Abstract
Empirical Mode Decomposition (EMD) is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the processed signal into components without using any basis functions. This is a data driven representation and provides intrinsic mode functions (IMFs) as components. These are obtained through a so-called sifting process. This study presents an EMD decomposition-based filtering procedure applied to ECG signals (from specific databases), the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for non-linear and non stationary time series analysis. Proc. Roy. Soc. Lond. A 454, 903–995 (1998)
Rilling, G., Flandrin, P., Gonalves, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I), June 2003
Rilling, G., Flandrin, P., Gonalves, P., Lilly, J.M.: Bivariate empirical mode decomposition. Sig. Proc. Lett. (submitted)
Huang, N.E., et al.: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc. Roy. Soc. Lond. A 459, 2317–2345 (2003)
Deering, R., Kaiser, J.F.: The use of a masking signal to improve empirical mode decomposition. In: ICASSP (2005). Smith, J., Jones, M. Jr., Houghton, L., et al.: Future of health insurance. N. Engl. J. Med. 965, 325–329 (1999)
Wu, Z., Huang, N. E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A. 460(2046), 1597–1611 (2003)
Flandrin, P., Goncalves, P., Rilling, G.: Detrending and denoising with empirical mode decomposition. In: Proceedings of XII EUSIPCO 2004, Vienna, Austria, Sept 2004
Tang, G., Qin, A.: ECG denoising based on empirical mode decomposition. In: 9th International Conference for Young Computer Scientists, pp. 903–906 (2008)
Kopsinis, Y., McLaughlin, S.: Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Sig. Process. 57, 1351–1362 (2009)
Benitez, D., Gaydecki, P.A., Zaidi, A., Fitzpatrick, A.P.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31(5), 399–406 (2001)
Tang, G., Qin, A.: ECG de-noising based on empirical mode decomposition. In: 9th International Conference for Young Computer Scientists, pp. 903–906, Feb 2008
Boudraa, A.O., Cexus, J.C.: EMD-based signal filtering. IEEE Trans. Instrum. Meas. 56(6), 2196–2202 (2007)
Conflict of Interest
The authors declare that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Germán-Salló, Z., Germán-Salló, M., Grif, HŞ. (2019). Empirical Mode Decomposition in ECG Signal De-noising. In: Vlad, S., Roman, N. (eds) 6th International Conference on Advancements of Medicine and Health Care through Technology; 17–20 October 2018, Cluj-Napoca, Romania. IFMBE Proceedings, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-13-6207-1_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-6207-1_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6206-4
Online ISBN: 978-981-13-6207-1
eBook Packages: EngineeringEngineering (R0)