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.
CITATION STYLE
Joshi, A., Rajshekhar, Chandran, S., Phadke, S., Jayaraman, V. K., & Kulkarni, B. D. (2005). Arrhythmia classification using local hölder exponents and support vector machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 242–247). https://doi.org/10.1007/11590316_33
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