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Epileptic seizure detection: a nonlinear viewpoint.

Niina Päivinen·Seppo Lammi·Asla Pitkänen·Jari Nissinen·Markku Penttonen·Tapio Grönfors
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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.

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AlgorithmsDiscriminant AnalysisElectroencephalographyFractalsHumansNonlinear DynamicsSeizuresJournal ArticleResearch Support, Non-U.S. Gov't

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Niina Päivinen, Seppo Lammi, Asla Pitkänen, Jari Nissinen, Markku Penttonen, Tapio Grönfors. 2005. Epileptic seizure detection: a nonlinear viewpoint.. https://doi.org/10.1016/j.cmpb.2005.04.006

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