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

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

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.

Keywords

Classification PSO LVQ Neural networks Arrhythmias 

References

  1. 1.
    Amezcua J., Melin P.: A modular LVQ neural network with fuzzy response integration for arrhythmia classification. In: IEEE 2014 Conference on Norbert Wiener in the 21st century, Boston, June 2014Google Scholar
  2. 2.
    Amezcua J., Melin P.: Optimization of Modular Neural Networks with the LVQ Algorithm for Classification of Arrhythmias using Particle Swarm Optimization, Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence Book 547, pp. 307–314. Springer, 2014Google Scholar
  3. 3.
    Anuradha B., Veera-Reddy V.C.: Cardiac arrhythmia classification using fuzzy classifiers. J. Theor. Appl. Inform. Technol. 353–359 (2005)Google Scholar
  4. 4.
    Biswal, B., Biswal, M., Hasan, S., Dash, P.K.: Nonstationary power signal time series data classification using LVQ classifier. Appl. Soft Comput. Elsevier 18, 158–166 (2014)CrossRefGoogle Scholar
  5. 5.
    Castillo, O., Melin, P., Ramirez, E., Soria, J.: Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system. J. Expert Syst. Appl. 39(3), 2947–2955 (2012)CrossRefGoogle Scholar
  6. 6.
    Cavuslu, M., Karakuzu, C., Karakaya, F.: Nueral Identification of dynamic systems on FPGA with improved PSO learning. Appl. Soft Comput. Elsevier 12, 2707–2718 (2012)CrossRefGoogle Scholar
  7. 7.
    Frasconi, P., Gori, M., Soda, G.: Links between LVQ and backpropagation original research article. Pattern Recogn. Lett. 18(4), 303–310 (1997)CrossRefGoogle Scholar
  8. 8.
    Grbovic M., Vucetic S.: Regression learning vector quantization. In: 2009 Ninth IEEE International Conference on Data Mining, Miami, December 2009Google Scholar
  9. 9.
    Hashemi, A.B., Meybodi, M.R.: A note on the learning automata based algorithms for adaptive parameter selection in PSO. Appl. Soft Comput. Elsevier 11, 689–705 (2011)CrossRefGoogle Scholar
  10. 10.
    Hu Y.H., Palreddy S., Tompkins W.: A patient adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 891–900 (1997)Google Scholar
  11. 11.
    Hu, Y.H., Tompkins, W., Urrusti, J.L., Afonso, V.X.: Applications of ANN for ECG signal detection and classification. J. Electrocardiol. 28, 66–73 (1994)Google Scholar
  12. 12.
    Kim J., Sik-Shin H., Shin K., Lee M.: Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomed. Eng. Online (2009)Google Scholar
  13. 13.
    Kohonen T.: Improved versions of learning vector quantization. In: International Joint Conference on Neural Networks, vol. 1, pp. 545–550, San Diego (1990)Google Scholar
  14. 14.
    Krisshna N.L., Kadetotad Deepak V., Manikantan K., Ramachandran S.: Face recognition using transform domain feature extraction and PSO-based feature selection. Appl. Soft Comput. Elsevier. 22, 141–161 (2014)Google Scholar
  15. 15.
    Learning Vector Quantization Networks. http://www.mathworks.com/help/nnet/ug/bss4b_l-15.html. Last Accessed 24 June 2014
  16. 16.
    Lee, C., Leu, Y., Yang, W.: Constructing gene regulatory networks from microarray data using GA/PSO with DTW. Appl. Soft Comput. Elsevier 12, 1115–1124 (2012)CrossRefGoogle Scholar
  17. 17.
    Martín-Valdivia, M.T., Ureña-López, L.A., García-Vega, M.: The learning vector quantization algorithm applied to automatic text classification tasks. Neural Netw. 20(6), 748–756 (2007)CrossRefzbMATHGoogle Scholar
  18. 18.
    Melin, P., Amezcua, J., Valdez, F., Castillo, O.: A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf. Sci. 279, 483–497 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    MIT-BIH Arrhythmia Database. PhysioBank, Physiologic Signal Archives for Biomedical Research. http://www.physionet.org/physiobank/database/mitdb/. Last Accessed 24 June 2014
  20. 20.
    Narayan, R., Chatterjee, D., Kumar-Goswami, S.: An application of PSO technique for harmonic elimination in a PWM inverter. Appl. Soft Comput. Elsevier 9, 1315–1320 (2009)CrossRefGoogle Scholar
  21. 21.
    Nasiri J.A., Naghibzadeh M., Yazdi H.S., Naghibzadeh B.: ECG arrhythmia classification with support vector machines and genetic algorithm. In: Third UKSim European Symposium on Computer Modeling and Simulation, 2009Google Scholar
  22. 22.
    Nouaouria, N., Boucadoum, M.: Improved global-best particle swarm optimization algorithm with mixed-attribute data classification capability. Appl. Soft Comput. Elsevier 21, 554–567 (2014)CrossRefGoogle Scholar
  23. 23.
    Owis M.I., Abou-Zied A.H., Youssef A.M., Kadah Y.M.: Study of features based on non-linear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans. Biomed. Eng. 49(7) (2002)Google Scholar
  24. 24.
    Pang-Ning T., Steinbach M., Kumar V.: Introduction to Data Mining, pp. 145–148. Pearson Addison Wesley (2006)Google Scholar
  25. 25.
    Pedreira C.: Learning vector quantization with training data selection. IEEE Trans. Pattern Anal. Mach. Intell. 28, 157–162 (2006)Google Scholar
  26. 26.
    Torrecilla, J.S., Rojo, E., Oliet, M., Domínguez, J.C., Rodríguez, F.: Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils and detect adulterations of extra virgin olive oil. Comput. Aided Chem. Eng. 28, 313–318 (2010)CrossRefGoogle Scholar
  27. 27.
    Tsipouras M.G., Fotiadis D.I., Sideris D.: An arrhythmia classification system based on the RR-interval signal. Artif. Intell. Med. 237–250 (2005)Google Scholar
  28. 28.
    Vellasques, E., Sabourin, R., Granger, E.: Fast intelligent watermarking of heterogeneous image streams through mixture modeling of PSO populations. Appl. Soft Comput. Elsevier 13, 3130–3148 (2013)CrossRefGoogle Scholar
  29. 29.
    Vieira, S., Mendoca, L., Farinha, G., Souza, J.: Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Appl. Soft Comput. Elsevier 13, 3494–3504 (2013)CrossRefGoogle Scholar
  30. 30.
    Xinye Z., Jubai A., ZhiFeng Y.: A method of LVQ network to detect vehicle based on morphology. In: 2009 WRI Global Congress on Intelligent Systems, Xiamen, China, May 2009Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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