title: Epileptic seizure detection: a nonlinear viewpoint.
abstract: This study concerns the detection of epileptic seizures from electroencephalogram (EEG) data using computational methods. Using short sliding time windows, a set of features is computed from the data. The feature set includes time domain, frequency domain and nonlinear features. Discriminant analysis is used to determine the best seizure-detecting features among them. The findings suggest that the best results can be achieved by using a combination of features from the linear and nonlinear realms alike.
authors: Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland. niina.paivinen@cs.uku.fi
authors: Niina
authors: N
authors: Päivinen
authors: Seppo
authors: S
authors: Lammi
authors: Asla
authors: A
authors: Pitkänen
authors: Jari
authors: J
authors: Nissinen
authors: Markku
authors: M
authors: Penttonen
authors: Tapio
authors: T
authors: Grönfors
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004569:Electroencephalography; D017709:Fractals; D006801:Humans; D017711:Nonlinear Dynamics; D012640:Seizures / Q000503:physiopathology*