Adaptive-Filtering-Based Algorithm for Impulsive Noise Cancellation from ECG Signal

Document Type: Research Paper

Authors

1 Department of Electrical Engineering, Malayer University, Malayer, Iran

2 Department of Electrical and Electronic Engineering, Independent University, Bangladesh

Abstract

Suppression of noise and artifacts is a necessary step in biomedical data processing. Adaptive filtering is known as useful method to overcome this problem. Among various contaminants, there are some situations such as electrical activities of muscles contribute to impulsive noise. This paper deals with modeling real-life muscle noise with α-stable probability distribution and adaptive filtering noise cancellation assessment with maximum correntropy criterion (MCC) as adaptive technique. Based on our test on some data of MIT-BIH arrhythmia and EMBC databases, we achieve an improved SNR in any electrocardiogram (ECG) signal corrupted by impulsive noise. The worst achieved improvement based on setting the best parameter values using trial and error for both filter and utilized algorithm is 9.5 dB with correlation coefficient value of 0.93. The SNR improvement on the whole utilized database records is 11.03 dB on average. The proposed algorithm is applied to the records from MIT-BIH arrhythmia and EMBC databases to remove the impulsive noise. A computer simulation is used to create and add it to the ECG signals. Simulation results are also provided to support the discussions.

Graphical Abstract

Adaptive-Filtering-Based Algorithm for Impulsive Noise Cancellation from ECG Signal

Keywords


[1] J.L. Rodríguez-Sotelo, G. Castellanos-Domínguez, and C.D. Acosta-Medina, “Recognition of cardiac arrhythmia by means of beat clustering on ECG-holter recordings, advances in electrocardiograms - methods and analysis,” Ph.D. Richard Millis (Ed.), ISBN: 978-953-307-923-3, 2012.

[2] A.F. Shackil, “Microcomputers: Microprocessors and the M.D.: A new breed of smart medical equipment can diagnose,

monitor, analyze, and rehabilitate,” IEEE Spectrum, vol. 18, no. 4, pp. 45-49, 2012.

[3] D. C. Reddy, “Biomedical signal processing: principles and techniques,” McGraw-Hill Education (India) Pvt Limited, ISBN: 0070583889, pp. 254-311, 2005.

[4] E.B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, and N. Curzen, “A lowcomplexity ECG feature extraction algorithm for mobile healthcare applications,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 2, pp. 459-469, 2013.

[5] V. Zarzoso and A.K, Nandi, “Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 1, pp. 12-18, 2001.

[6] G.D. Fraser, A.D.C. Chan, J.R Green, and D.T. MacIsaac, “Automated biosignal quality analysis for electromyography using a one-class support vector machine,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 12, pp. 2919- 2930, 2014.

[7] M.P.S. Chawla, H.K Verma, and V. Kumar, “RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis,” International Journal of Cardiology, vol. 129, no. 2, pp. 278-281, 2008.

[8] M. Milanesi, N. Martini, N. Vanello, V. Positano, M.F. Santarelli, and L. Landini “Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals,” Int. J. Med. Biol. Eng. Comput., vol. 46, no. 3, pp 251- 261, 2008.

[9] M.P.S. Chawla, “PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison,” Applied Soft Computing, vol. 11, no. 2, pp. 2216- 2226, 2011.

[10] S. Banerjee, R. Gupta, and M. Mitra, “Delineation of ECG characteristic features using multiresolution wavelet analysis method,” Measurement, vol. 45, no. 3, pp. 474-487, 2012.

[11] P. Saurabh and M. Madhuchhanda, “Detection of ECG characteristic points using multiresolution Wavelet analysis based selective coefficient method,” Measurement, vol. 43, no. 2, pp. 255-261, 2010.

[12] L. Smital, M. tek, J. ozumpl k, and I. rovazn k, “adaptive wavelet Wiener filtering of ECG signals,” IEEE Transactions on Biomedical Engineering, , vol. 60, no. 2, pp. 437-445, 2013.

[13] L. Chmelka and J. Kozumplik, “Wavelet-based Wiener filter for electrocardiogram signal denoising,” Computers in Cardiology, pp.771-774, DOI: 10.1109/CIC.2005.1588218, 2005.

[14] R. Vullings, B. Vries, and J.W.M. Bergmans, “An adaptive kalman filter for ECG signal enhancement,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 1094-1103, 2011.

[15] R. Sameni, M.B. Shamsollahi, C. Jutten, and G.D. Clifford, “A nonlinear bayesian filtering framework for ECG denoising,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 12, pp. 2172-2185, 2007.

[16] J. Shazia and A. Noor Atinah, “An adaptive noise cancelation model for removal of noise from modeled ECG signals,” Region 10 Symposium, 2014 IEEE, pp. 471-475, 14-16 April 2014.

[17] T. Pander, “Impulsive noise filtering in biomedical signals with application of new myriad filter”, The 20th biennial international EURASIP conference biosignal, pp. 94-101, 2010.

[18] T.P. Pander, “A suppression of an impulsive noise in ECG signal processing,” 26th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, IEMBS '04. vol. 1, pp. 596-599, 1-5 Sept. 2004.

[19] S. Gupta, R. Manthalkar, and S. Gajre, “Suppression of impulse noise using adaptive filters," Computing in Cardiology Conference (CinC), pp. 527-530, 22-25 Sept. 2013.

[20] A. Singh and J.C. Principe, "Using correntropy as a cost function in linear adaptive filters," International Joint Conference on Neural Networks, 2009. IJCNN 2009, pp. 2950-2955, 14-19 June 2009.

[21] S. Zhao, CH. Badong , and J.C Principe, “Kernel adaptive filtering with maximum correntropy criterion,” The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2012-2017, July 31 2011-Aug. 5 2011.

[22] A. H. Sayed. “Fundamentals of adaptive filtering. Hoboken”, NJ, USA: Wiley, 2003.

[23] L. Shi and L. Yun, “Convex combination of adaptive filters under the maximum correntropy criterion in impulsive interference," IEEE Signal Processing Letters, vol. 21, no. 11, pp. 1385-1388, 2014.

[24] E. Kheirati Roonizi, “A new algorithm for fitting a gaussian function riding on the polynomial background," IEEE Signal Processing Letters, vol. 20, no. 11, pp. 1062-1065, 2013.

[25] M.D. Button, J.C. Gardiner, and I.A. Glover, “Measurement of the impulsive noise environment for satellite-mobile radio systems at 1.5 GHz,” IEEE Transactions on Vehicular Technology, , vol. 51, no. 3, pp. 551-560, 2002.

[26] M. Nassar, K. Gulati, A.K. Sujeeth, N. Aghasadeghi, B.L. Evans, and K.R. Tinsley, “Mitigating near-field interference in laptop embedded wireless transceivers,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008, pp.1405-1408, March 31 2008-April 4 2008.

[27] D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: new methods an results for class A and class B noise models,” IEEE Transactions on Information Theory, vol. 45, no. 4, pp. 1129-1149, 1999.

[28] L. Weifeng , P.P. Pokharel, and J.C. Principe, “Correntropy: properties and applications in non-Gaussian signal processing,” IEEE Transactions on Signal Processing, vol. 55, no. 11, pp. 5286-5298, 2007.

[29] Ch. Badong and J.C. Principe, “Maximum correntropy estimation is a smoothed map estimation,” IEEE Signal Processing Letters, vol. 19, no. 8, pp. 491-494, 2012.

[30] H. Ran , Zh. Wei-Shi, and H. Bao-Gang, “Maximum correntropy criterion for robust face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1561-1576, 2011.

[31] H. Ran , H. Bao-Gang, Zh. Wei-Shi, and K. Xiang-Wei, “Robust principal component analysis based on maximum correntropy criterion,” IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1485-1494, 2011.

[32] Ch. Badong , L. Xing, J, Liang, N. Zheng, and J.C Principe, “Steady-State mean-square error analysis for adaptive filtering under the maximum correntropy criterion,” IEEE Signal Processing Letters, vol. 21, no. 7, pp. 880-884, 2014. [33] W. Bazzi, A. Rastegarnia, and A. Khalili, “A robust diffusion adaptive network based on the maximum correntropy criterion,” in 2015 24th International Conference on Computer Communication and Networks (ICCCN), pp. 1–4,2015.

[34] Al. Goldberger, L. Amaral , L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. Peng, and H. Stanley “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. 215-220.

[35] C.A. Ledezma, E. Severeyn, G. Perpinan, M. Altuve, and S. Wong, “A new on-line electrocardiographic records database and computer routines for data analysis,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), , pp. 2738-2741, 26-30 Aug.2014.

[36] Md. K. Islam, A. Rastegarnia, and A. halili, “A robust distributed estimation algorithm under alpha-stable noise condition,” Journal of Communication Engineering, vol. 4, no. 2, pp. 76-85, 2015.