Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7462
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dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:57:10Z-
dc.date.available2021-09-11T15:57:10Z-
dc.date.issued2008en_US
dc.identifier.issn1210-0552-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7462-
dc.description.abstractMedical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. Features are used to represent patterns with minimal loss of important information. The feature vector, which is comprised of the set of all features used for describing a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of recurrent neural networks (RNNs) used in the classification of electrocardiogram (ECG) signals. In order to extract features representing four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database, eigenvector methods were used. The RNNs used in the ECG beats classification were trained for the SNR screening method. The results of the application of the SNR screening method to the ECG signals demonstrated that classification accuracies of the RNNs with salient input features are higher than those of the RNNs with salient and non-salient input features.en_US
dc.language.isoenen_US
dc.publisherAcad Sciences Czech Republic, Inst Computer Scienceen_US
dc.relation.ispartofNeural Network Worlden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature saliencyen_US
dc.subjectsignal-to-noise ratioen_US
dc.subjecteigenvector methodsen_US
dc.subjectelectrocardiogram (ECG) beats classificationen_US
dc.titleSIGNAL-TO-NOISE RATIOS FOR MEASURING SALIENCY OF FEATURES EXTRACTED BY EIGENVECTOR METHODS FROM ECG SIGNALSen_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.issue5en_US
dc.identifier.startpage381en_US
dc.identifier.endpage400en_US
dc.identifier.wosWOS:000260888500003en_US
dc.identifier.scopus2-s2.0-56349090646en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
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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