Arrhythmia classification using local Holder exponents and support vector machine
| Title | Arrhythmia classification using local Holder exponents and support vector machine |
| Publication Type | Conference Paper |
| Year of Publication | 2005 |
| Authors | Joshi, A, Rajshekhar,, Chandran, S, Phadke, S, Jayaraman, VK, Kulkarni, BD |
| Editor | Pal, SK, Bandyopadhyay, S, Biswas, S |
| Conference Name | 1st International Conference on Pattern Recognition and Machine Intelligence |
| Date Published | DEC |
| Publisher | Springer-Verlag Berlin, Heidelberger Platz 3, D-14197 Berlin, Germany |
| Conference Location | Statist Inst. Kolkata, India |
| ISBN Number | 3-540-30506-8 |
| 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. |
Divison category:
Chemical Engineering & Process Development