Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks

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dc.authorscopusid57203169526
dc.authorscopusid8945093900
dc.authorscopusid15077642900
dc.authorscopusid57204779064
dc.contributor.authorSenturk, Umit
dc.contributor.authorPolat, Kemal
dc.contributor.authorYucedag, Ibrahim
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2024-09-25T19:45:27Z
dc.date.available2024-09-25T19:45:27Z
dc.date.issued2023
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractA noninvasive technique, electrocardiography (ECG), is crucial in the detection and treatment of cardiovascular disorders. Periodic beats make up ECG signals, and these beats vary based on the internal dynamics of the cardiovascular system. It is highly challenging to categorize ECG beats that can be understood by specialists in the area. In recent years, attempts have been made to use artificial intelligence programs in conjunction with a database of specified ECG beats to identify autonomous cardiovascular illness. In this investigation, the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Database were used to attempt to classify arrhythmias. A one-dimensional convolutional neural network (CNN, or ConvNet) model was used to estimate the arrhythmia classes. The ECG beats defined in the database are divided into five classes: normal (N), supraventricular premature (S), premature ventricular contraction (V), ventricular and normal fusion (F), and Unclassifiable beats (U). The utilized one-dimensional convolutional neural network (1D-CNN VGG16) model’s average accuracy in classifying arrhythmias was found to be 99.12%. With the aid of this study, a system of experts has been built to assist specialized doctors in the healthcare system. The high estimating success of the used model will help in combining the right diagnosis with the right therapy and saving lives. © 2023 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/B978-0-323-96129-5.00002-0
dc.identifier.endpage101en_US
dc.identifier.isbn978-032396129-5
dc.identifier.isbn978-032399681-5
dc.identifier.scopus2-s2.0-85161110068en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage83en_US
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-96129-5.00002-0
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13029
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofDiagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methodsen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectArrhythmia classificationen_US
dc.subjectcardiovascular diseaseen_US
dc.subjectECG signal beatsen_US
dc.subjectone dimension CNNen_US
dc.subjecttime series CNNen_US
dc.titleArrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networksen_US
dc.typeBook Chapteren_US

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