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- W2280006739 abstract "In this paper we present an arrhythmia classification method using Heart Rate Variability (HRV) signal features and Support Vector Machine (SVM) Classifier. Eight linear and nonlinear features are extracted from the HRV signals and a subset of these features is selected using the Improved Forward Floating Selection (IFFS) method to train the classifier. BSVM is a classification algorithm based on SVM which is able to solve the multi-class classification problems. here, five types of the most life threatening cardiac arrhythmias including normal sinus rhythm, atrial fibrillation, ventricular fibrillation, ventricular bigeminy and sick sinus syndrome can be discriminated by BSVM and selected features with the average accuracy of 99.78%." @default.
- W2280006739 created "2016-06-24" @default.
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- W2280006739 date "2009-01-01" @default.
- W2280006739 modified "2023-09-26" @default.
- W2280006739 title "An Arrhythmia Classification Method Based on Selected Features of Heart Rate Variability Signal and Support Vector Machine-Based Classifier" @default.
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- W2280006739 doi "https://doi.org/10.1007/978-3-642-03882-2_512" @default.
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