Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7772
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dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:59:39Z-
dc.date.available2021-09-11T15:59:39Z-
dc.date.issued2008en_US
dc.identifier.issn1051-2004-
dc.identifier.issn1095-4333-
dc.identifier.urihttps://doi.org/10.1016/j.dsp.2007.05.005-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7772-
dc.description.abstractIn this paper, implementation of automated diagnostic systems with diverse and composite features for electrocardiogram (ECG) beats was presented and their accuracies were determined. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures were searched for ECG beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features were compared. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems. (C) 2007 Elsevier Inc. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofDigital Signal Processingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecteigenvector methodsen_US
dc.subjectelectrocardiogram (ECG) beatsen_US
dc.subjectmultilayer perceptron neural network (MLPNN)en_US
dc.subjectcombined neural network (CNN)en_US
dc.subjectmixture of experts (ME)en_US
dc.subjectmodified mixture of experts (MME)en_US
dc.subjectprobabilistic neural network (PNN)en_US
dc.subjectsupport vector machine (SVM)en_US
dc.titleUsage of Eigenvector Methods in Implementation of Automated Diagnostic Systems for Ecg Beatsen_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.volume18en_US
dc.identifier.issue1en_US
dc.identifier.startpage33en_US
dc.identifier.endpage48en_US
dc.identifier.wosWOS:000252537700005en_US
dc.identifier.scopus2-s2.0-36549032224en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.dsp.2007.05.005-
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
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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