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Kawser Ahammed

Abstract

This research clearly demonstrates the comparative performance study of Least Mean Square (LMS) adaptive and fixed Notch filter in terms of simulation results and different performance parameters (mean square error, signal to noise ratio and percentage root mean square difference) for removing structured noise (50 Hz line interference and its harmonics) and baseline wandering from electrocardiogram (ECG) signal. The ECG samples collected from the PhysioNet ECG-ID database are corrupted by adding structured noise and base line wandering noise. The simulation results and numerical performance analysis of this research clearly show that LMS adaptive filter can remove noise efficiently from ECG signal than fixed notch filter

Keywords

Keywords: LMS Filter, Fixed Notch Filter, Structured Noise, Base line Wander, ECG, Mean Square Error (MSE), Signal to Noise Ratio (SNR), Percentage Root Mean Square Difference (PRD)

References

References

Hosseini, H. Gholam, Dehan Luo, and Karen Jane Reynolds, “The

comparison of different feed forward neural network architectures for ECG signal diagnosis,” Medical engineering & physics, vol. 28, pp. 372-378, 2006.

Klemm et al., “Correlation of symptoms to ECG diagnosis following atrial fibrillation ablation,” Journal of cardiovascular electrophysiology, vol. 17, pp. 146-150, 2006.

Fesmire and Francis M,“ECG diagnosis of acute myocardial infarction in the presence of left bundle-branch block in patients undergoing continuous ECG monitoring,” Annals of emergency medicine, vol. 26, pp. 69-82, 1995.

E. T. Gar, C. Thomas and M. Friesen, “Comparison of Noise Sensitivity of QRS Detection Algorithms,” IEEE Transactions on Biomedical Engineering, vol. 37, pp. 85-98, 1990.

B. Widrow et al., “Adaptive noise cancelling: Principles and applications,” Proc. IEEE, vol. 63, pp. 1692 -1716, 1975.

N. V. Thakor and Y. S. Zhu, “Applications of adaptive filtering to

ECG analysis: Noise cancellation and arrhythmia detection,” IEEE Trans. Biomed. Eng., vol. 38, pp. 785 -794, 1991.

A. K. Ziarani and A. Konrad, “A nonlinear adaptive method of elimination of power line interference in ECG signals,” IEEE Transactions on Biomedical Engineering, vol. BME-49, no. 6, pp. 540 -547, 2002.

J. M. Leski and N. Henzel, “ECG baseline wander and power line

interfeence reduction using nonlinear fiter bank,” Signal Proessing, vol. 85, pp. 781-793, 2005.

S. Olmos and P. Laguna, “Steady-state MSE convergence analysis in LMS adaptive fiters with deterministic reference inputs for biomedical signals,” IEEE Trasactions on Signal Processing, vol. 48, pp. 2229-2241, 2000.

M. Kotas, “Application of projection pursuit based robust principal component analysis to ECG enhancement,” Biomedical Signal Processing and Control, vol. 1, pp. 289-298, 2007.

G. Mihov and I. Dotsinsky, “Power-line interference eliminaton from ECG in case of non-multiplicity between the sampling rate and the powerline frequency,” Biomedical Signal Processing and Control, vol. 3, pp.334-340, 2008.

M. Blanco-Velasco et al., “On the use of PRD and CR parameters for ECG compression,” Medical Engineering & Physics, Elsevier, vol. 27, pp. 798-802, 2007.

S. Haykin, Adaptive Filter Theory, 4th ed., Prentice Hall, 1996, pp. 50-52.

J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, 4th ed., Printice Hall, 2011, pp. 896-897.

E. C. Ifeachor and B. W. Jervis, Digital Signal Processing: A Practical Approach, 2nd ed., Pearson Hall, 2002, pp. 553-556.

V. Thakor and Yi-Sheng Zhu, “Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection”, IEEE

Transactions on Biomedical Engineering, vol. 18, 1991.

T. S. Lugovaya, “Biometric human identification based on electrocardiogram”, Master’s thesis, Faculty of Computing Technologies and Informatics, Electrotechnical University ”LETI”, Saint-Petersburg, Russian Federation, 2005.

A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”, Circulation vol. 101, pp. e215-e220 [Circulation Electronic Pages: http://circ.ahajournals.org/content/101/23/e215.full], 2000.

Shivika Goyal et al., “Design of ANC filter using modified cuckoo search technique for ECG signal enhancement”, Perspectives in Science, vol. 8, pp. 43-45, 2016.

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