title: Optimized feature subsets for epileptic seizure prediction studies.
abstract: The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three patients of the European Database on Epilepsy, the most important univariate features are related to spectral information and statistical moments.
authors.affiliation: Science and Technology Faculty, University of Coimbra, Pólo II, Coimbra, Portugal. bdireito@dei.uc.pt
authors.forename: Bruno
authors.initials: B
authors.lastname: Direito
authors.forename: Francisco
authors.initials: F
authors.lastname: Ventura
authors.forename: César
authors.initials: C
authors.lastname: Teixeira
authors.forename: António
authors.initials: A
authors.lastname: Dourado
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine