New ideas in optimization
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
A new arrhythmia clustering technique based on Ant Colony Optimization
Journal of Biomedical Informatics
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
Combining recurrent neural networks with eigenvector methods for classification of ECG beats
Digital Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Application of principal component analysis to ECG signals for automated diagnosis of cardiac health
Expert Systems with Applications: An International Journal
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Real-time CHF detection from ECG signals using a novel discretization method
Computers in Biology and Medicine
Hierarchical Particle Swarm Optimization with Ortho-Cyclic Circles
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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 Premature 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 structure 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 network without making any concessions on the classification performance.