Computer aided diagnosis of ECG data on the least square support vector machine

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dc.contributor.authorPolat, Kemal
dc.contributor.authorAkdemir, Bayram
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:26:34Z
dc.date.available2020-03-26T17:26:34Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system. (C) 2007 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.dsp.2007.05.006en_US
dc.identifier.endpage32en_US
dc.identifier.issn1051-2004en_US
dc.identifier.issn1095-4333en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage25en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.dsp.2007.05.006
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22282
dc.identifier.volume18en_US
dc.identifier.wosWOS:000252537700004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEen_US
dc.relation.ispartofDIGITAL SIGNAL PROCESSINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectECG dataseten_US
dc.subjectleast square support vector machineen_US
dc.subjectROC curvesen_US
dc.titleComputer aided diagnosis of ECG data on the least square support vector machineen_US
dc.typeArticleen_US

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