Recognition of ECG Patterns Using Artificial Neural Network

Abstract

In this paper, the artificial neural network method was used for electrocardiogram (ECG) pattern recognition. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. ECG morphology and R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Three types of artificial neural network models, SOM, BP and LVQ networks were separately trained and tested for ECG pattern recognition and the experimental results of the different models have been compared. The SOM network exhibited the best performance and reached an overall accuracy of 95.5%, and the BP and LVQ network reached 92.5% and 91.5%