Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks

Authors

  • F. Bagheri Department of Biomedical Engineering, Iran University of Science & Technology, Iran
  • N. Ghafarnia Department of Mechatronics, Tehran University, Iran
  • F. Bahrami Department of Biomedical Engineering, Tehran University, Iran
Volume: 3 | Issue: 1 | Pages: 345-348 | February 2013 | https://doi.org/10.48084/etasr.243

Abstract

The Electrocardiogram (ECG) signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN) is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN) is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

Keywords:

Hopfield Neural Networks, ECG signal modeling, noise reduction

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References

O. Sayadi, M. B. Shamsollahi, “ECG denoising and compression using a modified extended kalman filter structure”, IEEE Trans. Biomed. Eng, Vol. 55, No. 9, pp. 2240-2248, 2008 DOI: https://doi.org/10.1109/TBME.2008.921150

W. Zhang, T. Ma, L. Ge, “Enhancement of ECG signals by multi-resolution sub band filter”, 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2008, China, 2008 DOI: https://doi.org/10.1109/ICBBE.2008.860

J. Wang, Z. Li, “An ECG segmentation model used for signal generator”, 2nd International Conference on Innovative Computing, Information and Control, ICICIC '07, Japan, 2007 DOI: https://doi.org/10.1109/ICICIC.2007.132

Y. Lu, J. Yan, Y. Yam, “Model-based ECG denoising using empirical mode decomposition”, IEEE International Conference on Bioinformatics and Biomedicine, USA, 2009 DOI: https://doi.org/10.1109/BIBM.2009.14

W. Zgallai, M. Sabry-Rizk, P. Hardiman, J. O’Riordan, “Music-based bispectrum detector: a novel non-invasive detection method for overlapping fetal and mother ECG signals”, Proceedings of the 19th International Conference of the IEEE - Engineering in Medicine and Biology Society, Vol. 1, pp. 72-75, 1997

R. Swarnalatha, D. V. Prasad, “A novel technique for extraction of FECG using multi stage adaptive filtering”, Journal of Applied Scienses, Vol. 10, No. 4, pp. 319-324, 2010 DOI: https://doi.org/10.3923/jas.2010.319.324

I. I. Christov, I. K. Daskalov, “Filtering of electromyogram artefacts from the electrocardiogram”, Med. Eng. Phys., Vol. 21, pp. 731–736, 1999 DOI: https://doi.org/10.1016/S1350-4533(99)00098-3

N. V. Thakor, Y. S. Zhu, “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection”, IEEE Trans. Biomed. Eng., Vol. 38, No. 8, pp. 785–794, 1991 DOI: https://doi.org/10.1109/10.83591

P. Laguna, R. Jane, O. Meste, P. W. Poon, P. Caminal, H. Rix, N. V. Thakor, “Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: Comparison with signal averaging techniques”, IEEE Trans. Biomed. Eng., Vol. 39, No. 10, pp. 1032–1044, 1992 DOI: https://doi.org/10.1109/10.161335

R. Sameni, M. B. Shamsollahi, C. Jutten, “Filtering electrocardiogram signals using the extended Kalman filter”, in Proc. 27th Annu. Int. Conf. IEEE Eng. Medicine Biol. Soc. (EMBS), Vol. 6, pp. 5639–5642, 2005 DOI: https://doi.org/10.1109/IEMBS.2005.1615765

Y. Chen, B. Yang, J. Dong, “Time-series prediction using a local linear wavelet neural network”, Neurocomputing, Vol. 69, No. 4-6, pp. 449-465, 2006 DOI: https://doi.org/10.1016/j.neucom.2005.02.006

L. Liu, J. Jiang, “Using stationary wavelet transformation for signal denoising”, 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Vol. 4, pp. 2203-2207, China, 2011 DOI: https://doi.org/10.1109/FSKD.2011.6020040

S. Haykin, Neural networks a comprehensive foundation, MacMaster University, Hamilton, 1994

PhysioBank, The MIT-BIH noise stress test database, http://www.physionet.org/physiobank/database/nstdb/

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How to Cite

[1]
F. Bagheri, N. Ghafarnia, and F. Bahrami, “Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 3, no. 1, pp. 345–348, Feb. 2013.

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