Epileptic seizure detection: a nonlinear viewpoint
Niina Päivinen1, Seppo Lammi, Asla Pitkänen
1Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland. niina.paivinen@cs.uku.fi
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.
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