Skip to main content
SpringerLink
Log in

Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance, based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus, this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number for EEMD was also examined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  1. Sayadi, O., Shamsollahi, M. B. (2008). Model-based fiducial points extraction for baseline wandered electrocardiograms. IEEE Transactions on Biomedical Engineering, 55(1), 347–351.

    Article  Google Scholar 

  2. Salisbury, J. I., Sun, Y. (2007). Rapid screening test for sleep apnea using a nonlinear and nonstationary signal processing technique. Medical Engineering and Physics, 29(3), 336–343.

    Article  Google Scholar 

  3. Stegle, O., Fallert, S. V., MacKay, D. J., Brage, S. (2008). Gaussian process robust regression for noisy heart rate data. IEEE Transactions on Biomedical Engineering, 55(9), 2143–2151.

    Article  Google Scholar 

  4. Poornachandra, S., Kumaravel, N. (2003). A wavelet coefficient smoothened RLS adaptive denoising model for ECG. Biomedical Sciences Instrumentation, 39, 154–157.

    Google Scholar 

  5. Erçelebi, E. (2004). Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Computers in Biology and Medicine, 34(6), 479–93.

    Article  Google Scholar 

  6. Wu, Y., Rangayyan, R. M., Zhou, Y., Ng, S. C. (2009). Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Medical Engineering and Physics, 31(1), 17–26.

    Article  Google Scholar 

  7. Milanesi, M., Martini, N., Vanello, N., Positano, V., Santarelli, M. F., Landini, L. (2008). Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals. Medical and Biological Engineering and Computing, 46(3), 251–261.

    Article  Google Scholar 

  8. Wu, Z., Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society A, 460, 1597–1611.

    Article  MATH  Google Scholar 

  9. Huang, Z., Chen, Y., Pan, M. (2007). Time-frequency characterization of atrial fibrillation from surface ECG based on Hilbert–Huang transform. Journal of Medical Engineering and Technology, 31(5), 381–389.

    Article  Google Scholar 

  10. Blanco-Velasco, M., Weng, B., Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38(1), 1–13.

    Article  Google Scholar 

  11. Nimunkar, A. J., Tompkins, W. J. (2007). EMD-based 60-Hz noise filtering of the ECG. Conference Proceedings—IEEE Engineering in Medicine and Biology Society, 1904–1907.

  12. Wu, Z., Huang, N. E. (2009). Ensemble empirical. Mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1–41.

    Article  Google Scholar 

  13. Vaseghi, S. V. (2006). Advanced digital signal processing and noise reduction. 3rd. Least Square Error Filters (pp.166–182). England: John Wiley & Sons.

  14. Paul, J. S., Reddy, M. R., Kumar, V. J. (1998). Data processing of stress ECGs using discrete cosine transform. Computers in Biology and Medicine, 28(6), 639–658.

    Article  Google Scholar 

  15. Lander, P., Berbari, E. J. (1997). Time-frequency plane Wiener filtering of the high-resolution ECG: background and time-frequency representations. IEEE Transactions on Biomedical Engineering, 44(4), 247–255.

    Article  Google Scholar 

  16. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), E 215–E 220.

    Google Scholar 

  17. Flandrin, P., Rilling, G., Goncalves, P. (2004). Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters, 11(2), 112–114.

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by NSC (Taiwan, grant number 98-2221-E-468-009-).

Author information

Authors and Affiliations

  1. Asia University, Taichung, Taiwan, Republic of China

    Kang-Ming Chang

  2. Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, Republic of China

    Shing-Hong Liu

Authors
  1. Kang-Ming Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shing-Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kang-Ming Chang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chang, KM., Liu, SH. Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition. J Sign Process Syst 64, 249–264 (2011). https://doi.org/10.1007/s11265-009-0447-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-009-0447-z

Keywords

Search

Navigation