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/0000-0002-0403-7316en_US
dc.authoridhttps://orcid.org/0000-0002-5229-4018en_US
dc.authoridhttps://orcid.org/0000-0003-3312-992Xen_US
dc.authorscopusid36728602600en_US
dc.authorscopusid35581650500en_US
dc.authorwosidG-2829-2015en_US
dc.authorwosidK-5083-2015en_US
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.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
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.en_US
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.en_US
dc.identifier.doi10.31590/ejosat.1221450en_US
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.isoengen_US
dc.publisherEuropean Journal of Science and Technologyen_US
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisi / European Journal of Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectElectrocardiographic signalen_US
dc.subjectClassificationen_US
dc.subjectLearning algorithmsen_US
dc.titleDesigning of the Artificial Neural Network Model Trained by Using the Different Learning Algorithms to Classify the Electrocardiographic Signalsen_US
dc.title.alternativeElektrokardiyografik Sinyallerin Sınıflandırılması İçin Farklı Öğrenme Algoritmaları Kullanılarak Eğitilmiş Yapay Sinir Ağı Modelinin Tasarlanmasıen_US
dc.typearticleen_US

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