Skip to main content
SpringerLink
Log in

AMSOM: artificial metaplasticity in SOM neural networks—application to MIT-BIH arrhythmias database

  • S.I. : IWINAC 2015
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Artificial metaplasticity is the machine learning algorithm inspired in the biological metaplasticity of neural synapses. Metaplasticity stands for plasticity of plasticity, and as long as plasticity is related to memory, metaplasticity is related to learning. Implemented in supervised learning assuming input patterns distribution or a related function, it has proved to be very efficient in performance and in training convergence for multidisciplinary applications. Now, for the first time, this kind of artificial metaplasticity is implemented in an unsupervised neural network, achieving also excellent results that are presented in this paper. To compare results, a modified self-organization map is applied to the classification of MIT-BIH cardiac arrhythmias database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abraham WC (1996) Activity-dependent regulation of synaptic plasticity (metaplasticity) in the hippocampus. In: The hippocampus: functions and clinical relevance. Elsevier, Amsterdam, pp 15–26

  2. Jedlicka P (2002) Synaptic plasticity, metaplasticity and BCM theory. Bratisl Med J 103(4):137–143

    Google Scholar 

  3. Ropero-Pelaez J, Andina D (2012) Do biological synapses perform probabilistic computations? Neurocomputing. https://doi.org/10.1016/j.neucom.2012.08.042

    Article  Google Scholar 

  4. Andina D, Alvarez-Vellisco A, Jevtic A, Fombellida J (2009) Artificial metaplasticity can improve artificial neural network learning. Intell Autom Soft Comput Spec Issue Signal Process Soft Comput 15(4):681–694

    Google Scholar 

  5. Kinto E, Del-Moral-Hernandez E, Marcano-Cedeño A, Ropero-Pelaez J (2007) A preliminary neural model for movement direction recognition based on biologically plausible plasticity rules. In: Proceeding IWINAC 2007, vol 45, no 28, pp 628–636

  6. Marcano-Cedeño A, Quintanilla-Dominguez J, Andina D (2011) Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing 74(8):1243–1250

    Article  Google Scholar 

  7. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech. J. 27:379–423

    Article  MathSciNet  Google Scholar 

  8. Haykin S (1995) Neural networks a comprehensive foundation. MacMillan College Publishing Company, New York

    MATH  Google Scholar 

  9. Benchaib Y, Marcano-Cedeño A, Torres-Alegre S, Andina D (2013) Application of artificial metaplasticity neural networks to cardiac arrhythmias classification. Lect Notes Comput Sci 79(30):181–190

    Article  Google Scholar 

  10. Ruck DWH, Rogers SK, Kabrisky M, Oxley ME, Suter BW (1990) The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Trans Neural Netw 1(4):296–298

    Article  Google Scholar 

  11. Torres-Alegre S, Fombellida J, Piũela JA, Andina D (2015) Artificial metaplasticity: application to MIT-BIH arrhythmias database. Lect Notes Comput Sci 91(7):133–142

    Article  Google Scholar 

  12. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69

    Article  MathSciNet  Google Scholar 

  13. Kohonen T (1982) Analysis of a simple self organizing process. Biol Cybern 44:135–140

    Article  MathSciNet  Google Scholar 

  14. Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50

    Article  Google Scholar 

  15. Minami K, Nakajima H, Toyoshima T (1999) Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans Biomed Eng 46(2):179–185

    Article  Google Scholar 

  16. Owis MI, Youssef ABM, Kadah YM (2002) Characterization of ECG signals based on blind source separation. Med Biol Eng Comput 40(5):557–564

    Article  Google Scholar 

  17. Yu SN, Chou KT (2008) Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst Appl 34(4):2841–2846

    Article  Google Scholar 

  18. Benchaib Y, Chikh M (2009) A Specialized learning for neural classification of cardiac arrhythmias. J Theor Appl Inf Technol 6(1):81–89

    Google Scholar 

  19. Ghorbanian P, Jalali A, Ghaffari A, Nataraji A (2009) An improved procedure for detection of heart arrhythmias with novel pre-processing techniques. Expert Syst 29(5):478–491

    Article  Google Scholar 

  20. Alonso-Atienza F, Morgado E, Fernandez-Martinez L, Garcia-Alberola A, Rojo-Alvarez J (2014) Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 61(3):832–840

    Article  Google Scholar 

  21. Elhaj F, Salim N, Harris A, Swee T, Ahmed T (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed 127:5263

    Article  Google Scholar 

  22. Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63:664675

    Article  Google Scholar 

  23. Shanshan C, Wei H, Zhi L, Jian L, Xingjiao G (2017) Heartbeat classification using projected and dynamic features of ECG signal. Biomed Signal Process Control 31:165–173

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Santiago Torres-Alegre

    Present address: ETSIT-Technical University of Madrid, Avd Complutense, Madrid, 28040, Spain

Authors and Affiliations

  1. Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain

    Santiago Torres-Alegre, Juan Fombellida & Diego Andina

  2. Universidad Europea de Madrid, Madrid, Spain

    Juan Antonio Piñuela-Izquierdo

Authors
  1. Santiago Torres-Alegre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Fombellida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Antonio Piñuela-Izquierdo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diego Andina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santiago Torres-Alegre.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Torres-Alegre, S., Fombellida, J., Piñuela-Izquierdo, J.A. et al. AMSOM: artificial metaplasticity in SOM neural networks—application to MIT-BIH arrhythmias database. Neural Comput & Applic 32, 13213–13220 (2020). https://doi.org/10.1007/s00521-018-3576-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3576-0

Keywords

Search

Navigation