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

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Novel Developments in Uncertainty Representation and Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 401))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Jonathan Amezcua & Patricia Melin

Authors
  1. Jonathan Amezcua
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  2. Patricia Melin
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Corresponding author

Correspondence to Jonathan Amezcua .

Editor information

Editors and Affiliations

  1. Inst. of Biophy. & Biomedical Eng., Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  2. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. Warsaw School of Information Technology, Polish Academy of Sciences, Warsaw, Poland

    Maciej Krawczak

  5. Div of Gdu Stud,CalzTecn sn,Fra.TomAqu, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  6. Intelligent Systems Laboratory, Asen Zlatarov University, Bourgas, Bulgaria

    Sotir Sotirov

  7. Intelligent Systems Laboratory, “Prof. Dr. Asen Zlatarov” University, Burgas, Bulgaria

    Evdokia Sotirova

  8. Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland

    Eulalia Szmidt

  9. Dept. of Tele. & Info. Processing, Ghent University, Gent, Belgium

    Guy De Tré

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Amezcua, J., Melin, P. (2016). Optimization of the LVQ Network Architectures with a Modular Approach for Arrhythmia Classification. In: Atanassov, K., et al. Novel Developments in Uncertainty Representation and Processing. Advances in Intelligent Systems and Computing, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-319-26211-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-26211-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26210-9

  • Online ISBN: 978-3-319-26211-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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