Epileptic seizure detection by means of genetically programmed artificial features

Authors:
Hiram Firpi;Erik Goodman;Javier Echauz
Affiliations:
Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI;BioQuantix Corp., Atlanta, GA
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 2
Cited 1

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Chaos and Time-Series Analysis

Chaos and Time-Series Analysis

Genetic programming of conventional features to detect seizure precursors

Engineering Applications of Artificial Intelligence

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Abstract

In this paper, we describe a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features-features that are computer-crafted and may not have a known physical meaning-directly from the reconstructed state-space trajectories of the EEG signals that reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in three patients and validation experiments were carried out using 267.6 hours of EEG recordings. The results with the artificial features compare favorably with previous benchmark work that used a handcrafted feature.