Epileptic seizure detection: A nonlinear viewpoint

Authors:
Niina Päivinen;Seppo Lammi;Asla Pitkänen;Jari Nissinen;Markku Penttonen;Tapio Grönfors
Affiliations:
Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland;Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland;A.I. Virtanen Institute for Molecular Sciences, Department of Neurobiology, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland;A.I. Virtanen Institute for Molecular Sciences, Department of Neurobiology, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland;A.I. Virtanen Institute for Molecular Sciences, Department of Neurobiology, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland;Department of Computer Science, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland
Venue:
Computer Methods and Programs in Biomedicine
Year:
2005

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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.