INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.8, No.5, May. 2016

Muscle and Baseline Wander Artifact Reduction in ECG Signal Using Efficient RLS Based Adaptive Algorithm

Full Text (PDF, 494KB), PP.41-48


Views:95   Downloads:3

Author(s)

GOWRI T., RAJESH KUMAR P.

Index Terms

RLS Adaptive algorithms;Signal to noise ratio;artifacts;mean square error;ECG signal

Abstract

When we acquiring the Electrocardiogram (ECG) signal from the person, the signal amplitude (PQRST) and timing values are changes due to various artefacts. The different artefacts are Baseline wander, power line interference, muscle artefact, motion artefact and the channel noise also added sometimes during the transmission of the signal for diagnosis purpose. The adaptive filters play vital role for reduction of noise in the desired signals. In this paper we proposed, block based error normalized Recursive Least Square (RLS) adaptive algorithm and sign based RLS adaptive algorithm, which are used for reduction of muscle artifact noise and base line wander noise in the ECG signal. From the simulation result we analyzed that, comparing to Least Mean Square algorithm, the proposed RLS algorithm gives fast convergence rate with high signal to noise ratio and less mean square error.

Cite This Paper

GOWRI T., RAJESH KUMAR P.,"Muscle and Baseline Wander Artifact Reduction in ECG Signal Using Efficient RLS Based Adaptive Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.5, pp.41-48, 2016. DOI: 10.5815/ijisa.2016.05.06

Reference

[1]Van Alste JA, Schilder TS. Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps. IEEE Transactions on Biomedical Engineering 1985; 32(12), pp.1052-1060. 

[2]Gomez-Clapers J, Casanella R. A Fast and Easy-to-Use ECG Acquisition and Heart Rate Monitoring System Using a Wireless Steering Wheel. Sensors Journal 2012;12(3), pp.610-616.

[3]Razzaq N, Butt M, Salman M, Munawar K, Zaidi T. An efficient method for estimation of power line interference in ECG. Proceedings of International Conference on Modelling, Identification & Control (ICMIC), Aug. 31-Sept. 2; 2013, pp.275-279. 

[4]Vullings Rik, de Vries Bert, Bergmans JWM. An Adaptive Kalman Filter for ECG Signal Enhancement. IEEE Transactions on Biomedical Engineering 2011,58(4), pp.1094-1103.

[5]Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection. IEEE Trans.Biomed. Eng. 1991,38(8), pp.785–794.

[6]Poungponsri S, X. Yu. Electrocardiogram signal modeling and noise reduction using wavelet neural networks. IEEE International Conference on Automation and Logistics, pp. 394-398, 2009.

[7]Agante P. M, de Sa J.P.M, ”ECG noise filtering using wavelets with soft-thresholding Methods”, In Computers in Cardiology, pp. 535-538, 1999.

[8]Sayadi O, Shamsollahi M.B.ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure. Biomedical Engineering, IEEE Transactions on, vol. 55(9), pp. 2240-2248, 2008.

[9]Yan J, Y. Lu, Liu J, X. Wu, and Y. Xu. Self-adaptive model-based ECG denoising using features extracted by mean shift algorithm. Biomedical Signal Processing and Control, vol.5(2), pp.103 -113, 2010.

[10]Floris E, Schlaefer A, Dieterich S, Schweikard A. A Fast Lane Approach to LMS prediction of respiratory motion signals. Biomed. Signal Process. Control 2008, pp.291–299.

[11]Sayadi O, Shamsollahi MB. Model-based fiducial points extraction for baseline wander electrocardiograms. IEEE Trans. Biomed. Eng. 2008,55(1), pp347–351.

[12]Samit Ari, Das MK, Chacko A. ECG signal enhancement using S-Transform. Computers in Biology and Medicine 2013,43(6), pp.649–660.

[13]Patnaik P, Prasad Das D, Mishra S. K. Adaptive Inverse Model of Nonlinear Systems. International Journal of Intelligent Systems and Applications (IJISA) vol. 5, pp. 40-47, April 2015. 

[14]Aboulnasr T, Mayyas K. A robust variable size LMS type algorithm: analysis and simulation. IEEE Trans. Signal Processing 1997,45(3), pp.631-639.

[15]Gowri T, Rajesh Kumar P, Rama Koti Reddy DV. An Efficient Variable Step Size Least Mean Square Adaptive Algorithm Used to Enhance the Quality of Electrocardiogram Signal. Advaces in intelligent systems and computing 2014; pp.264; 463-475.

[16]Olaniyi E. O, Oyedotun O. K, Adnan K. Heart Diseases Diagnosis Using Neural Networks Arbitration. International Journal of Intelligent Systems and Applications (IJISA) vol. 12, pp. 75-82, April 2015.

[17]Yazdanpanah B, Sravan Kumar K, Raju G.S.N. Noise Removal ECG Signal Using Non-Adaptive Filters and Adaptive Filter Algorithm. Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015 International Conference, pp. 24-25, 2015.

[18]Rahmana MZU, Shaik RA, Reddy DVRK. Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry. Signal Processing 2010, 91, pp.225–239. 

[19]Butt M, Razzaq N, Sadiq I. Salman M, Zaidi T. Power Line Interference tracking in ECG signal using State 

[20]Space RLS. 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) 2013, June 19-21, pp.211-215.

[21]A. N. Ali, Advanced Bio Signal Processing. Berlin, Germany: Springer Verlag, 2009.

[22]The MIT-BIH noise stress test database. [Online]. Available: /http://www. physionet.org/physiobank/database/nstdb/.