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Methodology for Epileptic Episode Detection Using Complexity-Based Features

  • Jorge Andrés Gómez García
  • Carolina Ospina Aguirre
  • Edilson Delgado Trejos
  • Germán Castellanos Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Epilepsy is a neurological disease with a high prevalence on human beings, for which an accurate diagnosis remains as an essential step for medical treatment. Making use of pattern recognition tools is possible to design accurate automatic detection systems, capable of helping medical diagnostic. The present work presents an automatic epileptic episode methodology, based on complexity analysis where 3 classical nonlinear dynamic based features are used in conjunction with 3 regularity measures. k-nn and Support Vector Machines are used for classification. Results, superior to 98% confirm the discriminative ability of the presented methodology on epileptic detection labours.

Keywords

Support Vector Machine Extreme Learning Machine Hurst Exponent Sample Entropy Approximate Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jorge Andrés Gómez García
    • 1
  • Carolina Ospina Aguirre
    • 1
  • Edilson Delgado Trejos
    • 2
  • Germán Castellanos Dominguez
    • 1
  1. 1.Universidad Nacional de Colombiasede ManizalesColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia

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