Filtering of ECG Signal Using Adaptive and Non Adaptive Filters

Abhishek Sahu*, Jitendra Kumar**
* PG Scholar, Department of Electronics and Telecommunication, SSTC, Bhilai, India.
** Assistant Professor, Department of Electronics and Instrumentation, SSTC, Bhilai, India.
Periodicity:January - March'2016
DOI : https://doi.org/10.26634/jdp.4.1.4855

Abstract

Electrocardiogram (ECG) is an important diagnostic tool for the diagnosis of cardiac abnormalities. In this paper, the authors introduce a study on different types of noises, For example, Power Line Interference (PLI), Motion Artifacts, Electrode Contact Noise, Muscle Contraction, Base Line Drift, Electromyography/noise (EMG), Instrumentation Noise, etc. To eliminate the above mentioned noises, various algorithms of adaptive filter are used and authors also used Discrete Wavelet Transform (DWT) to remove Random Artifacts and filter with constant coefficients as because, hum manner is not accurate. To solve this problem, digital filters are used such as Adaptive filters as Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), sign LMS, sign-sign LMS algorithms and Discrete Wavelet Transform (DWT). The performance of algorithms are evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE), Percentage Root Mean Square (%PRD) and Normalized Mean Square (NMSE). In comparison to various adaptive algorithms, SSLMS gives better result for all parameters with MSE = 0.0262, NRMSE = 0.0033 , %PRD = 0.3331, RMSE = 0.331, and SNR = -4.3914 .

Keywords

ECG, LMS, NLMS, RLS, SLMS, DWT, SNR, MSE, NMSE, % PRD.

How to Cite this Article?

Sahu,A., and Kumar,J. (2016). Filtering of ECG Signal Using Adaptive and Non Adaptive Filters,i-manager’s Journal on Digital Signal Processing, 4(1), 1-8. https://doi.org/10.26634/jdp.4.1.4855

References

[1]. E. T. Gar, C. Thomas and M. Friesen, (1990). “Comparison of Noise Sensitivity of QRS Detection Algorithms”. IEEE Tran. Biomed. Eng., Vol. 37, No.1, pp. 85- 98.
[2]. Y. Der Lin, and Y. Hen Hu, (2008). “Power-line interference detection and suppression in ECG signal processing ”. IEEE Transactions on Biomedical Engineering, Vol. 55, pp. 354–357.
[3]. N.V. Thakor, and Y.S. Zhu, (1991). “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection”, IEEE Transactions on Biomedical Engineering, Vol. 38, No. 8, pp. 785–794.
[4]. J.A. Van Alste, and T.S. Schilder, (1985). “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, Vol. 32, No. 12, pp.1052–1060.
[5]. A.K. Ziarani, and A. Konrad, (2002). “A nonlinear adaptive method of elimination of power line interference in ECG signals”. IEEE Transactions on Biomedical Engineering, Vol. 49, No.6, pp. 540–547.
[6]. S. Olmos, J. Garcia, R. Jane, and P. Laguna, (1999). “ECG signal compression plus noise filtering with truncated orthogonal expansions”. Signal Processing, Vol. 79, pp. 97–115.
[7]. J.M. Leski, and N. Henzel, (2005). “ECG baseline wander and power line interference reduction using nonlinear filter bank”. Signal Processing, Vol. 85,pp. 781–793.
[8]. V. Almenar, and A. Albiol, (1999). “A new adaptive scheme for ECG enhancement”. Signal Processing, Vol. 75, pp. 253–263.
[9]. C. Meyer, J.F. Gavela, M. Harris, (2006). “Combining algorithms in automatic detection of QRS complexes in ECG signals”, IEEE Transactions on Information Technology in Biomedicine, Vol.10, No. 3, pp. 468–475.
[10]. C. Brouse, G.A. Bumont, F.J. Herrmann, and J.M. Ansermino, (2006). “A wavelet approach to detecting electrocautery noise in the ECG”. IEEE Engineering in Medicine and Biology Magazine, Vol. 25, No. 4, pp. 76–82.
[11]. C. Vaz, and N.V. Thakor, (1989). “Adaptive Fourier estimation of time-varying evoked potentials”. IEEE Transactions on Biomedical Engineering, Vol. 36, pp. 448–455.
[12]. N.V. Thakor, C. Vaz, R.W. Mc Pherson, and D.F. Hanley, (1991). “Adaptive Fourier series modeling of time varyIng evoked potentials : study of human somatosensory evoked response to etomidate anesthetic”. Electro -encephalography Clinical Neurophysiology, Vol. 80, No. 2, pp.108–118.
[13]. A.K. Barros, M. Yoshizawa, and Y. Yasuda, (1995). “ Filtering non correlated noise in impedance cardiography ”. IEEE Transactions on Biomedical Engineering, Vol. 42, pp. 324–327.
[14]. P. Laguna, R. Jane, S. Olmos, N.V. Thakor, H. Rix, and P. Caminal, (1996). “Adaptive estimation of QRS complex by the Hermite model for classification and ectopic beat detection”. Medical & Biological Engineering & Computing, Vol. 34, No. 1, pp. 58–68.
[15]. B. Farhang-Boroujeny, (1998). Adaptive Filters- Theory and Applications. John Wiley and Sons, Chichester, UK.
[16]. S. Koike, (1999). “Analysis of adaptive filters using normalized signed regressor LMS algorithm”. IEEE Transactions on Signal Processing, Vol. 47, No. 10, pp. 2710–2723.
[17]. E. Eweda, (1990). “Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data”. IEEE Transactions on Circuits and Systems, Vol. 37, No. 11, pp.1367–1374.
[18]. de Laboratóio M. Biopac Student Lab. Biopac Systems Inc.
[19]. Moody GB, and Mark RG. (1990). “The MIT-BIH arrhythmia database on CD-ROM and software for use with it”. Proc. 1990 Computers in Cardiology, IEEE; pp. 185–8.
[20]. Manikandan MS, and Dandapat S. (2007). “Wavelet energy based diagnostic distortion measure for ECG”. Biomed Signal Process Control, Vol. 2, pp. 80–96.
[21]. R. Sivakumar, R. Tamilselvi and S. Abinaya, (2012). “Noise Analysis & QRS Detection in ECG Signals”. International Conference on Computer Technologyand Science (ICCTS 2012).
[22]. Smita Kasar, Abbhilasha Mishra, and Madhuri Joshi, (2014). “Performance of digital filters for noise removal from ECG signals in time domain”. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, No. 4.
[23]. Uzzal Biswas, and Md. Maniruzzaman, (2014). “ReSmoving power line interference from ECG signal using adaptive filter and notch filter”, International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2014.
[24]. Ashraf A.M. Khalaf, Mostafa. M. Ibrahim, Hesham F, and A. Hamed, (2015). “Performance Study of Adaptive Filtering and Noise Cancellation of Artifacts in ECG Signals”. Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia, Egypt, ICACT2015, ISBN 978-89-968650-5-6 .
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