Genetic programming artificial features with applications to epileptic seizure prediction.

In this paper, we propose a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features (i.e., features that are computer crafted and may not have a known physical meaning) directly from the reconstructed state-space trajectory of the EEG signals that reveal patterns predictive of epileptic seizures. The algorithm was evaluated in three different patients, with prediction defined over a horizon of 5 minutes before unequivocal electrographic onset. Experiments are carried out using 20 baseline epochs (non-seizures) and 18 preictal epochs (pre-seizures). Results show that just two seizures were missed while a perfect classification on the baseline epochs was achieved, yielding a 0.0 false positive per hour.