Electrocardiograph signal denoising based on sparse decomposition

Junjiang Zhu; Xiaolu Li

doi:10.1049/htl.2016.0097

journal article Open Access Jun 29, 2017

Electrocardiograph signal denoising based on sparse decomposition

Junjiang Zhu Xiaolu Li

Healthcare Technology Letters Vol. 4 No. 4 pp. 134-137 · Institution of Engineering and Technology (IET)

View at Publisher Save 10.1049/htl.2016.0097

Abstract

Noise in ECG signals will affect the result of post‐processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse‐based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT‐BIH arrhythmia database, compared with wavelet transform (WT)‐based method. Signal–noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.

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References

22

[1]

10.1016/j.medengphy.2016.03.011

[2]

10.1109/tbme.2006.877119

[3]

Romero I. "PCA and ICA applied to noise reduction in multi‐lead ECG" Comput. Cardiol. (2011)

[4]

Romero I. "PCA‐based noise reduction in ambulatory ECGs" Comput. Cardiol. (2010)

[5]

Mourad T.: ‘ECG denoising based on PCA and usingRpeaks detection’.Int. Conf. on Bio‐Inspired Systems and Signal Processing 2015 pp.119–125 10.5220/0004998201190125

[6]

Singh N. Ayub S. Saini J.P.: ‘Design of digital IIR filter for noise reduction in ECG signal’.IEEE Computer Society Int. Conf. on Computational Intelligence and Communication Networks 2013 pp.171–176 10.1109/cicn.2013.45

[7]

Rahman M.Z.U. (2011)

[8]

Sonali S.O. Sunkaria R.K.: ‘ECG signal denoising based on empirical mode decomposition and moving average filter’.IEEE Int. Conf. on Signal Processing 2013 pp.1–6 10.1109/ispcc.2013.6663412

[9]

Maji U. Pal S.: ‘Empirical mode decomposition vs. variational mode decomposition on ECG signal processing: a comparative study’.Int. Conf. on Advances in Computing Communications and Informatics 2016 pp.1129–1134 10.1109/icacci.2016.7732196

[10]

10.1142/s0219691307001823

[11]

10.5755/j01.eee.20.3.6676

[12]

10.1109/lsp.2015.2388712

[13]

Deka B. "Sparse regularization method for the detection and removal of random‐valued impulse noise" Multimed. Tools Appl. (2016)

[14]

Mallat S. "A wavelet tour of signal processing" The Sparse Way (2013)

[15]

10.1109/51.932724

[16]

10.1109/tit.2004.834793

[17]

10.1109/tit.2007.909108

[18]

Atomic Decomposition by Basis Pursuit

Scott Shaobing Chen, David L. Donoho, Michael A. Saunders

SIAM Review 10.1137/s003614450037906x

[19]

10.1109/tit.2006.885507

[20]

10.1109/tsp.2010.2042490

[21]

Seena V. Yomas J.: ‘A review on feature extraction and denoising of ECG signal using wavelet transform’.IEEE Int. Conf. on Devices Circuits and Systems 2014 pp.1–6 10.1109/icdcsyst.2014.6926190

[22]

10.1186/s12938‐017‐0315‐1

Metrics

18

Citations

22

References

Details

Published: Jun 29, 2017
Vol/Issue: 4(4)
Pages: 134-137
License: View

Authors

J

Junjiang Zhu

Mechanical and Electronic Engineering Institute China Jiliang University Xueyuan Road 258, Jianggan District Hangzhou Zhejiang People's Republic of China

X

Xiaolu Li

Mechanical and Electronic Engineering Institute China Jiliang University Xueyuan Road 258, Jianggan District Hangzhou Zhejiang People's Republic of China

Cite This Article

Junjiang Zhu, Xiaolu Li (2017). Electrocardiograph signal denoising based on sparse decomposition. Healthcare Technology Letters, 4(4), 134-137. https://doi.org/10.1049/htl.2016.0097

Electrocardiograph signal denoising based on sparse decomposition

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