HE Hong, TAN Yonghong. A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal[J]. Chinese Journal of Electronics, 2018, 27(3): 507-513. doi: 10.1049/cje.2018.02.006
Citation: HE Hong, TAN Yonghong. A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal[J]. Chinese Journal of Electronics, 2018, 27(3): 507-513. doi: 10.1049/cje.2018.02.006

A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal

doi: 10.1049/cje.2018.02.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61571302, No.61371145, No.61671303), Industry-education-research Project of Shanghai Normal University (No.DCL201704), and the Project of the Science and Technology Commission of Shanghai (No.18070503000).
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  • Corresponding author: TAN Yonghong (corresponding author) received Ph.D. degree in electrical engineering from University of Ghent, Ghent, Belgium, in 1996. He is currently a professor at the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. His research interests include modeling and control of nonlinear systems, mechatronics, intelligent control, and signal processing. (Email:tany@shnu.edu.cn)
  • Received Date: 2017-05-17
  • Rev Recd Date: 2017-07-26
  • Publish Date: 2018-05-10
  • On the basis of wavelet theory, a novel Adaptive wavelet thresholding method (AWT) is proposed for the ECG signal enhancement. The best base wavelet for ECG signal filtering can be automatically obtained through the cross correlation coefficient and the energy to entropy ratio. The variable universal threshold (VarUniversal) is applied to different decomposition level so as to suppress diverse noise. To achieve a smooth cut-off transition, an identical correlation shrinkage function (IcoShrinkage) is also adopted in the AWT according to its correlation coefficients with the hard thresholding and the soft thresholding. The performance of AWT is compared with four threshold approaches and six shrinkage functions, respectively, on the basis of 150 practical ECG signals of 30 subjects. The filtering results reveal that the AWT can adaptively choose an optimal base wavelet for a specific ECG signal. With the VarUniversal threshold and IcoShrinkage, the AWT obtains the better filtering results than the other compared methods.
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  • Luo S. and Paul Johnston, "A review of electrocardiogram filtering", Journal of Electrocardiology, Vol.43, No.6, pp.486-496, 2010.
    Gacek, A. and Pedrycz, W., ECG Signal Processing, Classification and Interpretation, Springer-Verlag, London, UK, pp.21-46, 2012.
    Paul S Addison, "Wavelet transforms and the ECG:A review", Physiological Measurement, Vol.26, No.5, pp.155-199, 2005.
    Nagendra H., S. Mukherjee and Vinodkumar, "Application of wavelet techniques in ECG signal processing:An Overview", International Journal of Engineering Science and Technology (IJEST), Vol.3, No.10, pp.7432-7443, 2011.
    Stéphane Mallat, A Wavelet Tour of Signal Processing:The Sparse Way, 3rd ed., Academic Press, Burlington, USA, pp.535-595, 2009
    J. Mateo, A.M. Torres, C. Soria, et al., "A method for removing noise from continuous brain signal recordings", Computers & Electrical Engineering, Vol.39, No.5, pp.1561-1570, 2013.
    Donoho D.L. and Johnstone I.M., "Ideal spatial adaptation via wavelet shrinkage", Biometrika, Vol.81, No.3, pp.425-455, 1994.
    Donoho D.L., "Denoising by soft-thresholding", IEEE Trans. Inform. Theory, Vol.41, No.3, pp.613-627, 1995.
    Coifman, R.R. and Donoho, D.L., "Translation-invariant denoising, in:Wavelets and Statistics", Springer Lecture Notes in Statistics, Vol.103, No.2, pp.125-150, 1995.
    Poornachandra S. and Kumaravel N., "A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function", Digital Signal Processing, Vol.18, No.2, pp.116-126, 2008.
    Bruce A.G. and Gao H.Y., "'Wave Shrink with firm shrinkage", Statistica Sinica Research Report, Vol.7, No.4, pp. 855-874, 1997.
    Gao H.Y., "Wavelet shrinkage denoising using the non-negative garrote", Journal of Computational and Graphical Statistics, Vol.7, No. 4, pp. 469-488, 1998.
    Poornachandra S. and Kumaravel N., " Hyper-trim shrinkage for denoising of ECG signal", Digital Signal Processing, Vol.15, No.3, pp. 317-327, 2005.
    Ghanbari Y. and Mohammad Reza Karami-Mollaei, " A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets", Speech Communication, Vol.48, No.8, pp.927-940, 2006.
    Cui H.M., Zhao R.M. and Hou Y.L., "Improved threshold denoising method based on wavelet transform", Physics Procedia, Vol.33, No.1, pp.1354-1359, 2012.
    Awa M.A., Mosrafa S.S., Ahmad M., et al., "An adaptive level dependent wavelet thresholding for ECG denoising", Biocybernetics and Biomedical engineering, Vol.34, No.4, pp.238-249, 2014.
    El-Sayed A and El-Dahshan, "Genetic algorithm and wavelet hybrid scheme for ECG signal denoising", Telecommun Syst., Vol.46, No.3, pp.209-215, 2011.
    Han X.H. and Chang X.M., "An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms", Information Sciences, Vol.218, No.1, pp.103-118, 2013.
    Somnath Mukhopadhyay and J.K. Mandal, " Wavelet based denoising of medical images using sub-band adaptive thresholding through genetic algorithm", Procedia Technology, Vol.10, No.2, pp.680-689, 2013.
    Liua C.C., Sunb T.Y., Tsaib S.J., et al., "Heuristic wavelet shrinkage for denoising", Applied Soft Computing, Vol.11, No.1, pp. 256-264, 2011.
    Rainer von Sachs and Brenda MacGibbon, "Nonparametric curve estimation by wavelet thresholding with locally stationary errors", Scandinavian Journal of Statistics, Vol.27, No.3, pp.475-499, 2000.
    Gao R.X. and Yan R., Wavelets, Theory and Applications for Manufacturing, Springer Science Business Media, LLC, New York, USA, pp.165-187, 2011
    Brij N. Singh and Arvind K. Tiwari, "Optimal selection of wavelet basis function applied to ECG Signal denoising", Digital Signal Processing, Vol.16, No.3, pp.275-287, 2006.
    Adelino R. Ferreira da Silva, "Bayesian wavelet denoising and evolutionary calibration", Digital Signal Processing, Vol.14, No.6, pp.566-589, 2004.
    Donoho D.L. and Johnstone I.M., "Adapting to unknown smoothness via wavelet shrinkage", Journal of the American Statistical Association, Vol. 90, No.432, pp.1200-1224, 1995.
    J.J. Goldberger and J. Ng (eds.), Practical Signal and Image Processing in Clinical Cardiology, Springer-Verlag London, UK, pp.113-130, 2010
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