Log in

Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering

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

Abstract

This paper presents a novel electrocardiogram (ECG) denoising approach based on variational mode decomposition (VMD). This work also incorporates the efficacy of the non-local means (NLM) estimation and the discrete wavelet transform (DWT) filtering technique. Current ECG denoising methods fail to remove noise from the entire frequency range of the ECG signal. To achieve the effective ECG denoising goal, the noisy ECG signal is decomposed into narrow-band variational mode functions (VMFs) using VMD method. The idea is to filter out noise from these narrow-band VMFs. To achieve that, the center frequency information associated with each VMFs is used to exclusively divide them into lower- and higher-frequency signal groups. The higher frequency VMFs were filtered out using DWT-thresholding technique. The lower frequency VMFs are denoised through NLM estimation technique. The non-recursive nature of VMD enables the parallel processing of NLM estimation and DWT filtering. The traditional DWT-based approaches need large decomposition levels to filter low frequency noises and at the same time NLM technique suffers from the rare-patch effect in high-frequency region. On the contrary, in the proposed framework both NLM and DWT approaches complement each other to overcome their individual ill-effects. The signal reconstruction is performed using the denoised high frequency and low frequency VMFs. The simulation performed on the MIT-BIH Arrhythmia database shows that the proposed method outperforms the existing state-of-the-art ECG denoising techniques.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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 includes VAT (Germany)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Bruce EN (2001) Biomedical signal processing and signal modeling. Wiley, New York

    Google Scholar 

  2. Clifford GD, Azuaje F, McSharry P (2006) Advanced methods and tools for ECG data analysis. Artech house, London

    Google Scholar 

  3. Dixon AMR, Allstot EG, Gangopadhyay D, Allstot DJ (2012) Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans Biomed Circuits Syst 6(2):156–166

    Article  Google Scholar 

  4. Pandit D, Zhang L, Liu C, Aslam N, Chattopadhyay S, Lim CP (2017) Noise reduction in ECG signals using wavelet transform and dynamic thresholding. In: Bhatti A, Lee KH, Garmestani H, Lim CP (eds) Emerging trends in neuro engineering and neural computation. Springer, Singapore, pp 193–206

    Chapter  Google Scholar 

  5. Abbaspour S, Fallah A, Lindén M, Gholamhosseini H (2016) A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet. J Electromyogr Kinesiol 26:52–59

    Article  Google Scholar 

  6. Smital L, Vítek M, Kozumplík J, Provaznik I (2013) Adaptive wavelet wiener filtering of ECG signals. IEEE Trans Biomed Eng 60:437–445

    Article  Google Scholar 

  7. Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 16(3):275–287

    Article  Google Scholar 

  8. Sharma LN, Dandapat S, Mahanta A (2010) ECG signal denoising using higher order statistics in Wavelet subbands. Biomed Signal Process Control 5(3):214–222

    Article  Google Scholar 

  9. Alfaouri M, Daqrouq K (2008) ECG signal denoising by wavelet transform thresholding. Am J Appl Sci 5:276–281

    Article  Google Scholar 

  10. Sayadi O, Shamsollahi MB (2007) Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. EURASIP J Adv Signal Process 1:1–11

    Google Scholar 

  11. B’charri OEl, Latif R, Elmansouri K, Abenaou A, Jenkal W (2017) ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomed Eng Online 16(1):26–43

    Article  Google Scholar 

  12. Han G, Lin B, Xu Z (2017) Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview. J Instrum 12(3):3–10

    Article  Google Scholar 

  13. Jain S, Bajaj V, Kumar A (2017) Riemann Liouvelle fractional integral based empirical mode decomposition for ECG denoising. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2017.2753321

    Article  Google Scholar 

  14. Pal S, Mitra M (2012) Empirical mode decomposition based ECG enhancement and QRS detection. Comput Biol Med 42(1):83–92

    Article  Google Scholar 

  15. Boudraa AO, Cexus JC (2007) EMD-based signal filtering. IEEE Trans Instrum Meas 56:2196–2202

    Article  Google Scholar 

  16. Chacko A, Ari S (2012) Denoising of ECG signals using empirical mode decomposition based technique. In: 2012 international conference on advances in engineering, science and management. IEEE, pp 6–9

  17. Tracey BH, Miller EL (2012) Nonlocal means denoising of ECG signals. IEEE Trans Biomed Eng 59:2383–2386

    Article  Google Scholar 

  18. Agante PM, Marques De Sá JP (1999) ECG noise filtering using wavelets with soft-thresholding methods. In: Proceedings of computers in cardiology, vol 26. IEEE, pp 535-538

  19. Addison PS (2005) Wavelet transforms and the ECG: a review. Physiol Meas 26(5):155–199

    Article  Google Scholar 

  20. Singh P, Pradhan G, Shahnawazuddin S (2017) Denoising of ECG signal by non-local estimation of approximation coefficients in DWT. Biocybern Biomed Eng 37(3):599–610

    Article  Google Scholar 

  21. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 60–65

  22. Singh P, Shahnawazuddin S, Pradhan G (2017) Significance of modified empirical mode decomposition for ECG denoising. In: 39th annual international conference of the IEEE engineering in medicine and biology society, vol 39, pp 2956–2959

  23. Naik GR, Selvan SE, Nguyen HT (2016) Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorder. IEEE Trans Neural Syst Rehabil Eng 24:734–743

    Article  Google Scholar 

  24. Vazquez RR, Perez HV, Ranta R, Dorr VL et al (2012) Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomed Signal Process Control 7:389–400

    Article  Google Scholar 

  25. Guo Y, Huang S, Li Y, Naik GR (2012) Edge effect elimination in single-mixture blind source separation. Circuits Syst Signal Process 32:2317–2334

    Article  Google Scholar 

  26. Guo Y, Naik GR, Nguyen H (2013) Single channel blind source seperation based local mean decomposition for Biomedical applications. In: 35th annual international conference of the IEEE engineering in medicine and biology society, vol 35, pp 6812-6815

  27. Rashid A, Qureshi IM, Saleem A (2011) Electrocardiogram signal processing for baseline noise removal using blind source separation techniques: a comparative analysis. In: Proceedings of the 2011 international conference on machine learning and cybernetics, IEEE, pp 1756–1761

  28. Barhatte AS, Ghongade R, Tekale SV (2016) Noise analysis of ECG signal using fast ICA. In: 2016 Conference on advances in signal processing, IEEE, pp 118–122

  29. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    Article  Google Scholar 

  30. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC (2000) PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101:215–220

    Google Scholar 

  31. Seljuq U, Himayun F, Rasheed H (2014) Selection of an optimal mother wavelet basis function for ECG signal denoising. In: 17th IEEE international multi topic conference, vol 17, IEEE, pp 26–30

  32. Kabir MA, Shahnaz C (2012) Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control 7:481–489

    Article  Google Scholar 

  33. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  34. Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530

    Article  Google Scholar 

  35. Deledalle CA, Duval V, Salmon J (2012) Non-local methods with shape-adaptive patches. J Math Imaging Vis 43:103–120

    Article  Google Scholar 

  36. Bertsekas DP (1976) Multiplier methods: a survey. Automatica 12(2):133–145

    Article  Google Scholar 

  37. Nocedal J, Wright SJ (2006) Numerical optimization. Springer, New York

    Google Scholar 

  38. Bertsekas DP (1982) Constrained optimization and Lagrange multiplier methods. Academic Press, Cambridge

    Google Scholar 

  39. Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41:613–627

    Article  Google Scholar 

  40. Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50

    Article  CAS  Google Scholar 

  41. Donoho DL, Johnstone IM (1998) Minimax estimation via wavelet shrinkage. Ann Stat 26:879–921

    Article  Google Scholar 

  42. Stein CM (1981) Estimation of the mean of a multivariate normal distribution. Ann Stat 9(6):1135–1151

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pratik Singh.

Ethics declarations

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Ethical approval

This article does not contain any studies with human or animal subjects performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, P., Pradhan, G. Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering. Australas Phys Eng Sci Med 41, 891–904 (2018). https://doi.org/10.1007/s13246-018-0685-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-018-0685-0

Keywords

Navigation