access icon openaccess Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains

Hybrid denoising models based on combining empirical mode decomposition (EMD) and discrete wavelet transform (DWT) were found to be effective in removing additive Gaussian noise from electrocardiogram (ECG) signals. Recently, variational mode decomposition (VMD) has been proposed as a multiresolution technique that overcomes some of the limits of the EMD. Two ECG denoising approaches are compared. The first is based on denoising in the EMD domain by DWT thresholding, whereas the second is based on noise reduction in the VMD domain by DWT thresholding. Using signal-to-noise ratio and mean of squared errors as performance measures, simulation results show that the VMD-DWT approach outperforms the conventional EMD–DWT. In addition, a non-local means approach used as a reference technique provides better results than the VMD-DWT approach.

Inspec keywords: AWGN; medical signal processing; signal denoising; discrete wavelet transforms; electroencephalography

Other keywords: additive Gaussian noise; in empirical mode decomposition domains; DWT thresholding; hybrid denoising models; electrocardiogram signal denoising; wavelet thresholding; variational mode decomposition domains; ECG signals; discrete wavelet transform

Subjects: Digital signal processing; Biology and medical computing; Electrodiagnostics and other electrical measurement techniques; Bioelectric signals; Electrical activity in neurophysiological processes; Signal processing and detection

References

    1. 1)
      • 17. Yan, Y., Zhanzhong, C.: ‘Noise and zero excursion elimination of electrostatic detection signals based on EMD and wavelet transform’. IEEE Int. Congress on Image and Signal Processing, 2009, pp. 15.
    2. 2)
      • 16. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode composition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’. Proc. of the Royal Society London, 1998, Vol. A 454, pp. 903995.
    3. 3)
    4. 4)
    5. 5)
      • 25. Kabir, M.A., Shahnaz, C.: ‘Comparison of ECG signal denoising algorithms in EMD and wavelet domains’, IJRRAS, 2012, 11, pp. 499516..
    6. 6)
      • 11. Li, N., Li, P.: ‘An improved algorithm based on EMD-wavelet for ECG signal de-noising’. Proc. Int. Joint Conf. on Computational Sciences and Optimization, 2009, pp. 825827.
    7. 7)
      • 27. http://www.physionet.org/physiobank/database/mitdb/.
    8. 8)
      • 15. Clifford, G.D., Azuaje, F., McSharry, P.E.: ‘Advanced methods and tools for ECG data analysis’ (Artech House, Boston/London, 2006).
    9. 9)
    10. 10)
      • 2. Leski, J.M., Henzel, N.: ‘ECG baseline wander and power line interference reduction using nonlinear filter bank’, Signal Process., 2004, 35, (4), pp. 781793.
    11. 11)
    12. 12)
      • 4. He, T., Clifford, G., Tarassenko, L.: ‘Application of ICA in removing artefacts from the ECG’. Neural Processing Letters, 2006, pp. 105116.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 18. Sun, M., Shen, Y., Zhang, W.: ‘A wavelet threshold denoising method for ultrasonic signal based on EMD and correlation coefficient analysis’. Proc. IEEE Third Int. Congress on Image and Signal Processing, 2010, pp. 39923996.
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • 26. Daubechies, I.: ‘Ten lectures on wavelets’ (Society of Industrial and Applied Mathematics (SIAM), Philadelphia, Pennsylvania, 1992).
    25. 25)
    26. 26)
    27. 27)
    28. 28)
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