title: Epileptic seizure detection using genetically programmed artificial features.
abstract: Patient-specific epilepsy seizure detectors were designed based on the genetic programming artificial features algorithm, a general-purpose, methodic algorithm comprised by a genetic programming module and a k-nearest neighbor classifier to create synthetic features. Artificial features are an extension to conventional features, characterized by being computer-coded and may not have a known physical meaning. In this paper, artificial features are constructed from the reconstructed state-space trajectories of the intracranial EEG signals intended to reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in seven patients and validation experiments were carried out using 730.6 hr of EEG recordings. The results with the artificial features compare favorably with previous benchmark work that used a handcrafted feature. Among other results, 88 out of 92 seizures were detected yielding a low false negative rate of 4.35%.
authors.affiliation: Center for Computational Biology & Bioinformatics, Indiana University-Perdue University, 410 W. 10th Street, Suite 5000, Indianapolis 46202, USA. hfirpi@ieee.org
authors.forename: Hiram
authors.initials: H
authors.lastname: Firpi
authors.forename: Erik D
authors.initials: ED
authors.lastname: Goodman
authors.forename: Javier
authors.initials: J
authors.lastname: Echauz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity