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.
1st International Conference on Pattern Recognition and Machine Intelligence, Statist Inst Kolkata, Kolkata, INDIA, DEC 20-22, 2005