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Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks
Author(s) -
Gollas Frank,
Niederhöfer Christian,
Tetzlaff Ronald
Publication year - 2008
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.513
Subject(s) - nonlinear system , anticipation (artificial intelligence) , computer science , signal (programming language) , artificial intelligence , identification (biology) , electroencephalography , focus (optics) , feature extraction , pattern recognition (psychology) , field (mathematics) , feature (linguistics) , signal processing , support vector machine , algorithm , neuroscience , mathematics , psychology , telecommunications , linguistics , philosophy , physics , botany , radar , quantum mechanics , pure mathematics , optics , biology , programming language
Abstract Although in the field of epileptic seizure prediction many spatio‐temporal approaches have been carried out, the precursor detection problem remains unsolved up to now. It can be observed that an increasing number of algorithms are developed based on cellular nonlinear networks (CNNs). They are dealing with the extraction of signal features using intracranial EEG recordings in order to detect possible preseizure states. In general, reliable precursor detections cannot be obtained for all treated cases. The performance of these algorithms can be enhanced by adapting them to specific patients. Combining different features in a feature vector in a future seizure anticipation platform may lead to a reliably working seizure prediction system. In this contribution we focus on two different CNN‐based algorithms—a nonlinear identification approach and a prediction algorithm. They will be discussed in detail and recently obtained results will be given. Copyright © 2008 John Wiley & Sons, Ltd.