Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier

  • Antonio Quintero-RincónEmail author
  • Carlos D’Giano
  • Hadj Batatia
Conference paper
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 10)


This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet multi-scale transformation. The resulting coefficients are represented using their generalized Gaussian distribution. The proposed algorithm estimates the parameters of the distribution and the associated entropy. Next, an ensemble bagging classifier is used to performs the seizure onset detection using the entropy of each brain rhythm, by discriminating between seizure and non-seizure. Preliminary experiments with 105 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity.


Entropy Generalized Gaussian distribution Ensemble bagging classifier Wavelet filter banks EEG Epilepsy 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonio Quintero-Rincón
    • 1
    Email author
  • Carlos D’Giano
    • 2
  • Hadj Batatia
    • 3
  1. 1.Department of BioengineeringInstituto Tecnológico de Buenos Aires (ITBA)Buenos AiresArgentina
  2. 2.Centro Integral de Epilepsia y TelemetríaFundación Lucha contra las Enfermedades Neurólogicas Infantiles (FLENI)Buenos AiresArgentina
  3. 3.IRITUniversity of ToulouseToulouseFrance

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