Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition


  • W. Mohguen CCNS Laboratory, Department of Electronics, Faculty of Technology, University of Ferhat Abbas - Setif 1, Algeria
  • S. Bouguezel CCNS Laboratory, Department of Electronics, Faculty of Technology, University of Ferhat Abbas - Setif 1, Algeria
Volume: 11 | Issue: 5 | Pages: 7536-7541 | October 2021 |


In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.


denoising, ECG, EMD, EEMD, customized thresholding


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N. E. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903-995, Mar. 1998.

P. Flandrin, P. Gonçalvès, and G. Rilling, "Emd equivalent filter banks, from interpretation to applications," in Hilbert-Huang Transform and Its Applications, vol. 5, Singapore: World Scientific, 2005, pp. 57-74.

A. O. Boudraa, J. C. Cexus, and Z. Saidi, "EMD-Based Signal Noise Reduction," International Journal of Signal Processing, vol. 1, no. 1, pp. 33-37, 2004.

A.-O. Boudraa and Jean-Christophe Cexus, "Denoising via Empirical Mode Decomposition," presented at the International Symposium on Communications, Control and Signal Processing (ISCCSP '06), Marrakech, Morocco, Mar. 2006.

Y. Kopsinis and S. McLaughlin, "Empirical mode decomposition based soft-thresholding," in 2008 16th European Signal Processing Conference, Aug. 2008, pp. 1-5.

Y. Kopsinis and S. McLaughlin, "Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding," IEEE Transactions on Signal Processing, vol. 57, no. 4, pp. 1351-1362, Apr. 2009.

G. Yang, Y. Liu, Y. Wang, and Z. Zhu, "EMD interval thresholding denoising based on similarity measure to select relevant modes," Signal Processing, vol. 109, pp. 95-109, Apr. 2015.

W. Mohguen and R. E. Bekka, "Empirical Mode Decomposition Based Denoising by Customized Thresholding," International Journal of Electronics and Communication Engineering, vol. 11, no. 5, pp. 519-524, Mar. 2017.

W. Mohguen and R. E. Bekka, "New Denoising Method Based on Empirical Mode Decomposition and Improved Thresholding Function," Journal of Physics: Conference Series, vol. 787, Jan. 2017, Art. no. 012014.

W. Mohguen and R. E. Bekka, "An Empirical Mode Decomposition Signal Denoising Method Based on Novel Thresholding," presented at the 5th International Conference on Control & Signal Processing (CSP-2017), Kairouan, Tunisia, Oct. 2017.

M. V. Sarode and P. R. Deshmukh, "Image Sequence Denoising with Motion Estimation in Color Image Sequences," Engineering, Technology & Applied Science Research, vol. 1, no. 6, pp. 139-143, Dec. 2011.

W. Helali, Ζ. Hajaiej, and A. Cherif, "Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6204-6208, Oct. 2020.

M. Atif, Z. H. Khand, S. Khan, F. Akhtar, and A. Rajput, "Storage Optimization using Adaptive Thresholding Motion Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6869-6872, Apr. 2021.

A. O. Boudraa and J.-C. Cexus, "EMD-Based Signal Filtering," IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 6, pp. 2196-2202, Dec. 2007.

D. L. Donoho and I. M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, vol. 81, no. 3, pp. 425-455, Sep. 1994.

D. L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, May 1995.

M. Rakshit and S. Das, "An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter," Biomedical Signal Processing and Control, vol. 40, pp. 140-148, Feb. 2018.

B.-J. Yoon and P. P. Vaidyanathan, "Wavelet-based denoising by customized thresholding," in 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2004, vol. 2.

Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 01, no. 01, pp. 1-41, Jan. 2009.

A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet," Circulation, vol. 101, no. 23, pp. e215-e220, Jun. 2000.

M. Elgendi, M. Jonkman, and F. D. Boer, "Improved QRS Detection Algorithm using Dynamic Thresholds," International Journal of Hybrid Information Technology, vol. 2, no. 1, pp. 65-80, Jan. 2009.


How to Cite

W. Mohguen and S. Bouguezel, “Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 5, pp. 7536–7541, Oct. 2021.


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