Automatic Classification of Heartbeats using Neural Network Classifier based on a Bayesian Framework

Abstract

This paper presents a method of automatic processing the electrocardiogram (ECG) signal for the classification of heart beats. Data were obtained from 48 records of the MIT-BIH arrhythmia database (only one lead). Five types of arrhythmic beats were classified using our method, Premature Ventricular Conduction beat (PVC), Atrial Premature Conduction beat (APC), Right Bundle Branch Block beat (RBBB), Left Bundle Branch Block beat (LBBB), and Paced Rhythm Beat (PRB), in addition to the Normal Beat (NB). A learning dataset for the neural network was obtained from a five records set (124, 214, 111, 100, and 107) which were manually classified using MIT-BIH Arrhythmia Database Directory and documentation, taking advantage of the professional experience of a cardiologist. Feature set was based on ECG morphology and time intervals. Our system resulted in a minimal sensitivity of 86% and minimal specificity of 90%