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, Volume 78, Issue 10, pp 13067–13089 | Cite as

ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes

  • Lahcen El BounyEmail author
  • Mohammed Khalil
  • Abdellah Adib
Article

Abstract

In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kurtosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then reconstructed by combining the thresholded IMFs and the retained unprocessed lower frequency relevant IMFs. Various tests and simulations are investigated to evaluate the performance of our proposed approach in combination with the EMD, Ensemble EMD (EEMD) and CEEMDAN algorithms. The simulation results carried on MIT-BIH Arrhythmia database, show that CEEMDAN method gives better performance than the two other methods, and outperforms some state-of-the-art methods in terms of Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE).

Keywords

ECG Denoising EMD EEMD CEEMDAN Interval thresholding Higher order statistics 

Notes

Acknowledgements

The authors would like to thank the editorial office Christian Malan, the editor-in-chief, the guest editors as well as the anonymous reviewers for their valuable comments and suggestions that helped to very much improve the quality of this paper.

This research was supported by the National Center for Scientific and Technical Research of Morocco (CNRST) (grant number : 148UH22017).

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Copyright information

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

Authors and Affiliations

  1. 1.LIM@II-FSTMMohammediaMorocco

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