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

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 producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. We first experiment and compare two common techniques: support vector machines (SVM) and logistic regression (LR). Then, we propose a two- stage serial fusion classifier system based on SVM's rejection option. We relate the SVM's distance outputs to confidence measure and reject to classify ambiguous samples with first level SVM classifier. A non-symmetric thresholding scheme is applied: two different rejection distance thresholds have been defined for positive and negative ECG samples. The rejected samples have been forwarded to a second stage LR classifier. Finally we choose a way to combine the classifiers decisions to obtain a final decision rule. The experiments have been performed on UCI Arrhythmia Database.