Optimization of the LVQ Network Architectures with a Modular Approach for Arrhythmia Classification

  • Jonathan AmezcuaEmail author
  • Patricia Melin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 401)


In this paper, the optimization of LVQ neural networks with modular approach is presented for classification of arrhythmias, using particle swarm optimization. This work focuses only in the optimization of the number of modules and the number of cluster centers. Other parameters, such as the learning rate or number of epochs are static values and are not optimized. Here, the MIT-BIH arrhythmia database with 15 classes was used. Results show that using 5 modules architecture could be a good approach for classification of arrhythmias.


Classification PSO LVQ Neural networks Arrhythmias 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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