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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/1969

Title: Arrhythmia classification using local Holder exponents and support vector machine
Authors: JOSHI, A
RAJSHEKHAR
CHANDRAN, S
PHADKE, S
JAYARAMAN, VK
KULKARNI, BD
Keywords: wavelets
Issue Date: 2005
Publisher: SPRINGER-VERLAG BERLIN
Citation: PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS,3776,242-247
Abstract: We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.
URI: http://dspace.library.iitb.ac.in/xmlui/handle/10054/15200
http://hdl.handle.net/100/1969
ISBN: 3-540-30506-8
ISSN: 0302-9743
Appears in Collections:Proceedings papers

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