Denoising of ECG signal based on improved adaptive filter with EMD and EEMD

New improved methods for denoising Electrocardiogram (ECG) signal are proposed based on adaptive filter with Empirical mode Decomposition (EMD) and Ensemble Empirical mode Decomposition (EEMD). EMD and EEMD methods are used to decompose the ECG signal into intrinsic mode functions (IMF). Performance of traditional EMD based denoising methods improved by adaptively processing the IMF components which are related to ECG noise. Convergence issue in Least Mean Square (LMS) algorithm addressed by EEMD based adaptive algorithm. Block least mean square (ELMS) algorithm used with EMD and EEMD to improve the computational efficiency of adaptive processing. Proposed methods are applied on white Gaussian noise added ECG signal and real time ECG signals obtained from physionet MIT-BIH arrhythmia data base. Signal to Noise Ratio (SNR), correlation co-efficient and Mean Square Error (MSE) are used to measure and compare the performance of proposed methods with traditional EMD based methods. All the experiments done with MATLAB based coding. Results show that EEMD with ELMS algorithm performs better than traditional EMD based methods.

[1]  A.J. Nimunkar,et al.  EMD-based 60-Hz noise filtering of the ECG , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Zhi-Dong Zhao,et al.  A New Method for Removal of Baseline Wander and Power Line Interference in ECG Signals , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Mahesh S. Chavan.  Suppression of Baseline Wander and power line interference in ECG using Digital IIR Filter , 2008 .

[5]  Alireza K. Ziarani,et al.  A nonlinear adaptive method of elimination of power line interference in ECG signals , 2002, IEEE Transactions on Biomedical Engineering.

[6]  Pun Sio Hang,et al.  Accurate Removal of Baseline Wander in ECG Using Empirical Mode Decomposition , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.

[7]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[8]  Zhao Zhidong,et al.  A novel cancellation method of powerline interference in ECG signal based on EMD and adaptive filter , 2008, 2008 11th IEEE International Conference on Communication Technology.

[9]  Pablo Laguna,et al.  Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS , 2002, IEEE Trans. Signal Process..

[10]  M.Z.U. Rahman,et al.  Adaptive noise removal in the ECG using the Block LMS algorithm , 2009, 2009 2nd International Conference on Adaptive Science & Technology (ICAST).