Multimedia Tools and Applications

, 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


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).


ECG Denoising EMD EEMD CEEMDAN Interval thresholding Higher order statistics 



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).


  1. 1.
    Alfaouri M, Daqrouq K (2008) ECG Signal denoising by wavelet transform thresholding. Am J Appl Sci 5:276–281CrossRefGoogle Scholar
  2. 2.
    Analyse Of Variance (ANOVA). [Online]. Available:
  3. 3.
    Ari S, Das MK, Chacko A (2013) ECG Signal enhancement using S-transform. Comput Biol Med 43(6):649–660CrossRefGoogle Scholar
  4. 4.
    Blanco-Velasco M, Weng B, Barner K (2008) ECG Signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 38(1):1–13CrossRefGoogle Scholar
  5. 5.
    Boudraa AO, Cexus JC (2007) EMD-Based signal filtering. IEEE Trans Instrum Meas 56(6):2196–2202CrossRefGoogle Scholar
  6. 6.
    Chang KM, Liu SH (2011) Gaussian noise filtering from ECG by wiener filter and ensemble empirical mode decomposition. J. Signal Process. Syst. 64(2):249–264CrossRefGoogle Scholar
  7. 7.
    Chawla M (2009) A comparative analysis of principal component and independent component techniques for electrocardiograms. Neural Comput Appl, Springer-Verlag Lond Limited 18:539–556CrossRefGoogle Scholar
  8. 8.
    Clifford GD, Azuaje F, McSharry PE (2006) Advanced Methods and Tools for ECG Data Analysis. Artech House Engineering in Medicine & Biology SeriesGoogle Scholar
  9. 9.
    Donoho D (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41 (3):613–627MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    EL bouny L, Khalil M, Adib A (2017) ECG Noise Reduction Based on Stationary Wavelet Transform and Zero-Crossings Interval Thresholding. In: IEEE ICEIT International Conference. Rabat. MoroccoGoogle Scholar
  11. 11.
    EL Bouny L, Khalil M, Adib A (2017) ECG Signal Denoising Based on Ensemble EMD Thresholding and Higher Order Statistics. In: 3th IEEE International Conference on Advanced Technologies for Signal and Image Processing (ATSIP’2017), MoroccoGoogle Scholar
  12. 12.
    El Hamdouni N, Adib A, Larbi S, Turki M (2013) Blind digital audio watermarking scheme based on EMD and UISA techniques. J Multimed Tools Appl, Springer 64(3):809–829CrossRefGoogle Scholar
  13. 13.
    Ercelebi E (2004) Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Comput Biol Med 34(6):479–493CrossRefGoogle Scholar
  14. 14.
    Flandrin P, Goncalves P, Rilling G (2004) Detrending and denoising with empirical mode decompositions. In: European Signal Processing Conference (EUSIPCO), pp 1581–1584Google Scholar
  15. 15.
    George T, Xenos D (2011) Signal denoising using empirical mode decomposition and higher order statistics. Int J Signal Process Image Process Pattern Recogn 4 (2):91–106Google Scholar
  16. 16.
    He T, Clifford G, Tarassenko L (2006) Application of ICA in removing artefacts from the ECG. Neural Process Lett 15:105–116Google Scholar
  17. 17.
    Huang NE et al (1998) The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. In: Royal Society London, pp 903–995Google Scholar
  18. 18.
    Hong H, Liang M (2007) K-Hybrid: A Kurtosis-Based hybrid thresholding method for mechanical signal denoising. Trans ASME J Vib Acoust 129:458–470CrossRefGoogle Scholar
  19. 19.
    Kabir M d A, Shahnaz C (2012) Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control 7:481–489CrossRefGoogle Scholar
  20. 20.
    Khalil M, El Hamdouni N, Adib A (2012) Increasing the information Capacity and Improving the detection reliability in audio watermarking system. International Symposium on Communications, Control and Signal Processing (ISCCSP). Rome, ItalieGoogle Scholar
  21. 21.
    Khaldi K et al (2008) Speech Signal Noise Reduction by EMD. In: proceeding International Symposium on Communications, Control and Signal Processing (ISCCSP). Malta, pp 1155–1185Google Scholar
  22. 22.
    Khaldi K, Boudraa AO, Bouchikhi A, Turki M (2008) Speech enhancement via EMD. EURASIP. J Adv Signal Process 2008:1–8Google Scholar
  23. 23.
    Komaty A, Boudraa AO, Dare D (2012) EMD-Based filtering using the Hausdorff distance. In: Proceeding IEEE ISSPIT, pp 1–12Google Scholar
  24. 24.
    Komaty A, Boudraa AO, Augier B, Dare D (2014) EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs. IEEE Trans Instrum Meas 63(1):27–34CrossRefGoogle Scholar
  25. 25.
    Kopsinis Y, McLaughlin S (2009) Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Signal Process 57:1351–1362Google Scholar
  26. 26.
    Kopsinis Y, McLaughlin S (2008) Empirical mode decomposition based soft-thresholding. In: proceeding of 16th European Signal Processing Conference (EUSIPCO), pp 1–5Google Scholar
  27. 27.
    Maniruzzaman M, Kazi M, Billah S, Biswas U, Gain B (2012) Least-Mean-Square algorithm based adaptive filters for removing power line interference from ECG signal. In International conference on Informatics, Electronics & VisionGoogle Scholar
  28. 28.
    MIT-BIH-Arrhythmia Database. Available at:
  29. 29.
    Nason G, Silverman B (1995) The stationary wavelet transform and some statistical applications. Wavelets Stat Lect Notes Stat-Springer Verlag 103:281–299zbMATHGoogle Scholar
  30. 30.
    Nguyen P, Kim JM (2016) Adaptive ECG Denoising Using Genetic Algorithm-Based Thresholding and Ensemble Empirical Mode Decomposition. Inf. Sci.
  31. 31.
    Poornachandra S (2008) Wavelet-based denoising using subband dependent threshold for ECG signals. Digit Signal Process 18(1):49–55CrossRefGoogle Scholar
  32. 32.
    Rani S (2011) Comparative study of FIR and IIR filters for the removal of Baseline noises from ECG signal. Int J Comput Sci Inform Technol 2(3):1105–1108Google Scholar
  33. 33.
    Rilling G, Flandrin P, Goncalves P (2009) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(11):112–114Google Scholar
  34. 34.
    Sameni R et al (2007) A nonlinear bayesian filtering framework for ECG denoising. IEEE Trans. Biomed. Eng. 54:1–14Google Scholar
  35. 35.
    Sayadi O, Shamsollahi MB (2006) ECG denoising with adaptive bionic wavelet transform. In: 28 th IEEE EMBS Annual International Conference. New York City, USAGoogle Scholar
  36. 36.
    Sharma LN, Dandapat S, Mahanta A (2010) ECG Signal denoising using higher order statistics in wavelet subbands. Biomed Signal Process Control, Elsevier 5:214–222CrossRefGoogle Scholar
  37. 37.
    Suchetha M, Kumaravel N (2013) Empirical mode decomposition based filtering techniques for powerline interference reduction in electrocardiogram using various adaptive structures and subtraction methods. Biomed Signal Process Control 8:575–585CrossRefGoogle Scholar
  38. 38.
    Tompkins A (2007) EMD-Based 60 Hz Noise Filtering of The ECG. In: 29 th IEEE EMBS Annual International Conference, pp 1904–1907Google Scholar
  39. 39.
    Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: IEEE international conference on acoustic, Speech and Signal Processing (ICASSP), pp 4144–4147Google Scholar
  40. 40.
    Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. In: Proceeding of Royal Society London, pp 1597–1611Google Scholar
  41. 41.
    Wu Z, Huang NE (2009) Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41MathSciNetCrossRefGoogle Scholar
  42. 42.
    Yang G, Yuanyuan L, Yanyong W, Zhanlong Z (2015) EMD Interval thresholding denoising based on similarity measure to select relevant mode. Signal Process 109:95–109CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.LIM@II-FSTMMohammediaMorocco

Personalised recommendations