN Min1;  GONG Guoliang1;  MAO Wenyu1;  CHEN Gang1
1. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
2. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
Keywords:
electrocardiogram signals; denoising; correlation; wavelet entropy; adaptively
CLC:
TP391
DOI:
10.11992/tis.201611017
Abstract:
In view of the baseline drift, power line interference and muscle noise of electrocardiogram (ECG) signals, the wavelet entropy denoising algorithm of ECG signals based on correlation was proposed. First, ECG signals were decomposed using wavelets to determine the number of scale of wavelet decomposition, and the lowest approximation coefficients were each set to zero, so as to remove the baseline drift. Then, the high-frequency wavelet coefficient of adjacent scales was processed by adaptively calculating the global threshold with the correlation coefficients between the adjacent scales, to remove the power line interference and the muscle noise. Last, the denoising signals were reconstructed using zero approximation coefficients and processed wavelet coefficients. Using this method, three kinds of noise were removed in one process of wavelet decomposition and reconstruction. Experiments using the MIT-BIH database and simulative data prove that the algorithm is much better than others in ECG denoising with low complexity.
References:
[1] 赵艳娜, 魏珑, 徐舫舟, 等. 基于小波变换的心电信号去噪综合算法[J]. 现代生物医学进展, 2009, 9(16):3128-3130. ZHAO Yanna, WEI Long, XU Fangzhou, et al. ECG signal denoising algorithm based on wavelet transform[J]. Progress in modern biomedicine, 2009, 9(16):3128-3130.
[2] HARITHA C, GANESAN M, SUMESH E P. A survey on modern trends in ECG noise removal techniques[C]//Proceedings of 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). Nagercoil:IEEE, 2016:1-7.
[3] 赵志华, 许爱华. 基于形态学的ECG小波自适应去噪算法[J]. 计算机工程与设计, 2008, 29(8):2140-2142. ZHAO Zhihua, XU Aihua. ECG adaptive wavelet transform of denoising algorithm based on morphology[J]. Computer engineering and design, 2008, 29(8):2140-2142.
[4] TAI Shenchuan, SUN C C, YAN W C. A 2-D ECG compression method based on wavelet transform and modified SPIHT[J]. IEEE transactions on biomedical engineering, 2005, 52(6):999-1008.
[5] 杨思军, 郝继飞, 闫文杰, 等. 小波滤波与QRS波检测[J]. 计算机工程与应用, 2011, 47(12):239-241. YANG Sijun, HAO Jifei, YAN Wenjie, et al. Wave filtering and QRS detection[J]. Computer engineering and applications, 2011, 47(12):239-241.
[6] DONOHO D L. denoising by soft-thresholding[J]. IEEE transactions on information theory, 1995, 41(3):613-627.
[7] AGANTE P M, DE SA J P M. ECG noise filtering using wavelets with soft-thresholding methods[C]//Proceedings of 1999 Computers in Cardiology. Hannover:IEEE, 1999, 26:523-538.
[8] 侯宏花, 桂志国. 基于小波熵的心电信号去噪处理[J]. 中国生物医学工程学报, 2010, 29(1):22-28, 34. HOU Honghua, GUI Zhiguo. Denosing processing of ECG signal based on wavelet entropy[J]. Chinese journal of biomedical engineering, 2010, 29(1):22-28, 34.
[9] 欧阳波, 程栋, 王玲. 改进小波阈值算法在心电信号去噪中的应用[J]. 计算机工程与应用, 2015, 51(4):213-217. OUYANG Bo, CHENG Dong, WANG Ling. Improved wavelet threshold algorithm in application of ECG signal de-noising[J]. Computer engineering and applications, 2015, 51(4):213-217.
[10] LI Nianqiang, LI Ping. An improved algorithm based on EMD-wavelet for ECG signal de-nosing[C]//Proceedings of 2009 International Joint Conference on Computational Sciences and Optimization. Sanya, China:IEEE, 2009, 1:825-827.
[11] MALLAT S. 信号处理的小波导引[M]. 北京:机械工业出版社, 2003.
[12] 刘霞, 黄阳, 黄敬, 等. 基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J]. 吉林大学学报:地球科学, 2016, 46(1):262-269. LIU Xia, HUANG Yang, HUANG Jing, et al. Wavelet entropy threshold seismic signal denoising based on empirical mode decomposition (EMD)[J]. Journal of Jilin university:earth science edition, 2016, 46(1):262-269.
[13] 李文, 刘霞, 段玉波, 等. 基于小波熵与相关性相结合的小波模极大值地震信号去噪[J]. 地震学报, 2012, 34(6):841-850. LI Wen, LIU Xia, DUAN Yubo, et al. Wavelet modulus maxima denoising of seismic signals based on combined wavelet entropy and correlation[J]. Acta seismologica sinica, 2012, 34(6):841-850.
[14] ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy:a new tool for analysis of short duration brain electrical signals[J]. Journal of neuroscience methods, 2001, 105(1):65-75.
[15] 朱泽煌, 胡广书, 郭恒, 等. MIT-BIH心电数据库的开发及用作检测标准[J]. 中国生物医学工程学报, 1993, 12(4):244-249, 243.
[16] 徐效文, 曾超, 崔松野, 等. MIT-BIH数据库心电数据重采样研究[J]. 计算机工程与应用, 2011, 47(8):245-248. XU Xiaowen, ZENG Chao, CUI Songye, et al. Research on resampling of ECG data from MIT-BIH database[J]. Computer engineering and applications, 2011, 47(8):245-248.
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