Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

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dc.contributor.authorAcir, N
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2005
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this paper, we present a new system for the classification of electrocardiogram ( ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.
dc.identifier.doi10.1007/s00521-005-0466-z
dc.identifier.endpage309
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue4
dc.identifier.scopus2-s2.0-27744453578
dc.identifier.scopusqualityQ1
dc.identifier.startpage299
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-005-0466-z
dc.identifier.urihttps://hdl.handle.net/11480/5563
dc.identifier.volume14
dc.identifier.wosWOS:000232985200004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAcir, N
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLSSVM
dc.subjectECG beat classification
dc.subjectfeature selection
dc.subjectdynamic programming
dc.subjectbackpropagation MLP
dc.titleClassification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
dc.typeArticle

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