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.
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Keywords
- Support Vector Machine
- Wavelet Transformation
- Normal Sinus Rhythm
- Multifractal Formalism
- Multiclass Support Vector Machine
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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: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_33
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DOI: https://doi.org/10.1007/11590316_33
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