Classification of arrhythmias using modular architecture of LVQ neural network and type 2 fuzzy logic

Abstract

In this paper, a new model for arrhythmia classification using a modular LVQ neural network architecture and a type-2 fuzzy system is presented. This work focuses on the implementation of a type-2 fuzzy system to determine the shortest distance in a LVQ neural network competitive layer. In this work, the MIT-BIH arrhythmia database with 15 classes was used. Results show that using five modules architecture could be a good approach for classification of arrhythmias.