A modular LVQ neural network with fuzzy response integration for arrhythmia classification

In this paper, the development of a fuzzy system as the integrating unit in a classification model based on modular learning vector quantization (LVQ) neural networks is presented. The method uses a modular approach and is applied for the classification of different types of arrhythmias. The architecture is composed by three modules, each one is working with five different types of arrhythmias; the MIT-BIB arrhythmia dataset, composed by 15 classes, was used for this work. Simulation results show that the modular LVQ with fuzzy response integration is a good arrhythmia classification model.