Designing of the Artificial Neural Network Model Trained by Using the Different Learning Algorithms to Classify the Electrocardiographic Signals

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dc.authoridhttps://orcid.org/0000-0002-0403-7316
dc.authoridhttps://orcid.org/0000-0002-5229-4018
dc.authoridhttps://orcid.org/0000-0003-3312-992X
dc.authorscopusid36728602600
dc.authorscopusid35581650500
dc.authorwosidG-2829-2015
dc.authorwosidK-5083-2015
dc.contributor.authorÇelik, İbrahim
dc.contributor.authorÜstün, Deniz
dc.contributor.authorAkdağlı, Ali
dc.date.accessioned2023-08-31T10:04:42Z
dc.date.available2023-08-31T10:04:42Z
dc.date.issued2022
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAn artificial neural network model trained by using various learning algorithms is designed to classify the electrocardiographic signals in this study. The model of artificial neural network is constructed on the structure consisting of a multilayered perceptron based on the feed forward back propagation. A data pool is built by using a dataset consists of 66 electrocardiographic data’s taken from the MIT BIH arrhythmia database to perform the training and testing processes of artificial neural network model. The training process of artificial neural network model is performed with 46 electrocardiographic data and then the accuracy of the model is tested via 20 electrocardiographic data. The artificial neural network is trained by 3 different learning algorithms to achieve a robust model. The performance of the learning algorithms used for training the model of the artificial neural network is evaluated according to percentage error. It illustrates that the artificial neural network model trained by Levenberg–Marquardt learning algorithm obtains the better classification result than other learning algorithms. The proposed artificial neural network model can be successfully used to classify the electrocardiographic signals.
dc.identifier.citationCelik, I., Ustun, D., ve Akdagli, A. (2022). Designing of the Artificial Neural Network Model Trained by Using the Different Learning Algorithms to Classify the Electrocardiographic Signals. European Journal of Science and Technology, (45), 74-78.
dc.identifier.doi10.31590/ejosat.1221450
dc.identifier.endpage78en_US
dc.identifier.issue45en_US
dc.identifier.startpage74en_US
dc.identifier.urihttps://doi.org/10.31590/ejosat.1221450
dc.identifier.urihttps://hdl.handle.net/20.500.13099/175
dc.institutionauthorÜstün, Deniz
dc.language.isoen
dc.publisherEuropean Journal of Science and Technology
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisi / European Journal of Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial neural networks
dc.subjectElectrocardiographic signal
dc.subjectClassification
dc.subjectLearning algorithms
dc.titleDesigning of the Artificial Neural Network Model Trained by Using the Different Learning Algorithms to Classify the Electrocardiographic Signals
dc.title.alternativeElektrokardiyografik Sinyallerin Sınıflandırılması İçin Farklı Öğrenme Algoritmaları Kullanılarak Eğitilmiş Yapay Sinir Ağı Modelinin Tasarlanması
dc.typeArticle

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