Arrhythmia Classification Using Serial Fusion of Support Vector Machines and Logistic Regression

Abstract

Reliable arrhythmia classification from complex electrocardiogram (ECG) signals is one of the most challenging pattern recognition problems. Several individual classifiers have been studied in the ECG domain. Also, parallel and serial classifier fusion systems have been proposed to increase the reliability. In this study, we are mainly interested in… (More)

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@article{Uyar2007ArrhythmiaCU, title={Arrhythmia Classification Using Serial Fusion of Support Vector Machines and Logistic Regression}, author={A. Sx0327ima Uyar and Fatma Gurgen}, journal={2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications}, year={2007}, pages={560-565} }