Artificial Metaplasticity: Application to MIT-BIH Arrhythmias Database

  • Santiago Torres-Alegre
  • Juan Fombellida
  • Juan Antonio Piñuela-Izquierdo
  • Diego Andina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)


Artificial Metaplasticity are Artificial Learning Algorithms based on modelling higher level properties of biological plasticity: the plasticity of plasticity itself, so called Biological Metaplasticity. Artificial Metaplasticity aims to obtain general improvements in Machine Learning based on the experts generally accepted hypothesis that the Metaplasticity of neurons in Biological Brains is of high relevance in Biological Learning. Artificial Metaplasticity Multilayer Perceptron (AMMLP) is the application of Metaplasticity in MLPs ANNs trying to improve uniform plasticity of the Backpropagation algorithm. In this paper two different AMMLP algorithms are applied to the MIT-BIH electro cardiograms database and results are compared in terms of network performance and error evolution.


Metaplasticity Plasticity MLP MMLP AMP MIT-BIH ECGs Feature Extraction Machine Learning Artificial Neural Network 


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  1. 1.
    Benchaib, Y., Marcano-Cedeño, A., Torres-Alegre, S., Andina, D.: Application of Artificial Metaplasticity Neural Networks to Cardiac Arrhythmias Classification. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F. J. (eds.) IWINAC 2013, Part I. LNCS, vol. 7930, pp. 181–190. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Andina, D., Alvarez-Vellisco, A., Jevtic, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intelligent Automation and Soft Computing; Special Issue in Signal Processing and Soft Computing 15(4), 681–694 (2009)Google Scholar
  3. 3.
    Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001)CrossRefGoogle Scholar
  4. 4.
    Ropero-Pelaez, J., Andina, D.: Do biological synapses perform probabilistic computations? Neurocomputing (2012),
  5. 5.
    Abraham, W.C.: Activity-dependent regulation of synaptic plasticity (metaplasticity) in the hippocampus. In: The Hippocampus: Functions and Clinical Relevance, pp. 15–26. Elsevier Science, Amsterdam (1996)Google Scholar
  6. 6.
    Kinto, E.A., Del Moral Hernandez, E., Marcano, A., Ropero Peláez, F.J.: A preliminary neural model for movement direction recognition based on biologically plausible plasticity rules. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 628–636. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Marcano-Cedeño, A., Quintanilla-Dominguez, J., Andina, D.: Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing 74(8), 1243–1250 (2011)CrossRefGoogle Scholar
  8. 8.
    Leung, H., Haykin, S.: The complex backpropagation algorithm. IEEE Transactions on Signal Processing 39(9), 2101–2104 (1991)CrossRefGoogle Scholar
  9. 9.
    Hu, Y.H., Palreddy, S., Tompkins, W.J.: A patient- adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering 44(9), 891–900 (1997)CrossRefGoogle Scholar
  10. 10.
    Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering 46(2), 179–185 (1999)CrossRefGoogle Scholar
  11. 11.
    Owis, M.I., Youssef, A.B.M., Kadah, Y.M.: Characterization of ECG signals based on blind source separation. Medical and Biological Engineering and Computing 40(5), 557–564 (2002)CrossRefGoogle Scholar
  12. 12.
    Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with Applications 34(4), 2841–2846 (2008)CrossRefGoogle Scholar
  13. 13.
    Benchaib, Y., Chikh, M.: A Specialized learning for neural classification of cardiac arrhythmias. Journal of Theoretical and Applied Information Technology 6(1), 81–89 (2009)Google Scholar
  14. 14.
    Gothwal, H., Kedawat, S., Kumar, R.: Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Journal of Biomedical Science and Engineering 4, 289–296 (2011)CrossRefGoogle Scholar
  15. 15.
    Ghorbanian, P., Jalali, A., Ghaffari, A., Nataraj, C.: An improved procedure for detection of heart arrhythmias with novel pre-processing techniques. Expert systems 29(5), 478–491 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Santiago Torres-Alegre
    • 1
  • Juan Fombellida
    • 1
  • Juan Antonio Piñuela-Izquierdo
    • 2
  • Diego Andina
    • 1
  1. 1.Group for Automation in Signals and CommunicationsTechnical University of MadridMadridSpain
  2. 2.Universidad Europea de MadridMadridSpain

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