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

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

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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 informa...

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Bibliographic Details
Main Authors: F. Bagheri, N. Ghafarnia, F. Bahrami
Format: Article
Language:English
Published: D. G. Pylarinos 2013-02-01
Series:Engineering, Technology & Applied Science Research
Subjects:
Hopfield Neural Networks
ECG signal modeling
noise reduction
Online Access:http://www.etasr.com/index.php/ETASR/article/view/243/156
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spelling doaj-54460a687eb6408dad32010beddab91c2020-12-02T10:44:42ZengD. G. PylarinosEngineering, Technology & Applied Science Research1792-80362013-02-0131345348Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural NetworksF. BagheriN. GhafarniaF. BahramiThe 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.http://www.etasr.com/index.php/ETASR/article/view/243/156Hopfield Neural NetworksECG signal modelingnoise reduction
collection DOAJ
language English
format Article
sources DOAJ
author F. Bagheri
N. Ghafarnia
F. Bahrami
spellingShingle F. Bagheri
N. Ghafarnia
F. Bahrami
Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
Engineering, Technology & Applied Science Research
Hopfield Neural Networks
ECG signal modeling
noise reduction
author_facet F. Bagheri
N. Ghafarnia
F. Bahrami
author_sort F. Bagheri
title Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
title_short Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
title_full Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
title_fullStr Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
title_full_unstemmed Electrocardiogram (ECG) Signal Modeling and Noise Reduction Using Hopfield Neural Networks
title_sort electrocardiogram (ecg) signal modeling and noise reduction using hopfield neural networks
publisher D. G. Pylarinos
series Engineering, Technology & Applied Science Research
issn 1792-8036
publishDate 2013-02-01
description 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.
topic Hopfield Neural Networks
ECG signal modeling
noise reduction
url http://www.etasr.com/index.php/ETASR/article/view/243/156
work_keys_str_mv AT fbagheri electrocardiogramecgsignalmodelingandnoisereductionusinghopfieldneuralnetworks
AT nghafarnia electrocardiogramecgsignalmodelingandnoisereductionusinghopfieldneuralnetworks
AT fbahrami electrocardiogramecgsignalmodelingandnoisereductionusinghopfieldneuralnetworks
_version_ 1724407090987925504

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