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

F. Bagheri, N. Ghafarnia, F. Bahrami

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


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