Study on Electrocardiogram Signal De-noising Methods Based on Ensemble Empirical Mode Decomposition Decomposed by White Noise_Journal of Biomedical Engineering

Authors：

RENBinbin , TANHaiyan , MAChengqun , LUDewei , LIUFangfang , ZHANGHaitao ,  LIZhangyong

College of Bio-information, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;

Corresponding author：

LIZhangyong, Email: lizy@cqupt.edu.cn

Keywords：

ensemble empirical mode decomposition; electrocardiogram signal; white noise; adaptive; de-noising

DOI：

10.7507/1001-5515.20160039

Video：

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Abstract Full text Figures/Tables Video References Cited by

Ensemble empirical mode decomposition (EEMD) is an effective method for non-stationary signal analysis, such as electrocardiogram (ECG) signals. However, the precision and correctness of EEMD are affected by the two parameters, ratio of the added noise and ensemble number. The values of two parameters are set relying on experience and lacking of adaptability for uncertain signals. In order to solve these problems, we proposed a method based on white noise decomposed by EEMD in the present study shown in this paper. Empirical mode decomposition (EMD) was applied to decompose the signal to different intrinsic mode functions (IMFs) in the de-noising process. The white noise IMFs were selected to constitute high frequency part based on the character that the product of the energy density of white noise and its average period tended to be a constant. Then the two parameters of EEMD were adaptively obtained according to the criterion which was used to avoid modal aliasing. Experimental results showed that the method was an effective one for ECG signal de-noising.

Citation： RENBinbin, TANHaiyan, MAChengqun, LUDewei, LIUFangfang, ZHANGHaitao, LIZhangyong. Study on Electrocardiogram Signal De-noising Methods Based on Ensemble Empirical Mode Decomposition Decomposed by White Noise. Journal of Biomedical Engineering, 2016, 33(2): 221-226. doi: 10.7507/1001-5515.20160039 Copy

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2.	KABIR M A, SHAHNAZ C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J]. Biomed Signal Process Control, 2012, 7(5):481-489.
3.	WU Z, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data anlysis method[J]. Adv Adapt Data Anal, 2009, 1(1):1-41.
4.	陈略, 唐歌实, 訾艳阳, 等.自适应EEMD方法在心电信号处理中的应用[J].数据采集与处理, 2011, 26(3):361-366.
5.	何星, 王宏力, 姜伟, 等.改进的自适应EEMD方法及其应用[J].系统仿真学报, 2014, 26(4):869-873.
6.	CHANG Kangming. Arrhythmia ECG noise reduction by ensemble empirical mode decomposition[J]. Sensors (Basel), 2010, 10(6):6063-6080.
7.	SINGH G, KAUR G, KUMAR V. ECG denoising using adaptive selection of IMFs through EMD and EEMD[C]//International Conference on Data Science & Engineering (ICDSE). Kochi, India:2014:228-231.
8.	WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proce R Soc Lond A, 2004, 460:1957-1611.
9.	张磊邦, 唐荣斌, 蒋建波, 等.心音信号的预处理与包络提取算法研究[J].生物医学工程学杂志, 2014, 31(4):734-741.
10.	NARONA N H, MUKHERJEE S, KUAMR V. Wavelet based non linear thresholding techniques for pre-processing ECG Signals[J]. International Journal of Biomedical and Advance Research, 2013, 4(8):534-544.
11.	AGARWAL M, JAIN R C. Ensemble empirical mode decomposition:An adaptive method for noise reduction[J]. Journal of Electronics and Communication Engineering, 2013, 5(5):60-65.
12.	JENITTA J, RAJESWARI A. Denoising of ECG Signal based on Improved Adaptive filter with EMD and EEMD[C]//2013 IEEE Conference on Information & Communication Technologies (ICT). JeJu Island:2013:957-962.

1. BLANCO-VELASCO M, WENG Binwei, BARNER K E. ECG signal denoising and baseline wander correction based on the empirical mode decomposition[J]. Comput Biol Med, 2008, 38(1):1-13.
2. KABIR M A, SHAHNAZ C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J]. Biomed Signal Process Control, 2012, 7(5):481-489.
3. WU Z, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data anlysis method[J]. Adv Adapt Data Anal, 2009, 1(1):1-41.
4. 陈略, 唐歌实, 訾艳阳, 等.自适应EEMD方法在心电信号处理中的应用[J].数据采集与处理, 2011, 26(3):361-366.
5. 何星, 王宏力, 姜伟, 等.改进的自适应EEMD方法及其应用[J].系统仿真学报, 2014, 26(4):869-873.
6. CHANG Kangming. Arrhythmia ECG noise reduction by ensemble empirical mode decomposition[J]. Sensors (Basel), 2010, 10(6):6063-6080.
7. SINGH G, KAUR G, KUMAR V. ECG denoising using adaptive selection of IMFs through EMD and EEMD[C]//International Conference on Data Science & Engineering (ICDSE). Kochi, India:2014:228-231.
8. WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proce R Soc Lond A, 2004, 460:1957-1611.
9. 张磊邦, 唐荣斌, 蒋建波, 等.心音信号的预处理与包络提取算法研究[J].生物医学工程学杂志, 2014, 31(4):734-741.
10. NARONA N H, MUKHERJEE S, KUAMR V. Wavelet based non linear thresholding techniques for pre-processing ECG Signals[J]. International Journal of Biomedical and Advance Research, 2013, 4(8):534-544.
11. AGARWAL M, JAIN R C. Ensemble empirical mode decomposition:An adaptive method for noise reduction[J]. Journal of Electronics and Communication Engineering, 2013, 5(5):60-65.
12. JENITTA J, RAJESWARI A. Denoising of ECG Signal based on Improved Adaptive filter with EMD and EEMD[C]//2013 IEEE Conference on Information & Communication Technologies (ICT). JeJu Island:2013:957-962.

Journal of Biomedical Engineering

Study on Electrocardiogram Signal De-noising Methods Based on Ensemble Empirical Mode Decomposition Decomposed by White Noise

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