Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

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dc.contributor.authorPolat K.
dc.contributor.authorGüneş S.
dc.date.accessioned2020-03-26T17:19:08Z
dc.date.available2020-03-26T17:19:08Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractChanges in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50-50% of training-test dataset, 70-30% of training-test dataset and 80-20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too. © 2006 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorship5401069en_US
dc.description.sponsorshipThis study is supported by the Scientific Research Projects of Selcuk University (Project no: 05401069).en_US
dc.identifier.doi10.1016/j.amc.2006.08.020en_US
dc.identifier.endpage906en_US
dc.identifier.issn0096-3003en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage898en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.amc.2006.08.020
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21811
dc.identifier.volume186en_US
dc.identifier.wosWOS:000245999000095en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofApplied Mathematics and Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectECG Arrhythmiaen_US
dc.subjectLeast square support vector machine (LSSVM)en_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.subjectROC curvesen_US
dc.titleDetection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machineen_US
dc.typeArticleen_US

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