Empirical Mode Decomposition Based Denoising by Customized Thresholding
This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).
[1] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shin, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, ”Proceedings of the Royal Society ofLondon,454:903–995, 1998.
[2] A. O. Boudraa, J. C. Cexus, Z. Saidi, “EMD-based signal noise reduction, Int. J. Signal Process. 1(1), 33–37, 2004.
[3] A. O. Boudraa, J. C. Cexus, “Denoising via empirical mode decomposition”, in Proceedings of the IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP ’06), p. 4, Marrakech, Morocco, March 2006.
[4] P. Flandrin, G. Rilling, P. Goncalces, “EMD equivalent filter banks, from interpretations to application,” in Hilbert-Huang Transform and its Application, N. E. Huang and S. Shen, Eds., 1st ed. Singapore: World Scientific, 2005.
[5] A. O. Boudraa, J. C. Cexus, “EMD-Based Signal Filtering”, IEEE Transactions on Instrumentation and Measurement, Vol. 56, NO. 6, pp. 2196-2202, 2007.
[6] Y. Kopsinis, S. Mclanglin, “Development of EMD-based denoising methods inspired by wavelet thresholding“, IEEE Trans. Signal Process. 57 (4) 1351–1362, 2009.
[7] Y. Kopsinis, S. McLaughlin, “Empirical Mode Decomposition Based Soft-Thresholding,” in Proc. 16th Eur. Signal Process. Conf. (EUSIPCO), Lausanne, Switzerland, Aug. 25–29, 2008.
[8] G. S. Tsolis and T. D. Xenos, “Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics” International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 2, pp.91-106, 2011.
[9] G. Yang, Y. Liu, Y. Wang, Z. Zhu, “EMD interval thresholding denoising based on similarity measure to select relevant modes,” Signal Processing109,95–109, 2015.
[10] Byung-Jun Yoon, P. P. Vaidyajnathan, “Wavelet-based denoising by customized thresholding”, ICASP-2004.
[11] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage, ”Biometrika, vol. 81, no. 3, pp. 425 – 455, Aug. 1994.
[12] Donoho DL, “De-noising by soft-thresholding,” IEEE Trans Inform Theory, Vol.14, No.3, pp. 612-627, 1995.
[13] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, Vol. 101, N° 23, pp. 215–220, 2000.
[1] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shin, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, ”Proceedings of the Royal Society ofLondon,454:903–995, 1998.
[2] A. O. Boudraa, J. C. Cexus, Z. Saidi, “EMD-based signal noise reduction, Int. J. Signal Process. 1(1), 33–37, 2004.
[3] A. O. Boudraa, J. C. Cexus, “Denoising via empirical mode decomposition”, in Proceedings of the IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP ’06), p. 4, Marrakech, Morocco, March 2006.
[4] P. Flandrin, G. Rilling, P. Goncalces, “EMD equivalent filter banks, from interpretations to application,” in Hilbert-Huang Transform and its Application, N. E. Huang and S. Shen, Eds., 1st ed. Singapore: World Scientific, 2005.
[5] A. O. Boudraa, J. C. Cexus, “EMD-Based Signal Filtering”, IEEE Transactions on Instrumentation and Measurement, Vol. 56, NO. 6, pp. 2196-2202, 2007.
[6] Y. Kopsinis, S. Mclanglin, “Development of EMD-based denoising methods inspired by wavelet thresholding“, IEEE Trans. Signal Process. 57 (4) 1351–1362, 2009.
[7] Y. Kopsinis, S. McLaughlin, “Empirical Mode Decomposition Based Soft-Thresholding,” in Proc. 16th Eur. Signal Process. Conf. (EUSIPCO), Lausanne, Switzerland, Aug. 25–29, 2008.
[8] G. S. Tsolis and T. D. Xenos, “Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics” International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 2, pp.91-106, 2011.
[9] G. Yang, Y. Liu, Y. Wang, Z. Zhu, “EMD interval thresholding denoising based on similarity measure to select relevant modes,” Signal Processing109,95–109, 2015.
[10] Byung-Jun Yoon, P. P. Vaidyajnathan, “Wavelet-based denoising by customized thresholding”, ICASP-2004.
[11] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage, ”Biometrika, vol. 81, no. 3, pp. 425 – 455, Aug. 1994.
[12] Donoho DL, “De-noising by soft-thresholding,” IEEE Trans Inform Theory, Vol.14, No.3, pp. 612-627, 1995.
[13] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, Vol. 101, N° 23, pp. 215–220, 2000.
@article{"International Journal of Electrical, Electronic and Communication Sciences:75293", author = "Wahiba Mohguen and Raïs El’hadi Bekka", title = "Empirical Mode Decomposition Based Denoising by Customized Thresholding", abstract = "This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).
", keywords = "Customized thresholding, ECG signal, EMD, hard thresholding, Soft-thresholding.", volume = "11", number = "5", pages = "519-6", }
