Feature selection consideration for multi-class cardiac arrhythmia classification

Conference proceedings article

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Publication Details

Author list: Thanawattano C., Yingthawornsuk T.

Publisher: Hindawi

Publication year: 2010

Start page: 1175

End page: 1178

Number of pages: 4

ISBN: 9781424474530

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78751500946&partnerID=40&md5=b8148e1a8b4e64585f26b2e1726df629

Languages: English-Great Britain (EN-GB)

Abstract

This paper presents the performance of support vector machine to classify the multi-class arrhythmia dataset by pre-selecting sets of feature that best suit the training data set in two-class fashion. By allowing freedom of feature dimension selection in different grouping in classification procedure, the classification performance is comparable to one that uses constant feature dimension but with less computational complexity. ฉICROS.

Keywords

Electrocardiography, Principal component analysis