Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy

  • Authors:

  • Vangelis Sakkalis;Tracey Cassar;Michalis Zervakis;Kenneth P. Camilleri;Simon G. Fabri;Cristin Bigan;Eleni Karakonstantaki;Sifis Micheloyannis

  • Affiliations:

  • Greece and Institute of Computer Science, Foundation for Research and Technology, Heraklion, Greece;Faculty of Engineering, University of Malta, Msida, Malta;Department of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece;Faculty of Engineering, University of Malta, Msida, Malta;Faculty of Engineering, University of Malta, Msida, Malta;Faculty of Engineering, Ecological University of Bucharest, Bucharest, Romania;Faculty of Medicine, University of Crete, Heraklion, Greece;Clinical Neurophysiology Laboratory (L. Widen), Faculty of Medicine, University of Crete, Heraklion, Greece

  • Venue:
  • Computational Intelligence and Neuroscience - Processing of Brain Signals by Using Hemodynamic and Neuroelectromagnetic Modalities
  • Year:
  • 2008

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Abstract

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.