Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network

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dc.contributor.authorCeylan, Rahime
dc.contributor.authorOzbay, Yuksel
dc.date.accessioned2020-03-26T17:17:07Z
dc.date.available2020-03-26T17:17:07Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractPrincipal component analysis (PCA) and wavelet transform (WT) are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this paper, four different structures, FCM-NN, PCA-NN, FCM-PCA-NN and WT-NN, are formed by using these two techniques and fuzzy c-means clustering. In addition, FCM-PCA-NN is the new method proposed in this paper for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-layered perceptron (MLP) with backpropagation training algorithm. The ECG signals taken from MIT-BIH ECG database, are used in training to classify 10 different arrhythmias. 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. Before testing, the proposed structures are trained by backpropagation algorithm. All of the structures are tested by using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that FCM-PCA-NN structure can generalize better than PCA-NN and is faster than NN, FCM-NN and WT-NN. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2006.05.014en_US
dc.identifier.endpage295en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage286en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2006.05.014
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21276
dc.identifier.volume33en_US
dc.identifier.wosWOS:000244344000003en_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.subjectprincipal component analysisen_US
dc.subjectwavelet transformen_US
dc.subjectfuzzy c-means clusteringen_US
dc.subjectECGen_US
dc.subjectarrhythmiaen_US
dc.subjectartificial neural networken_US
dc.titleComparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural networken_US
dc.typeArticleen_US

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