A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

Basit Öğe Kaydı
dc.contributor.authorCeylan, Rahime
dc.contributor.authorOzbay, Yuksel
dc.contributor.authorKarlik, Bekir
dc.date.accessioned2020-03-26T17:37:45Z
dc.date.available2020-03-26T17:37:45Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents an improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of a combined Fuzzy Clustering Neural Network Algorithm for Classification of ECG Arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network. Type-2 fuzzy c-means clustering is used to improve performance of neural network. The aim of improving classifier's performance is to constitute the best classification system with high accuracy rate for ECG beats. Ten types of ECG arrhythmias (normal beat, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter) obtained from MIT-BIH database were analyzed. However, the presented structure was tested by experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The classification accuracy of an improved classifier in training and testing, namely Type-2 Fuzzy Clustering Neural Network (T2FCNN), was compared with neural network (NN) and fuzzy clustering neural network (FCNN). In T2FCNN architecture, decision making has two stages: forming of the new training set obtained by selection of the best arrhythmia for each arrhythmia class using T2FCM and classification using neural network trained on the new training set. The results are demonstrated that the proposed diagnostic systems achieved high (99%) accuracy rate. (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk University [07101021]en_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects under Project No: 07101021.en_US
dc.identifier.doi10.1016/j.eswa.2008.08.028en_US
dc.identifier.endpage6726en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6721en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.08.028
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23224
dc.identifier.volume36en_US
dc.identifier.wosWOS:000263817100119en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectType-2 fuzzy c-means clusteringen_US
dc.subjectECGen_US
dc.subjectNeural networken_US
dc.titleA novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural networken_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu