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dc.contributor.authorGüvenir, H. Altayen_US
dc.contributor.authorAcar, Buraken_US
dc.contributor.authorDemiröz, Gülşenen_US
dc.contributor.authorÇekin, A.en_US
dc.coverage.spatialLund, Sweden, Swedenen_US
dc.date.accessioned2016-02-08T11:59:50Z
dc.date.available2016-02-08T11:59:50Z
dc.date.issued1997en_US
dc.identifier.issn0276-6574
dc.identifier.urihttp://hdl.handle.net/11693/27699
dc.descriptionDate of Conference: 7-10 Sept. 1997en_US
dc.description.abstractA new machine learning algorithm for the diagnosis of cardiac arrhythmia from standard 12 lead ECG recordings is presented. The algorithm is called VFI5 for Voting Feature Intervals. VFI5 is a supervised and inductive learning algorithm for inducing classification knowledge from examples. The input to VFI5 is a training set of records. Each record contains clinical measurements, from ECG signals and some other information such as sex, age, and weight, along with the decision of an expert cardiologist. The knowledge representation is based on a recent technique called Feature Intervals, where a concept is represented by the projections of the training cases on each feature separately. Classification in VFI5 is based on a majority voting among the class predictions made by each feature separately. The comparison of the VFI5 algorithm indicates that it outperforms other standard algorithms such as Naive Bayesian and Nearest Neighbor classifiers.en_US
dc.language.isoEnglishen_US
dc.source.titleComputers in Cardiology 1997en_US
dc.relation.isversionofhttps://doi.org/10.1109/CIC.1997.647926en_US
dc.subjectDigital signal processingen_US
dc.subjectElectrocardiographyen_US
dc.subjectKnowledge representationen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectStandardsen_US
dc.subjectStatistical methodsen_US
dc.subjectArrhythmia analysisen_US
dc.subjectNearest neighbor classifiersen_US
dc.subjectVoting feature intervalsen_US
dc.subjectCardiologyen_US
dc.titleSupervised machine learning algorithm for arrhythmia analysisen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage433en_US
dc.citation.epage436en_US
dc.identifier.doi10.1109/CIC.1997.647926en_US
dc.publisherIEEEen_US


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