Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414


Select areas to restrict search in scientific publication database:
10006844
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).
Digital Object Identifier (DOI):

References:

[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.
Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007