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Arrhythmia classification using local Holder exponents and support vector machine

DSpace at IIT Bombay

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Title Arrhythmia classification using local Holder exponents and support vector machine
 
Creator JOSHI, A
RAJSHEKHAR
CHANDRAN, S
PHADKE, S
JAYARAMAN, VK
KULKARNI, BD
 
Subject wavelets
 
Description 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.
 
Publisher SPRINGER-VERLAG BERLIN
 
Date 2011-10-23T18:26:03Z
2011-12-15T09:11:17Z
2011-10-23T18:26:03Z
2011-12-15T09:11:17Z
2005
 
Type Article; Proceedings Paper
 
Identifier PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS,3776,242-247
3-540-30506-8
0302-9743
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15200
http://hdl.handle.net/100/1969
 
Source 1st International Conference on Pattern Recognition and Machine Intelligence,Kolkata, INDIA,DEC 20-22, 2005
 
Language English