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Arrhythmia Classification Using Local Hölder Exponents and Support Vector Machine.
- Source :
- Pattern Recognition & Machine Intelligence; 2005, p242-247, 6p
- Publication Year :
- 2005
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540305064
- Database :
- Complementary Index
- Journal :
- Pattern Recognition & Machine Intelligence
- Publication Type :
- Book
- Accession number :
- 32965648
- Full Text :
- https://doi.org/10.1007/11590316_33