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


  • 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


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.


Hopfield Neural Networks, ECG signal modeling, noise reduction


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How to Cite

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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