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|Title: ||Arrhythmia classification using local Holder exponents and support vector machine|
|Authors: ||JOSHI, A|
|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.|
|Appears in Collections:||Proceedings papers|
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