ECG Denoising Based on MP Algorithm

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Abstract:

The electrocardiogram (ECG) signal is an important basis of diagnosing cardiopathy. It is very important to remove the noises of the signal before the use. Matching pursuit (MP) algorithm uses overcomplete dictionary to decompose the signal, so it can reflect the properties of the signal. In this paper we denoise the ECG signal using MP algorithm,whose dictionary is composed of Gabor atoms.The experiment simulation results show that the proposed method has a good denosing effect.

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510-513

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October 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Manuel Blanco-Velasco, BinweiWeng, Kenneth E. Barner, ECG signal denoising and base line wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, vol. 38(2008), p.1.

DOI: 10.1016/j.compbiomed.2007.06.003

Google Scholar

[2] P.E. Tikkanen, Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal, Biol. Cybern, vol. 80(1999) , p.259.

DOI: 10.1007/s004220050523

Google Scholar

[3] C.Y. -F. Ho, B.W. -K. Ling, T.P. -L. Wong, A.Y. -P. Chan, P.K. -S. Tam, Fuzzy multiwavelet denoising on ECG signal, Electron. Lett., vol. 39 (2003), p.1163.

DOI: 10.1049/el:20030757

Google Scholar

[4] E. Ercelebi, Electrocardiogram signals de-noising using lifting-based discrete wavelet transform, Comput. Biol. Med, vol. 34(2004) , p.479.

DOI: 10.1016/s0010-4825(03)00090-8

Google Scholar

[5] S. Poornachandra, N. Kumaravel, Hyper-trim shrinkage for denoising of ECG signal, Digital Signal Process, vol. 15(2005) , p.317.

DOI: 10.1016/j.dsp.2004.12.005

Google Scholar

[6] CHEN Gang, TANG Ming –hao, CHENG Hui, GE Man, An ECG Denoising Algorithm Based on Morphology and Wavelet Transform, COMPUTER TECHNOLOGY AND DEVELOPMENT, vol. 22(2012), p.100.

Google Scholar

[7] V. Almenar, A. Albiol, A new adaptive scheme for ECG enhancement, Signal Process, vol. 75 (1999), p.253.

DOI: 10.1016/s0165-1684(98)00237-0

Google Scholar

[8] A.K. Barros, A. Mansour, N. Ohnishi, Removing artifacts from electrocardiographic signals using independent components analysis, Neurocomputing , vol. 22(1998), p.173.

DOI: 10.1016/s0925-2312(98)00056-3

Google Scholar

[9] Mallat S, Zhang Z. Matching pursuit with time-frequency dictionaries, IEEE Trans on Signal Processing, vol. 41(1993, p.3397.

DOI: 10.1109/78.258082

Google Scholar

[10] SUN Yu-bao, WU Min, WEI Zhi-hui, XIAO liang, FENG Can, EEG spike detection using sparse representation(In Chinese), Acta Electronica Sinica, vol. 3(2009), p. (1971).

Google Scholar

[11] JING Ai-wen, LIU Yun, MA Yi-li, Speech signal sparse decomposition based on matching pursuit algorithm(In Chinese), Computer Engineering and Applications, vol. 45(2009), p.144.

Google Scholar

[12] Durka PJ, Adaptive time-frequency parametrization of epileptic spikes, Physical Review E, vol. 69(2004), p. (1914).

DOI: 10.1103/physreve.69.051914

Google Scholar

[13] Durka P J, Blinowska KJ, A unified time-frequency parametrization of EEG, IEEE Engineering in Medicineand Biology, vol. 20(2001), p.47.

DOI: 10.1109/51.956819

Google Scholar

[14] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet, Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23): e215-e220 [Circulation Electronic Pages; http: /circ. ahajournals. org/cgi/content/full/101/23/e215]; (2000).

DOI: 10.1161/01.cir.101.23.e215

Google Scholar

[15] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3): 45-50 (May-June 2001).

DOI: 10.1109/51.932724

Google Scholar