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Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain

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Abstract

In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application of the inverse of BWT (B W T −1) to the de-noise de-noised bionic wavelet coefficients. For evaluating this new proposed de-noising technique, we have compared it to a thresholding technique in the FWT_TI domain. Preliminary tests of the application of the two de-noising techniques were constructed on a number of ECG signals taken from MIT-BIH database. The obtained results from Signal to Noise Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed de-noising technique outperforms the second technique. We have also compared the proposed technique to the thresholding technique in the bionic wavelet domain and this comparison was performed by SNR improvement computing. The obtained results from this evaluation showed that the proposed technique also outperforms the de-noising technique based on bionic wavelet coefficients thresholding.

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References

  • Addison P S 2005 Wavelet transforms and the ECG a review. Institute of physics publishing, pp 155–199

  • Bahoura M and Rouat J 2006 Wavelet speech enhancement based on time–scale adaptation. Speech Commun. 48(12): 1620–1637

    Article  Google Scholar 

  • Berger J, Coifman R R and Goldberg M 1994 Removing noise from music using local trigonometric bases and wavelet packets. J. Audio Eng. Soc. 42

  • Chmelka L, Kozumplík J 2005 Wavelet-based wiener filter for electrocardiogram signal denoising. Comput. Cardiol. 32: 771–774

    Google Scholar 

  • Coifman R R and Donoho D L 1995 Translation-Invariant De-noising. In A. Antoniadis and G. Oppenheim (eds.), Wavelets and Statistics, pp 125-150, Springer-Verlag

  • Ephraim Y and Malah D 1984 Speech enhancement using a minimum mean square error short time spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Process. ASSP-32(6): 1109–1121

    Article  Google Scholar 

  • Gao W, Li H, Zhang Z and Wang T 2003 De-noising of ECG signal based on stationary wavelet transform. Acta Electr. Sin. 32: 238–240

    Google Scholar 

  • Han J Y, Lee S K and Park H B 2009 Denoising ECG using Translation Invariant Multiwavelet. Int. J. Electr. Comput. Syst. Eng. 3(3): 138–142

    Google Scholar 

  • Janusek D, Kania M, Zaczek1 R, Zavala-Fernandez H, Zbieć A, Opolski1 G and Maniewski R 2011 Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps. Measurement Science Review, 11(6): 181–184

    Article  Google Scholar 

  • Johnson M T, Yuan X and Ren Y 2007 Speech signal enhancement through adaptive wavelet thresholding. Sci. Direct. Speech Commun. 49: 123–133

    Article  Google Scholar 

  • Mallat S and Zhong S 1991 Charactrization of signal from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14: 701–732

    Google Scholar 

  • Mourad T, Lotfi S, Sabeur A and Adnane C 2010 Recurrent neural network and bionic wavelet transform for speech enhancement. Int. J. Signal Imaging Syst. Eng. 3(2): 136–144

    Article  Google Scholar 

  • Mourad T, Aouinet A, Riadh B and Adnane C 2012 ECG analysis based on wavelet transform. IJCSI Int. J. Comput. Sci. Issues 9(Issue 1, No. 3): 427–435

    Google Scholar 

  • Nikolaev N and Gotchev A 2000 ECG signal denoising using wavelet domain wiener filtering. Proceeding of the European Association for Signal and Image Processing (EURASIP)

  • Sahambi J S, Tandon S N and Bhatt R K P 1998 Wavelet based ST-segment analysis. Med. Biol. Comput. 36: 568–572

    Article  Google Scholar 

  • Saito N 1994 Feature Extraction using Local Discriminant Basis. Yale Dissertation December 1994

  • Sayadi O and Shamsollahi M B 2006 ECG Denoising with Adaptive Bionic Wavelet Transform. Proc. EMBS, pp. 6597–6600

  • Tasmaz H and Ercelebi E 2007 Speech enhancement based on undecimated wavelet packet-perceptual filterbanks and MMSE-STSA estimation in various noise environments. Digital Signal Processing, (p.16, in press, available online 12 October 2007).

  • Yao J and Zhang Y T 1999 Cochlear is an inhomogeneous, active and nonlinear model. Proc. IEEE EMBS/BMES, Atlanta, 1031

  • Yao J and Zhang Y T 2000 From Otoacoustic Emission Modeling to Bionic Wavelet Transform. Proceedings of the 22nd Annual EMBS International Conference, 314–316

  • Yao J, and Zhang Y T 2001 Bionic wavelet transform: a new time-frequency method based on an auditory model. IEEE Trans. Biomed. Eng. 48(8): 856–863

    Article  Google Scholar 

  • Yuan X 2003 Auditory Model-based Bionic Wavelet Transform for Speech Enhancement. M.Sc. Thesis, Marquette University, Speech and Signal Processing Lab Milwaukee, Wisconsin

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Authors and Affiliations

  1. High Institute of Applied Mathematics and Informatics of Kairouan, University of Kairouan, Kairouan, Tunisia

    MOURAD TALBI

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  1. MOURAD TALBI
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TALBI, M. Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain. Sadhana 39, 921–937 (2014). https://doi.org/10.1007/s12046-014-0247-4

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  • DOI: https://doi.org/10.1007/s12046-014-0247-4

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