On prediction of epileptic seizures by computing multiple genetic programming artificial features

  • Authors:

  • Hiram Firpi;Erik Goodman;Javier Echauz

  • Affiliations:

  • Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan;Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan;BioQuantix Corp., Atlanta, Georgia

  • Venue:
  • EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
  • Year:
  • 2005

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Abstract

In this paper, we present a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier, to automatically create multiple artificial features (i.e., features that are computer-crafted and may not have a known physical meaning) directly from EEG signals, in a process that reveals patterns predictive of epileptic seizures. The algorithm was evaluated in three patients, with prediction defined over a horizon that varies between 1 and 5 minutes before unequivocal electrographic onset of seizure. For one patient, a perfect classification was achieved. For the other two patients, high classification accuracy was reached, predicting three seizures out of four for one, and eleven seizures out of fifteen for the other. For the latter, also, only one normal (non-seizure) signal was misclassified. These results compare favorably with other prediction approaches for patients from the same population.