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Neural Computing and Applications

, Volume 31, Supplement 2, pp 1317–1329 | Cite as

Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms

  • Luís A. M. Pereira
  • João P. PapaEmail author
  • André L. V. Coelho
  • Clodoaldo A. M. Lima
  • Danillo R. Pereira
  • Victor Hugo C. de Albuquerque
Original Article

Abstract

Epilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of the electroencephalogram (EEG) signal can be instrumental for the proper diagnosis of this mental condition. This work presents a systematic assessment of the performance of different variants of the binary magnetic optimization algorithm (BMOA), two of which are introduced here, while serving as feature selectors for epileptic EEG signal identification. In this context, the optimum-path forest classifier was adopted as a classification model, whereas different wavelet families were considered for EEG feature extraction. In order to compare the performance of the improved BMOA variants against the traditional one, as well as other metaheuristic techniques, namely particle swarm optimization, binary bat algorithm, and genetic algorithm, we employed a well-known EEG benchmark dataset composed of five classes of EEG signals (two of which comprising normal patients with eyes open or closed, and the remaining comprising ill patients with different levels of epilepsy). Overall, the results evidenced the robustness of the proposed BMOA and its variants.

Keywords

Feature selection Epilepsy EEG signal classification Magnetic optimization algorithm Metaheuristics Optimum-path forest 

Notes

Acknowledgements

LAMP and JPP are grateful to FAPESP Grants #2011/14094-1, #2009/16206-1, and #2014/16250-9, respectively, and also CNPq Grants #303182/2011-3, #470571/2013-6, and #306166/2014-3. The ALVC and CAML also acknowledge the sponsorship from CNPq via Grants #475406/2010-9, #304603/2012-0, 308816/2012-9, and #303182/2011-3. VHCA acknowledges CNPq for the Grants #470501/2013-8 and #301928/2014-2.

Compliance with ethical standards

Conflicts of interest

The authors declare no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Instituto de ComputaçãoUniversidade Estadual de CampinasCampinasBrazil
  2. 2.Departamento de ComputaçãoUNESP - Univ Estadual PaulistaBauruBrazil
  3. 3.Programa de Pós-Graduação em Informática AplicadaUniversidade de FortalezaFortalezaBrazil
  4. 4.Escola de Artes, Ciências e HumanidadesUniversidade de São PauloSão PauloBrazil

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