Skip to main content

Advertisement

SpringerLink
Log in

ECG signal denoising via empirical wavelet transform

  • Technical Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

This paper presents new methods for baseline wander correction and powerline interference reduction in electrocardiogram (ECG) signals using empirical wavelet transform (EWT). During data acquisition of ECG signal, various noise sources such as powerline interference, baseline wander and muscle artifacts contaminate the information bearing ECG signal. For better analysis and interpretation, the ECG signal must be free of noise. In the present work, a new approach is used to filter baseline wander and power line interference from the ECG signal. The technique utilized is the empirical wavelet transform, which is a new method used to compute the building modes of a given signal. Its performance as a filter is compared to the standard linear filters and empirical mode decomposition.The results show that EWT delivers a better performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010

    Article  Google Scholar 

  2. Huang NE, Shen Z, Long S, Wu MC, Shih HH, Zheng Q, Yen N-C (1998) The empirical mode composition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995

    Article  Google Scholar 

  3. Flandrin P, Rilling G, Gonçalvés P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(2):112–114

    Article  Google Scholar 

  4. Glover JR Jr, McCool JM, Kaunitz J, Williams CS, Hearn RH, Zeidler JR, Dong JE, Goodlin RC (1975) Adaptive noise canceling: principles and applications. Proc IEEE 63:1692–1716

    Article  Google Scholar 

  5. Agrawal S, Gupta A (2013) Fractal and EMD based removal of baseline wander and powerline interference from ECG signals. Comput Biol Med 43:1889–1899

    Article  PubMed  Google Scholar 

  6. 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 8:575–585

    Article  Google Scholar 

  7. Van Alste JA, Schilder TS (1985) Removal of baseline wander and power line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans Biomed Eng 32:1052–60

    Article  PubMed  Google Scholar 

  8. Thakor NV, Zhu YS (1991) Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng 38:785–94

    Article  CAS  PubMed  Google Scholar 

  9. Leski JM, Henzel N (2004) ECG baseline wander and power line interference reduction using nonlinear filter bank. Signal Process 35(4):781–793

    Google Scholar 

  10. Hamilton PS (1996) A comparison of adaptive and non adaptive filters for reduction of power line interference in the ECG. IEEE Trans Biomed Eng 43(1):105–109

    Article  CAS  PubMed  Google Scholar 

  11. Boudraa A-O, Cexus J-C (2007) EMD-based signal filtering. IEEE Trans Instrum Meas 56:2196–2202

    Article  Google Scholar 

  12. Hamilton PS (1996) A comparison of adaptive and non adaptive filters for reduction of powerline interference in the ECG. IEEE Trans Biomed Eng 43(1):105–109

    Article  CAS  PubMed  Google Scholar 

  13. Kumaravel N, Nithiyanandam N (1998) Genetic-algorithm cancellation of sinusoidal powerline interference in electrocardiograms. Med Biol Eng Comput 36:191–196

    Article  CAS  PubMed  Google Scholar 

  14. Huhta JC, Webster JG (1973) 60-Hz interference in electrocardiography. IEEE Trans Biomed Eng 20:91–101

    Article  CAS  PubMed  Google Scholar 

  15. Ziarani AK, Konrad A (2002) A nonlinear adaptive method of elimination of power line interference in ECG signals. IEEE Trans Biomed Eng 49:540–7

    Article  PubMed  Google Scholar 

  16. Weiting Y, Runjing Z (2007) An improved self-adaptive filter based on LMS algorithm for filtering 50 Hz interference in ECG signals. In: Proceedings of 8th international conference on electronic measurement instruments, vol 3, pp 874–878

  17. Mitov IP (2004) A method for reduction of power line interference in the ECG. Med Eng Phys 26:879–887

    Article  CAS  PubMed  Google Scholar 

  18. Zhidong Z, Juan L (2010) Baseline wander removal of ECG signals using empirical mode decomposition and adaptive filter. In: Proceedings of international conference on bioinformatics and biomedical engineering, pp 1–3

  19. Hargittai S (2008) Efficient and fast ECG baseline wander reduction without distortion of important clinical information. Comput Cardiol 35 841–844

    Google Scholar 

  20. Patil PB, Chavan MS (2012) A wavelet based method for denoising of biomedical signal. In: Proceedings of international conference on pattern recognition, informatics and medical engineering, pp. 278–283

  21. Jing-tian T, Qing Z, Yan T, Bin L, Xiao-kai Z (2007) Hilbert-Huang Transform for ECG De-Noising. In: Proceedings of 1st international conference on bioinformatics and biomedical engineering, pp. 664–667

  22. Weng B, Blanco-Velasco M, Barner KE (2006) Baseline wander correction in ECG by the empirical mode decomposition. In: Proceedings of IEEE 32nd annual northeast bio engineering conference, pp. 135–136

  23. Singh O, Sunkaria RK (2015) Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering. J Med Eng Technol 39(1): 60–68

    Article  PubMed  Google Scholar 

  24. Lahmiri S, Boukadoum M (2015) A weighted bio-signal denoising approach using empirical mode decomposition. Biomed Eng Lett 5(2):131–139

  25. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62:531–544

    Article  Google Scholar 

  26. Lahmiri S, Boukadoum M (2015) Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding. In: 2015 IEEE international symposium on circuits and systems (ISCAS), pp 806–809

  27. Lahmiri, Boukadoum M (2014) Biomedical image denoising using variational mode decomposition. In: Proccedings of 2014 IEEE biomedical circuits and systems conference (BioCAS), pp 340–343

  28. Lahmiri S (2014) Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. IET Healthc Technol Lett 1(3):104–109

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Dr B. R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144 011, India

    Omkar Singh & Ramesh Kumar Sunkaria

Authors
  1. Omkar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramesh Kumar Sunkaria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omkar Singh.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors. Although the study is related to ECG signal analysis, we have used the online database from physionet for ECG recordings and no self recorded ECG was utilized in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, O., Sunkaria, R.K. ECG signal denoising via empirical wavelet transform. Australas Phys Eng Sci Med 40, 219–229 (2017). https://doi.org/10.1007/s13246-016-0510-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-016-0510-6

Keywords

Search

Navigation