Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6200
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dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:35:16Z-
dc.date.available2021-09-11T15:35:16Z-
dc.date.issued2009en_US
dc.identifier.issn0169-2607-
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2008.10.012-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6200-
dc.description.abstractThis paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers, To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. (C) 2008 Elsevier Ireland Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods And Programs In Biomedicineen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectFuzzy logicen_US
dc.subjectLyapunov exponenten_US
dc.subjectElectrocardiogram (ECG) signalsen_US
dc.titleAdaptive Neuro-Fuzzy Inference System for Classification of Ecg Signals Using Lyapunov Exponentsen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume93en_US
dc.identifier.issue3en_US
dc.identifier.startpage313en_US
dc.identifier.endpage321en_US
dc.identifier.wosWOS:000263938100010en_US
dc.identifier.scopus2-s2.0-59149084224en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.pmid19084286en_US
dc.identifier.doi10.1016/j.cmpb.2008.10.012-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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