A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification

In this paper, a novel use of Kernel–Adatron (K–A) learning algorithm to aid SVM (Support Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern classifier is compared with MLP (multi-layered perceptron) using back propagation (BP) learning algorithm. The ECG signals taken from MIT-BIH arrhythmia database are used in training to classify 6 different arrhythmia, plus normal ECG. The MLP and SVM training and testing stages were carried out twice. They were first trained only with one ECG lead signal and then a second ECG lead signal was added to the training and testing datasets. The aim was to investigate its influence on training and testing performance (generalization ability) plus time of training for both classifiers. Implementation of these three criteria for evaluation of ECG signals classification will ease the problem of structural comparisons, which has not been given attention in previous research works. The results indicate that SVM in comparison to MLP is much faster in training stage and nearly seven times higher in performance, but MLP generalization ability in terms of mean square error is more than three times less. The proposed SVM method shows considerable improvement in comparison to recently reported results obtained by Osowski et al. (2008).