Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

Abstract Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signa...

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Autores principales:	Marwa Fradi, Lazhar Khriji, Mohsen Machhout, Abdulnasir Hossen
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
Acceso en línea:	https://doaj.org/article/e2b4b6b845624d5d8e4a0eb004f1ce79
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spelling	oai:doaj.org-article:e2b4b6b845624d5d8e4a0eb004f1ce792021-11-22T16:30:22ZAutomatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks2631-768010.1049/smc2.12003https://doaj.org/article/e2b4b6b845624d5d8e4a0eb004f1ce792021-03-01T00:00:00Zhttps://doi.org/10.1049/smc2.12003https://doaj.org/toc/2631-7680Abstract Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi‐stage technique. The first stage combines an R–R peak extraction with a low‐pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network‐based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT‐BIH‐database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1‐score of 0.99 is obtained. Experimental results show a high achievement compared to the state‐of‐the‐art models where the implementation of GPU confirms the low computational complexity of the system.Marwa FradiLazhar KhrijiMohsen MachhoutAbdulnasir HossenWileyarticleEngineering (General). Civil engineering (General)TA1-2040City planningHT165.5-169.9ENIET Smart Cities, Vol 3, Iss 1, Pp 3-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9 Marwa Fradi Lazhar Khriji Mohsen Machhout Abdulnasir Hossen Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
description	Abstract Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi‐stage technique. The first stage combines an R–R peak extraction with a low‐pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network‐based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT‐BIH‐database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1‐score of 0.99 is obtained. Experimental results show a high achievement compared to the state‐of‐the‐art models where the implementation of GPU confirms the low computational complexity of the system.
format	article
author	Marwa Fradi Lazhar Khriji Mohsen Machhout Abdulnasir Hossen
author_facet	Marwa Fradi Lazhar Khriji Mohsen Machhout Abdulnasir Hossen
author_sort	Marwa Fradi
title	Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
title_short	Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
title_full	Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
title_fullStr	Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
title_full_unstemmed	Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
title_sort	automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/e2b4b6b845624d5d8e4a0eb004f1ce79
work_keys_str_mv	AT marwafradi automaticheartdiseaseclassdetectionusingconvolutionalneuralnetworkarchitecturebasedvariousoptimizersnetworks AT lazharkhriji automaticheartdiseaseclassdetectionusingconvolutionalneuralnetworkarchitecturebasedvariousoptimizersnetworks AT mohsenmachhout automaticheartdiseaseclassdetectionusingconvolutionalneuralnetworkarchitecturebasedvariousoptimizersnetworks AT abdulnasirhossen automaticheartdiseaseclassdetectionusingconvolutionalneuralnetworkarchitecturebasedvariousoptimizersnetworks
_version_	1718417528368136192

Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

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