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Heat Equation-Based ECG Signal Denoising in The Presence of White, Colored, and Muscle Artifact Noises

  • Prateep UpadhyayEmail author
  • S. K. Upadhyay
  • K. K. Shukla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)

Abstract

In this paper, we have derived a novel solution of heat equation which comes out in the form of wavelet transformation and we have applied this solution to the signals of the MIT-BIH normal sinus rhythm database from PhysioBank in the presence of white Gaussian noise, colored noises, and muscle artifact (MA) noise respectively. It was found that the proposed method outperforms the recently reported method by Hamed Danandeh Hesar et al. in their specified SNR range of noises.

Keywords

Heat Equation Wavelets Multiresolution analysis ECG signals Denoising 

References

  1. 1.
    Kaergaard, K., Jensen, S. H., Puthusserypady, S.: A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising. Biomed. Signal Process. Control 25, 178–187 (2016)Google Scholar
  2. 2.
    Wang, J., Ye, Y., Gao, Y., Qian, S., Gao, X.: Fractional compound integral with application to ECG signal denoising. Circuits Syst. Signal Process. 34, 1915–1930 (2015)Google Scholar
  3. 3.
    Wang, Z., Wan, F., Wong, C. M., Zhang, L.: Adaptive Fourier decomposition based ECG denoising. Comput. Biol. Med. 7, 195–205 (2016)Google Scholar
  4. 4.
    Pal, S., Mitra, M.: Empirical mode decomposition based ECG enhancement and QRS detection. Comput. Biol. Med. 42(1), 83–92 (2012)Google Scholar
  5. 5.
    Gacek, P., Adam, W.: ECG Signal Processing, Classification and Interpretation a Comprehensive Framework of Computational Intelligence. Springer (2012)Google Scholar
  6. 6.
    Moody, G.B., Mark, R.G.: QRS morphology representation and noise estimation using the Karhunen-Loeve transform. Proc. Comput. Cardiol. 269–272 (1989)Google Scholar
  7. 7.
    Barros, A.K., Mansour, A., Ohnishi, N.: Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 22(1), 173–186 (1998)Google Scholar
  8. 8.
    He, T., Clifford, G., Tarassenko, L.: Application of independent component analysis in removing artefacts from the electrocardiogram. Neural Comput. Appl. 15(2) 105–116 (2006)Google Scholar
  9. 9.
    Clifford, G., Tarassenko, L., Townsend, N.: One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats. Electron. Lett. 37(18), 1126–1127 (2001)Google Scholar
  10. 10.
    Kestler, H., Haschka, M., Kratz, W., Schwenker, F., Palm, G., Hombach, V., Hoher, M.: Denoising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter. In: Proceedings, Computers in Cardiology, pp. 233–236 (1998)Google Scholar
  11. 11.
    Popescu, M., Cristea, P., Bezerianos, A.: High resolution ECG filtering using adaptive Bayesian wavelet shrinkage. In: Proceedings, Computers in Cardiology, pp. 401–404 (1998)Google Scholar
  12. 12.
    Agante, P.M., Sa, J.P.M.D.: ECG noise filtering using wavelets with soft thresholding methods. In: Proceedings, Computers in Cardiology, pp. 535–538 (1999)Google Scholar
  13. 13.
    Lander, P., Berbari, E.J.: Time frequency plane Wiener filtering of the high-resolution ECG: development and application. IEEE Trans. Biomed. Eng. 44(4), 256–265 (1997)Google Scholar
  14. 14.
    Thakor, N.V., Zhu, Y. S.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)Google Scholar
  15. 15.
    Laguna, P., Jane, R., Meste, O., Poon, P. W., Caminal, P., Rix, H., Thakor, N.V.: Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques. IEEE Trans. Biomed. Eng. 39(10), 1032–1044 (1992)Google Scholar
  16. 16.
    McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003)Google Scholar
  17. 17.
    Sameni, R., Shamsollahi, M.B., Jutten, C., Clifford, G.D.: A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans. Biomed. Eng. 54(12), 2172–2185 (2007)Google Scholar
  18. 18.
    Sayadi, O., Shamsollahi, M.B.: ECG denoising and compression using a modified extended Kalman filter structure. IEEE Trans. Biomed. Eng. 55(9), 2240–2248 (2008)Google Scholar
  19. 19.
    Sayadi, O., Shamsollahi, M.B.: A model-based Bayesian framework for ECG beat segmentation. Physiol. Meas. 30(3), 335–352 (2009)Google Scholar
  20. 20.
    Sayadi, O., Shamsollahi, M.B., Clifford, G.D.: Robust detection of premature ventricular contractions using a wave-based bayesian framework. IEEE Trans. Biomed. Eng. 57(2), 353–362 (2010)Google Scholar
  21. 21.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)Google Scholar
  22. 22.
    Lee, J., McManus, D.D., Bourrell, P., Sörnmo, L., Chon, K.H.: Atrial flutter and atrial tachycardia detection using Bayesian approach with high resolution time frequency spectrum from ECG recordings. Biomed. Signal Process. Control 8(6), 992–999 (2013)Google Scholar
  23. 23.
    Lin, C., Bugallo, M., Mailhes, C., Tourneret, J.Y.: ECG denoising using a dynamical model and a marginalized particle filter. In: Proceedings, 45th Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1679–1683 (2011)Google Scholar
  24. 24.
    Schon, T., Gustafsson, F., Nordlund, P.J.: Marginalized particle filters for mixed linear/nonlinear state-space models. IEEE Trans. Signal Process. 53(7), 2279–2289 (2005)Google Scholar
  25. 25.
    Hesar, H.D., Mohebbi, M.: ECG denoising using marginalized particle extended kalman filter with an automatic particle weighting strategy. IEEE J. Biomed. Health Inform. 21(3) (2017)Google Scholar
  26. 26.
    Singh, O., Sunkaria, R.K.: ECG signal denoising via empirical wavelet transform. Australas. Phys. Eng. Sci. Med. 40(1), 219–229 (2017)Google Scholar
  27. 27.
    Banerjee, S., Gupta, R., Mitra, M.: Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 45(3), 474–487 (2012)Google Scholar
  28. 28.
    Maniewski, R., Lewandowski, P., Nowinska, M., Mroczka, T.: Time-frequency methods for high-resolution ECG analysis. In: 18th Annual International Conference IEEE on Proceedings, Engineering in Medicine and Biology Society. Bridging Disciplines for Biomedicine (1996)Google Scholar
  29. 29.
    Kania, M., Fereniec, M., Maniewski, R.: Wavelet denoising for multi-lead high resolution ECG signals. Meas. Sci. Rev. 7(4) (2007)Google Scholar
  30. 30.
    Janusek, D., Kania, M., Zaczek, R., Fernandez, H.Z., Zbieć, A., Opolski1, G., Maniewski, R.: Application of wavelet based denoising for T-wave alternans analysis in high resolution ECG maps. Meas. Sci. Rev. 11(6) (2011)Google Scholar
  31. 31.
    Jenkal, W., Latif, R., Toumanari, A., Dliou, A., B’charri, O.E., Maoulainine, F.M.R.: An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern. Biomed. Eng. 36, 499–508 (2016)Google Scholar
  32. 32.
    Debnath, L.: Wavelet Transforms and Their applications, pp. 63–371. Birkhäuser, Boston (2002)Google Scholar
  33. 33.
    Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.M.: Wavelets and Their Applications, pp. 197–206. ISTEGoogle Scholar
  34. 34.
    The MIT-BIH Normal Sinus Rhythm Database, PhysioNet. http://www.physionet.org/physiobank/data-base/nstdb/
  35. 35.
    The MIT-BIH Noise Stress Test Database, PhysioNet. http://www.physionet.org/physiobank/data-base/nstdb/
  36. 36.
    Manikandan, M.S., Dandapat, S.: Multiscale entropy-based weighted distortion measure for ECG coding. IEEE Signal Process. Lett. 15, 829–832 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Prateep Upadhyay
    • 1
    Email author
  • S. K. Upadhyay
    • 2
  • K. K. Shukla
    • 3
  1. 1.DST-CIMS Banaras Hindu UniversityVaranasiIndia
  2. 2.Department of Mathematical SciencesIIT (B.H.U.), & DST-CIMS Banaras Hindu UniversityVaranasiIndia
  3. 3.Department of Computer Science and EngineeringIIT (B.H.U.) VaranasiVaranasiIndia

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