A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias

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dc.contributor.authorÖzbay, Yüksel
dc.contributor.authorCeylan, Rahime
dc.contributor.authorKarlik, Bekir
dc.date.accessioned2020-03-26T17:02:56Z
dc.date.available2020-03-26T17:02:56Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAccurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis. The ECG signals are taken from MIT-BIH ECG database, which are used to classify 10 different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that a new proposed FCNN architecture can generalize better than ordinary MLP architecture and also learn better and faster. The advantage of proposed structure is a result of decreasing the number of segments by grouping similar segments in training data with fuzzy c-means clustering.en_US
dc.identifier.citationÖzbay, Y., Ceylan, R., Karlik, B., (2006). A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias. Computers in Biology and Medicine, 36(4), 376-388. Doi: 10.1016/j.compbiomed.2005.01.006
dc.identifier.doi10.1016/j.compbiomed.2005.01.006en_US
dc.identifier.endpage388en_US
dc.identifier.issn0010-4825en_US
dc.identifier.issn1879-0534en_US
dc.identifier.issue4en_US
dc.identifier.pmid15878480en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage376en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compbiomed.2005.01.006
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20285
dc.identifier.volume36en_US
dc.identifier.wosWOS:000236197500004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorÖzbay, Yüksel
dc.institutionauthorCeylan, Rahime
dc.language.isoenen_US
dc.publisherPergamon-elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectfuzzy clusteringen_US
dc.subjectFuzzy c-meansen_US
dc.subjectEcgen_US
dc.subjectArrhythmiaen_US
dc.subjectNeural networken_US
dc.subjectPattern recognitionen_US
dc.subjectMultilayer perceptronen_US
dc.titleA Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmiasen_US
dc.typeArticleen_US

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