Computational Intelligence Algorithms for Seizure Prediction

Authors

Abstract

Purpose:
To develop computational intelligence algorithms for seizure prediction to embed in a transportable device to support refractory epileptic patients.

Methods:
Firstly a set of features is extracted from the EEG, measuring energy, time-frequency and nonlinear dynamic contents. These features are then used for classification of the brain state into four classes: inter-ictal, pre-ictal, ictal, pos-ictal. Two approaches from computational intelligence are applied: (i) artificial neural networks in the original 14 features space (several architectures are compared: feedforward, with and without memory, radial basis function, Elman), (ii) multidimensional scaling to reduce the 14th dimensional space to 3-dimensional space where classification may be done in an easier way.

Keywords

computational intelligence; neural networks; multidimensional scaling; seizure prediction

Subject

computaional intelligence; seizurepredic

Conference

8th European Congress on Epileptology, September 2008