Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6708
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dc.contributor.authorGüler, İnan-
dc.contributor.authorÜbeyli, Derya Elif-
dc.date.accessioned2021-09-11T15:43:16Z-
dc.date.available2021-09-11T15:43:16Z-
dc.date.issued2005en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2004.10.008-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6708-
dc.description.abstractArtificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. 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 multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time-frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features. (C) 2004 Elsevier Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfeature saliencyen_US
dc.subjectsignal-to-noise ratioen_US
dc.subjectclassification accuracyen_US
dc.subjectECG beats classificationen_US
dc.titleFeature Saliency Using Signal-To Ratios in Automated Diagnostic Systems Developed 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.volume28en_US
dc.identifier.issue2en_US
dc.identifier.startpage295en_US
dc.identifier.endpage304en_US
dc.identifier.wosWOS:000226572200010en_US
dc.identifier.scopus2-s2.0-11244327794en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.eswa.2004.10.008-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
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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