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Arrhythmia classification using local hölder exponents and support vector machine
A. Joshi, Rajshekhar, S. Chandran, S. Phadke, , B.D. Kulkarni
Published in
Volume: 3776 LNCS
Pages: 242 - 247
We propose a novel hybrid Hölder-SVM detection algorithm for arrhythmia classification. The Hölder 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. © Springer-Verlag Berlin Heidelberg 2005.
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