Removal Algorithm of Power Line Interference in Electrocardiogram Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition_Journal of Biomedical Engineering

Authors：

 ZHAOWei ¹ , XIAOShixiao ¹ , ZHANGBaocan ¹ , HUANGXiaojing ² , YOURongyi ²

1. Chengyi University College, Jimei University, Xiamen 361021, China;
2. School of Sciences, Jimei University, Xiamen 361021, China;

Corresponding author：

ZHAOWei, Email: zhaowei701@163.com

Keywords：

power line interference; electrocardiogram signal; morphological component analysis; ensemble empirical mode decomposition

DOI：

10.7507/1001-5515.20150209

Video：

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Abstract Full text Figures/Tables Video References Cited by

Electrocardiogram (ECG) signals are susceptible to be disturbed by 50 Hz power line interference (PLI) in the process of acquisition and conversion. This paper, therefore, proposes a novel PLI removal algorithm based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD). Firstly, according to the morphological differences in ECG waveform characteristics, the noisy ECG signal was decomposed into the mutated component, the smooth component and the residual component by MCA. Secondly, intrinsic mode functions (IMF) of PLI was filtered. The noise suppression rate (NSR) and the signal distortion ratio (SDR) were used to evaluate the effect of de-noising algorithm. Finally, the ECG signals were re-constructed. Based on the experimental comparison, it was concluded that the proposed algorithm had better filtering functions than the improved Levkov algorithm, because it could not only effectively filter the PLI, but also have smaller SDR value.

Citation： ZHAOWei, XIAOShixiao, ZHANGBaocan, HUANGXiaojing, YOURongyi. Removal Algorithm of Power Line Interference in Electrocardiogram Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition. Journal of Biomedical Engineering, 2015, 32(6): 1179-1184. doi: 10.7507/1001-5515.20150209 Copy

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10.	李辉, 郑海起, 唐力伟.基于改进形态分量分析的齿轮箱轴承多故障诊断研究[J].振动与冲击, 2012, 31(12):135-140.
11.	BRUCE A, SARDY S, TSENG P. Block coordinate relaxation method for nonparametric signal denoising[C]//Proceedings of International Society for Optical Engineering (SPIE). Orlando, FL, USA:1998, 3391:75-86.
12.	HUANG N E, ZHENG S, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc Roy Soc London A, 1998, 454:903-995.
13.	WU Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.

1. 刘秀玲, 乔磊, 杨建利, 等.改进小波阈值法用于心电信号去噪[J].生物医学工程学杂志, 2014, 31(3):511-515.
2. 刘胜洋, 张根选, 曹阳.心电信号中的工频干扰滤除方法[J].生物医学工程学杂志, 2014, 31(3):577-582.
3. 胡晓, 魏薇, 刘长红, 等.基于正弦参数估计的工频干扰消除算法[J].生物医学工程学杂志, 2010, 27(6):1243-1246.
4. LEVKOV C, MIHOV G, IVANOV R, et al. Removal of power-line interference from the ECG:a review of the subtraction procedure[J]. BioMedical Engineering OnLine, 2005, 4:50-68.
5. 张雪, 王海燕, 李保军, 等.临床心电信号工频干扰小波去噪方法对比分析[J].计算机测量与控制, 2010, 18(4):902-905.
6. ZHAO W, HUANG X J, YOU R Y. Removal of white noise from ECG signal based on morphological component analysis[J]. Chinese Journal of Biomedical Engineering(English Edition), 2014, 23(1):1-5.
7. STARCK J L, ELAD M, DONOHO D. Redundant multi-scale transforms and their application for morphological component analysis[J]. Advances in Imaging and Electron Physics, 2004, 132(82):287-348.
8. ABRIAL P, MOUDDEN Y, STARCK J L, et al. Morphological component analysis and inpainting on the sphere:application in physics and astrophysics[J]. Journal of Fourier Analysis and Applications, 2007, 13(6):729-748.
9. 李映, 张艳宁, 许星.基于信号稀疏表示的形态成分分析:进展和展望[J].电子学报, 2009, 37(1):146-152.
10. 李辉, 郑海起, 唐力伟.基于改进形态分量分析的齿轮箱轴承多故障诊断研究[J].振动与冲击, 2012, 31(12):135-140.
11. BRUCE A, SARDY S, TSENG P. Block coordinate relaxation method for nonparametric signal denoising[C]//Proceedings of International Society for Optical Engineering (SPIE). Orlando, FL, USA:1998, 3391:75-86.
12. HUANG N E, ZHENG S, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc Roy Soc London A, 1998, 454:903-995.
13. WU Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.

Journal of Biomedical Engineering

Removal Algorithm of Power Line Interference in Electrocardiogram Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

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