Integrating neural networks and chaotic measurements for modelling epileptic brain

Authors:
Maurizio Fiasché;Stefan Schliebs;Lino Nobili
Affiliations:
Department of Mathematics "F. Brioschi", Polytechnic Institute of Milan, Milan, Italy;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Department of Neuroscience, Centre of Sleep Medicine, Centre for Epilepsy Surgery
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

In the last 20 years a lot of works in literature analysed and proposed several methods capable to predict the occurrence of seizures from the electroencephalogram (EEG) of epileptic patients. One of the best results was obtained using a version of the maximum Lyapunov exponent (Lmax) for predicting the advent of a seizure, but in spite of promising results presented, more recent evaluations could not reproduce these optimistic findings. Following this trend, in this paper we propose a new integrative technique starting from two different paradigms: Chaos and Neural Networks (NN). The new framework has been tested on long term intracerebral stereo-EEG (sEEG) recordings, with very good results. We present this way of analysis as the key for modelling brain mechanisms during epileptic seizures, going over critical state reported in literature for Lmax with suited computational methods, providing theoretical justifications. This is a preliminary work with numerous possible evolutions.