ECG beat classification using particle swarm optimization and radial basis function neural network

摘要

This paper presents a method for electrocardiogram (ECG) beat classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Prema-ture Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For classification stage of the extracted features, a RBFNN struc-ture which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of net-work without making any concessions on the classification performance.