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Hybrid SVM for multiclass arrhythmia classification
A.J. Joshi, S. Chandran, , B.D. Kulkarni
Published in
Pages: 287 - 290
Automatically classifying ECG recordings for Malignant Ventricular Arrhythmia is fraught with several diffi-culties. Even normal ECG signals exhibit only quasi-periodic nature, and contain various irregularities. The key to more accurate detection is the use of position, and amount of local singularities in the signals. In this paper, we propose a Hölder-SVM detection algorithm using a novel hybrid arrangement of binary and multiclass SVMs designed to take care of class imbalance rampant in biomedical signals. As a result, we significantly reduce the number of false negatives - patients falsely classified as normal. We used the MIT-BIH Arrhythmia database for seven different arrhythmias. We compare our hybrid SVM with a suitable conventional SVM, and show better results.We also use the new arrangement for features proposed earlier, and demonstrate the gain in accuracy. Our concept of hybrid SVM is applicable to a wide variety of multiclass classification problems. © 2009 IEEE.
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