Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6870
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dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:43:59Z-
dc.date.available2021-09-11T15:43:59Z-
dc.date.issued2008en_US
dc.identifier.issn0266-4720-
dc.identifier.issn1468-0394-
dc.identifier.urihttps://doi.org/10.1111/j.1468-0394.2008.00444.x-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6870-
dc.description.abstractMixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time-frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofExpert Systemsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmixture of expertsen_US
dc.subjectexpectation maximization algorithmen_US
dc.subjectclassification accuracyen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectECG beats classificationen_US
dc.titleImplementing wavelet transform/mixture of experts network for analysis of electrocardiogram 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.volume25en_US
dc.identifier.issue2en_US
dc.identifier.startpage150en_US
dc.identifier.endpage162en_US
dc.identifier.wosWOS:000255060400005en_US
dc.identifier.scopus2-s2.0-42949114312en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1111/j.1468-0394.2008.00444.x-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
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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