Advertisement

Empirical Mode Decomposition in ECG Signal De-noising

  • Zoltán Germán-SallóEmail author
  • Márta Germán-Salló
  • Horaţiu-Ştefan Grif
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 71)

Abstract

Empirical Mode Decomposition (EMD) is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the processed signal into components without using any basis functions. This is a data driven representation and provides intrinsic mode functions (IMFs) as components. These are obtained through a so-called sifting process. This study presents an EMD decomposition-based filtering procedure applied to ECG signals (from specific databases), the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Keywords

Empirical mode decomposition Signal processing Denoising 

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for non-linear and non stationary time series analysis. Proc. Roy. Soc. Lond. A 454, 903–995 (1998)CrossRefGoogle Scholar
  2. 2.
    Rilling, G., Flandrin, P., Gonalves, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I), June 2003Google Scholar
  3. 3.
    Rilling, G., Flandrin, P., Gonalves, P., Lilly, J.M.: Bivariate empirical mode decomposition. Sig. Proc. Lett. (submitted)Google Scholar
  4. 4.
    Huang, N.E., et al.: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc. Roy. Soc. Lond. A 459, 2317–2345 (2003)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Deering, R., Kaiser, J.F.: The use of a masking signal to improve empirical mode decomposition. In: ICASSP (2005). Smith, J., Jones, M. Jr., Houghton, L., et al.: Future of health insurance. N. Engl. J. Med. 965, 325–329 (1999)Google Scholar
  6. 6.
    Wu, Z., Huang, N. E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A. 460(2046), 1597–1611 (2003)Google Scholar
  7. 7.
    Flandrin, P., Goncalves, P., Rilling, G.: Detrending and denoising with empirical mode decomposition. In: Proceedings of XII EUSIPCO 2004, Vienna, Austria, Sept 2004Google Scholar
  8. 8.
    Tang, G., Qin, A.: ECG denoising based on empirical mode decomposition. In: 9th International Conference for Young Computer Scientists, pp. 903–906 (2008)Google Scholar
  9. 9.
    Kopsinis, Y., McLaughlin, S.: Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Sig. Process. 57, 1351–1362 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Benitez, D., Gaydecki, P.A., Zaidi, A., Fitzpatrick, A.P.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31(5), 399–406 (2001)CrossRefGoogle Scholar
  11. 11.
    Tang, G., Qin, A.: ECG de-noising based on empirical mode decomposition. In: 9th International Conference for Young Computer Scientists, pp. 903–906, Feb 2008Google Scholar
  12. 12.
    Boudraa, A.O., Cexus, J.C.: EMD-based signal filtering. IEEE Trans. Instrum. Meas. 56(6), 2196–2202 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zoltán Germán-Salló
    • 1
    Email author
  • Márta Germán-Salló
    • 2
  • Horaţiu-Ştefan Grif
    • 1
  1. 1.Faculty of EngineeringUniversity of Medicine, Pharmacy, Sciences and Technology of Targu-MuresTirgu-MuresRomania
  2. 2.Faculty of MedicineUniversity of Medicine, Pharmacy, Sciences and Technology of Targu-MuresTirgu-MuresRomania

Personalised recommendations