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Circuits, Systems, and Signal Processing

, Volume 38, Issue 1, pp 138–152 | Cite as

An Effective Optimization Scheme for ECG Signal Denoising via Low-Rank Matrix Decomposition

  • Qian Ye
  • Nian Cai
  • Hao Xia
  • Guandong Cen
  • Xindu Chen
  • Han Wang
Article
  • 112 Downloads

Abstract

Electrocardiogram (ECG) signal denoising is an important preprocessing for ECG signal analysis. The contaminated ECG signal can be considered as the combination of the desired clean signal and the noise. Thus, ECG signal denoising can be considered as a problem of obtaining an optimal solution to the desired clean signal. In this paper, an effective optimization scheme for ECG signal denoising is presented based on low-rank matrix decomposition. First, the ECG denoising problem is formulated as low-rank matrix decomposition. So, an ECG beats matrix is assumed to be the combination of a sparse noise matrix and a low-rank matrix. Considering the repeatability of ECG signal, the rank of the ECG beats matrix is assumed to be one. Then, to fully exploit the low-rank property of the ECG signal, the matrix decomposition is modified by means of adding different weights to different singular values. Finally, the desired clean ECG signal is reconstructed by the low-rank component. The experimental results show that the proposed denoising method achieves the best performance of suppressing the electromyographic noise in the ECG signals compared with other optimization models.

Keywords

Electrocardiographic signal Low-rank decomposition Robust principle component analysis Weighted nuclear norm minimization Electromyographic noise 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61001179 and 61571139), Guangdong Science and Technology Plan (Grant Nos. 2015B010104006, 2015B010124001 and 2015B090903017) and Guangzhou Science and Technology Plan (Grant Nos. 201604016022, 201803010065 and 201802020007).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Qian Ye
    • 1
  • Nian Cai
    • 1
  • Hao Xia
    • 1
  • Guandong Cen
    • 1
  • Xindu Chen
    • 2
  • Han Wang
    • 2
  1. 1.School of Information EngineeringGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Electromechanical EngineeringGuangdong University of TechnologyGuangzhouChina

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