A robust and reliable ECG pattern classification using QRS morphological features and ANN

This paper describes electrocardiogram (ECG) pattern classification using QRS morphological features and the artificial neural network. Four types of ECG patterns were chosen from the MIT-BIH database to be classified, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to reduce the feature vector size considerably to make the training process faster, and realize a simple but effective ECG heartbeat extraction scheme. Three types of artificial neural network models, MLP, RBF neural networks and support vector machine were separately trained and tested for ECG pattern classification and the experimental results of the different models have been compared. The MLP network exhibited the best performance and reached an overall test accuracy of 99.65%, while, RBF and SVM network reached 99.1% and 99.5% respectively. The performance of these classifiers was also evaluated in presence of additive white Gaussian noise. MLP network was found to be more robust in this respect.