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

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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).

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Notes

  1. The kurtosis function does not subtract 3 from the computed value as in (14).

  2. It was founded, after several repetitive simulations, that a rarely occurred problem can be shown when the boundaries K4jmax and K4js are consecutive. In this case, the HIT is now applied to the second set of IMFs.

  3. In our scheme, we have used the Stationary Wavelet Transform (SWT) [29].

  4. The choice is justified by the great similarity between ′db4′ and QRS complex shape.

  5. By using the Inverse Stationary Wavelet Transform (ISWT).

  6. To be in the same conditions to [11], the number of iterations is fixed to 200 with a 0.2 standard deviation for white Gaussian noise.

  7. The denoising principle in [6] is re-implemented by discarding only the first two IMFs.

  8. ECG record amplitudes are not divided by the the amplification gain of 200 for the MITDB.

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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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  1. LIM@II-FSTM, B.P. 146, Mohammedia, 20650, Morocco

    Lahcen El Bouny, Mohammed Khalil & Abdellah Adib

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  1. Lahcen El Bouny
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  2. Mohammed Khalil
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  3. Abdellah Adib
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Correspondence to Lahcen El Bouny.

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El Bouny, L., Khalil, M. & Adib, A. ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes. Multimed Tools Appl 78, 13067–13089 (2019). https://doi.org/10.1007/s11042-018-6143-x

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  • DOI: https://doi.org/10.1007/s11042-018-6143-x

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