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A Novel Technique of Noise Cancellation based on Stationary Bionic Wavelet Transform and WATV:
In this paper, is proposed a novel technique of Electrocardiogram (ECG) denoising. It is based on the application of
Wavelet/Total-Variation (WATV) denoising approach in the domain of the Stationary Bionic Wavelet Transform (SBWT). It
consists firstly in applying the SBWT to the noisy ECG signal for obtaining two noisy coefficients named wtb1 and wtb2 which
are respectively details and approximation coefficients. For estimating the level of noise altering the signal, named σ, we use
wtb1. This noise is an additive Gaussian white noise. The thresholding of wtb1 is secondly performed employing the soft
thresholding and a denoised coefficient wtd1 is obtained. This thresholding requires the use of a certain threshold, thr which is
computed using σ. Thedenoising of wtb2 is performed using WATV denoising method and we obtain a denoised coefficient,
wtd2. This WATV denoising method also uses . The denoised ECG signal is finally obtained by applying the inverse of SBWT
(SBWT-1) to wtd1 and wtd2. The proposed technique performance is justified by the results obtained from the computations of
Signal to Noise Ratio (SNR), Minimum Square Error (MSE), Mean Absolute Error (MAE), Peak-SNR (PSNR) and Cross-
Correlation (CC).
[1] Belkadi M. and Daamouche A., “Swarm Intelligence Approach to QRS Detection,” The International Arab Journal of Information Technology, vol. 17, no. 4, pp. 480-487, 2020.
[2] Chunqiang Q., Su H., and Yu H., “Local Means Denoising of ECG Signal,” Biomedical Signal Processing and Control, vol. 53, no. 3, 2019.
[3] Crouse M., Nowak R., and Baraniuk R., “Wavelet-Based Statistical Signal Processing Using Hidden Markov Models,” IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 886-902, 1998.
[4] Ding Y. and Selesnick I., “Artifact-Free Wavelet Denoising: Non-Convex Sparse Regularization, Convex Optimization,” IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1364-1368, 2015.
[5] Dubey A., Mirza H., and Ahmed M., “Two-stage Nonlocal Means Denoising of ECG Signals,” International Journal of Advanced Research in Computer Science, vol. 5, no. 8, pp. 114-118, 2014.
[6] Houamed I., Saidi L., and Srairi F., “ECG signal Denoising by Fractional Wavelet Transform Thresholding,” Research on Biomedical Engineering, vol. 36, pp. 349-360, 2020.
[7] Johnson M., Yuan X., and Ren Y., “Speech Signal Enhancement through Adaptive Wavelet Thresholding,” Speech Communication, vol. 49, no. 2, pp. 123-133, 2007.
[8] Karimipour A. and Homaeinezhad M., “Real- Time Electrocardiogram P-QRS-T Detection- Delineation Algorithm Based on Quality- Supported Analysis of Characteristic Templates,” Computers in Biology and Medicine, vol. 52, pp. 153-165, 2014.
[9] Kumar A., Tomar H., Mehla K., Komaragiri R., and Kumar M., “Stationary Wavelet Transform Based ECG Signal Denoising Method,” ISA Transactions, vol. 114, pp. 251-262, 2020.
[10] Ling B., Ho C., Lam H., Wong T., Chan A., and Tam P., “Fuzzy Rule Based Multiwavelet ECG Signal Denoising,” in Proceedings of International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Hong Kong, pp. 1064-1068, 2008.
[11] Lu G., Brittain J., Holland P., Yianni J., Green A., Stein J., Aziza T., and Wang S., “Removing ECG Noise from Surface EMG Signals Using Adaptive Filtering,” Neuroscience Letters, vol. 462, no. 1, pp. 14-19, 2009.
[12] Rakshit M. and Das S., “An Efficient ECG Denoising Methodology Using Empirical Mode Decomposition and Adaptive Switching Mean Filter,” Biomedical Signal Processing and Control, vol. 40, pp. 140-148, 2018.
[13] Sayadi O. Shamsollahi M., “ECG Denoising and Compression Using A Modified Extended Kalman Filter Structure,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 9, pp. 2240-2248, 2008.
[14] Selesnick I. and Wagner C., “Double-Density Wavelet Software,” Supported by: NSF.
[15] Sharma L., Dandapat S., and Mahanta A., “ECG Signal Denoising Using Higher order Statistics in Wavelet Subbands,” Biomedical Signal Processing and Control, vol. 5, no. 3, pp. 214- 222, 2010.
[16] Srivastava S., “Denoising and Artifacts Removal in ECG Signals,” Ph.D Thesis, National Institute of Technology, 2015.
[17] Sundar A., Evaluating performance of denoising algorithms using metrics: MSE, MAE, SNR, PSNR and cross correlation (https://www.mathworks.com/matlab central/fileexchange/52342-evaluating- performance-of-denoising-algorithms-using- metrics-mse-mae-snr-psnr-cross-correlation), MATLAB Central File Exchange, Last Visited, 2021.
[18] Talbi M., Salhi L., and Adnen C., “Spectral Entropy Employment in Speech Enhancement based on Wavelet Packet,” World Academy of Science, Engineering and Technology, International Journal of Electronics and Communication Engineering, vol. 1, no. 9, pp. 2746-2753, 2007.
[19] Talbi M., “Electrocardiogram De-Noising Based on Forward Wavelet Transform Translation Invariant Application in Bionic Wavelet Domain,” Sadhana Journal, vol. 39, no. 4, pp. 921-937, 2014.
[20] Talbi M., “Speech Enhancement Based on Stationary Bionic Wavelet Transform and Maximum A Posterior Estimator of Magnitude- Squared Spectrum,” International Journal of Speech Technology, vol. 20, pp. 75-88, 2017.
[21] Talbi M., “New Approach of ECG Denoising Based on 1-D Double-Density Complex DWT and SBWT,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 8, no. 6, pp. 608-620, 2020.
[22] Tracey B. and Miller E., “Non-Local Means Denoising of ECG Signals,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2383-2386, 2012. A Novel Technique of Noise Cancellation based on Stationary Bionic Wavelet ... 387
[23] Üstündağ M., Gökbulut M., Şengür A., and Ata F., “Denoising of Weak ECG Signals By Using Wavelet Analysis and Fuzzy Thresholding,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 1, no. 4, pp. 135-140, 2012.
[24] Wang Z., Zhu J., Yan T., and Yang L., “A New Modified Wavelet-Based ECG Denoising,” Computer Assisted Surgery, vol. 24, no. 1, pp. 174-183, 2019.
[25] Yao J. and Zhang Y., “Bionic Wavelet Transform: A New Time-Frequency Method Based on An Auditory Model,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 8, pp. 856- 863, 2001.
[26] Yao J., “An Active Model for Otoacoustic Emissions and its Application to Time-Frequency Signal Processing” Ph.D Thesis, the Chinese University of Hong Kong, 2001.
[27] Zhang D., Wang S., Li F., Wang J., Sangaiah A., Sheng V., and Ding X., “An ECG Signal De- Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter,” Applied Sciences, vol. 9, no. 22, pp. 1-16, 2019. Mourad Talbi is an Assistant professor in the center of researches and technologies of energy, Tunis, Tunisia. He received his Master (2004) in automatics and Signal Processing from National School of Engineers of Tunis (ENIT) He received his Ph.D. Thesis (2010) and his HDR (2015) in Electronics from Faculty of sciences of Tunis. Med Salim Bouhlel received the engineering Diploma from the National Engineering School of Sfax (ENIS) in 1981, the DEA in Automatic and Informatic from the National Institute of Applied Sciences of Lyon in 1981, the degree of Doctor Engineer from the National Institute of Applied Sciences of Lyon in 1983. He is actually a full professor and the Head of Biomedical imagery Department in the Higher Institute of Biotechnology Sfax (ISBS).