Arrhythmia classification using local Holder exponents and support vector machine - Joshi, Aniruddha; Rajshekhar; Chandran, S.; Phadke, S.; Jayaraman, Valadi K.; Kulkarni, B. D. (1st International Conference on Pattern Recognition and Machine Intelligence, 2005)
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.