CFP last date
22 April 2024
Reseach Article

ECG Signal Processing: A Survey

Published on March 2012 by Smita Kasar, Madhuri Joshi
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 7
March 2012
Authors: Smita Kasar, Madhuri Joshi

Smita Kasar, Madhuri Joshi . ECG Signal Processing: A Survey. International Conference in Computational Intelligence. ICCIA, 7 (March 2012), 26-30.

author = { Smita Kasar, Madhuri Joshi },
title = { ECG Signal Processing: A Survey },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 7 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /proceedings/iccia/number7/5140-1052/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Smita Kasar
%A Madhuri Joshi
%T ECG Signal Processing: A Survey
%J International Conference in Computational Intelligence
%@ 0975-8887
%N 7
%P 26-30
%D 2012
%I International Journal of Computer Applications

Electrocardiograms (ECGs) are signals that originate from the action of the human heart. The ECG is the key biosignal for aiding the clinical staff in disease diagnosis. The recognition and analysis of the ECG signals is a very important task. This could be difficult, because the size and form of these signals may change eventually and can be noised. ECG noise removal is complicated due to the time varying nature of ECG signals. The traditional approach to remove high frequency noise from ECG signal is to employ a low-pass filter [1]. However, the cut-off frequency is difficult to determine and it may introduce some additional artifacts to the signal, especially on the QRS wave. Other filtering techniques that have been proposed are reviewed here. The next step is extracting feature from the signal. One cardiac cycle in an ECG signal consists of PQRST waves. The feature extraction scheme determines the amplitude, intervals etc. in the signal for further classification of cardiac diseases. Recently many research and techniques have been developed for processing the ECG signal. The techniques for filtering and extraction are reviewed here Keywords: ECG, Feature Extraction, Noise.

  1. T. Slonim, M. Slonim, E. Ovsyscher, “The use of simple FIR filters for filtering of ECG signals and a new method for post-filter signal reconstruction”, Proceedings Computers in Cardiology, 1993.
  2. V. Afonso, W. Tompkins, “Filter bank-based processing of the stress ECG”, IEEE 17th Annual Conference on Engineering in Medicine and Biology Society, 1995. 398
  3. Reza Sameni, Mohammad B. Shamsollahi , Christian Jutten , “A Nonlinear Bayesian Filtering Framework for ECG Denoising” , IEEE Transactions On Biomedical Engineering, VOL. 54, NO. 12, DECEMBER 2007,2172-2185
  4. P. E. McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, “A dynamic model for generating synthetic electrocardiogram signals,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 289– 294, Mar. 2003.
  5. P. E. McSharry and G. D. Clifford, ECGSYN—A realistic ECG waveform generator. PhysioNet, Cambridge,
  6. R. Sameni, M. B. Shamsollahi, and C. Jutten, “Filtering electrocardiogram signals using the extended Kalman filter,” Proceeding 27th Annu. Int Conf. IEEE Eng. Medicine Biol. Soc. (EMBS), Shanghai, China, Sep. 1–4, 2005, pp. 5639–5642.
  7. R. Sameni, M. B. Shamsollahi, C. Jutten, and M. Babaie-Zadeh,“Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model,” Proc. 32nd Annu. Int. Conf Comput. Cardiology, Lyon, France, Sep. 25–28, 2005, pp. 1017– 1020
  8. I. I. Christov and I. K. Daskalov, “Filtering of electromyogram artefacts from the electrocardiogram,” Med. Eng. Phys., vol. 21, pp. 731–736, 1999.
  9. A. Gotchev, N. Nikolaev, and K. Egiazarian, “Improving the transform domain ECG denoising performance by applying interbeat and intrabeat decorrelating transforms,” Proc. 2001 IEEE Int. Symp. Circuits Syst. (ISCAS), Sydney, Australia, 2001, pp. 17–20.
  10. Yue-Der Lin and Yu Hen Hu, “Power-Line Interference Detection and Suppression in ECG Signal Processing”, IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, January 2008,pp 354-357.
  11. V. Afonso, W. Tompkins, et al., “Filter bank-based processing of the stress ECG,” IEEE 17th Annual Conference on Engineering in Medicine and Biology Society, 1995.
  12. Friesen, G.M., Thomas, C.J., Jadallah, M.A., Yates, S.L., Quint, S.R., and Nagle, H.T. (1990): ‘A comparison of noise sensitivity of 9 QRS detection algorithms’, IEEE Trans. Biomed. Eng., 37(1), pp. 85–98.
  13. P. Lander and E. J. Berbari, “Timefrequency plane wiener filtering of the high-resolution ECG: Background and time-frequency representations,” IEEE Trans. Biomed. Eng., vol. 44, no. 4, pp. 247–255, Apr. 1997.
  14. H. A. Kestler, M. Haschka, W. Kratz, F. Schwenker, G. Palm, V. Hombach, and M. Höher, “Denoising of high-resolution ecg-signals by combining the discrete wavelet transform with the wiener filter,” Proceedings, IEEE Conf. Comput. Cardiology, 1998, pp. 233–236.
  15. N. Nikolaev and A. Gotchev, “ECG signal denoising using wavelet domain wiener filtering,” Proc. Eur. Signal Process. Conf. EUSIPCO- 2000, Tampere, Finland, Sep. 2000, pp. 51–54.
  16. D. L. Donoho, “De-noising by softthresholding,” IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613–627, May 1995.
  17. V.S. Chouhan, S.S. Mehta, “Total Removal of Baseline Drift from ECG Signal”, Proceedings of the International Conference on Computing: Theory and Applications (ICCTA'07) IEEE,2007
  18. P. M. Agante and J. P. M. de Sá, “ECG noise filtering using wavelets with soft-thresholding methods,” Proc. Comput. Cardiology 1999, 1999, pp. 535–542.
  19. G. Moody and R. Mark, “QRS morphology representation and noise estimation using the Karhunen-Loève transform,” Computer Cardiology, vol. 16, pp. 269–272, 1989.
  20. A. Barros, A. Mansour, and N. Ohnishi, “Removing artifacts from ECG signals using independent components analysis,” Neurocomputing, vol. 22, pp. 173–186, 1998.
  21. T. He, G. D. Clifford, and L. Tarassenko, “Application of ICA in removing artefacts from the ECG,” Neural Computer Application, vol. 15, no. 2, pp. 105–116, 2006.
  22. G. D. Clifford and L. Tarassenko, “One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats,” Electron. Lett., vol. 37, no. 18, pp. 1126–1127, Aug. 2000
  23. T. Schreiber and D. T. Kaplan, “Nonlinear noise reduction for electrocardiograms,” Chaos Interdisciplinary J. Nonlinear Sci., vol. 6, no. 1, pp. 87–92, Mar. 1996.
  24. Felipe E. Olvera , “Electrocardiogram Waveform Feature Extraction Using the Matched Filter”, ECE 510: Statistical Signal Processing II
  25. T. Srikanth, S. Napper, and H. Gu, “Bottom-Up Approach to Uniform Feature Extraction in Time and Frequency Domains for Single Lead ECG Signal,” Journal of the International Society for Biolectromagnetism, vol. 4, no. 1, 2002.
  26. Suranai Poungponsri, Xiao-Hua Yu, ”Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Wavelet Neural Networks” , CA 93407, USA
  27. John R. Hampton,” The ECG made Easy”,Churchill Livingstone Elsevier, ISBN 978-0-443-06826-3
  28. Jiapu Pan, Willis J. Tompkins,” A Real Time QRS Detection algorithm”, IEEE Transactions on Biomedical Engineering, Vol, BME-32, No 3, March 1985.
  29. Remi Dubois, Pierre Maison- Blanche, Brigitte Quenet , Gérard Dreyfus, “Automatic ECG wave extraction in longterm recordings using Gaussian Mesa Function Models and Nonlinear Probability Estimators”, Computer Methods and Programs in Biomedicine, vol. 88,2007, pp 217-233.
  30. S. Z. Fatemian, and D. Hatzinakos, “A new ECG feature extractor for biometric recognition,” 16th International Conference on Digital Signal Processing, pp. 1-6, 2009.
  31. Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen7, Chi Chao Tung and Henry H. Liu,” The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis”, Proceeding Royal Society London, A (1998) 454, 903-995
  32. Dean Cvetkovic , Elif Derya Ubeyli , Irena Cosic, “Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study”, ScienceDirect, Elsevier, Digital Signal Processing 18 (2008) 861–874
  33. Zhao Yong, Hong Wenxue, Xu Yonghong, Cui Jianxin, “ECG Beats Classification Based on Ensemble Feature Composed of Independent Components and QRS Complex Width” IEEE, International Conference on Computer Science and Software Engineering, 2008, pp 868- 871.
Index Terms

Computer Science
Information Sciences


ECG Feature Extraction Noise