Seizure prediction by delay-type single-layer discrete-time cellular nonlinear networks (DTCNN)?

Christian Niederhöfer; Frank Gollas; Ronald Tetzlaff

doi:10.1117/12.721911

22 May 2007 Seizure prediction by delay-type single-layer discrete-time cellular nonlinear networks (DTCNN)?

Christian Niederhöfer, Frank Gollas, Ronald Tetzlaff

Proceedings Volume 6592, Bioengineered and Bioinspired Systems III; 659202 (2007) https://doi.org/10.1117/12.721911
Event: Microtechnologies for the New Millennium, 2007, Maspalomas, Gran Canaria, Spain

Abstract

In previous publications,^1-6 several approaches targeting the problem of seizure prediction⁷ in epilepsy⁸ have been proposed. In this contribution recent results based on an EEG-signal prediction algorithm will be presented and discussed in detail. Therefore segmented data aquired by multi-electrode Stereoelectroencephalography (SEEG) and Electrocorticography (ECoG) are presented to a delay-type DTCNN with linear weight functions and a 3×1 network topology. This leads to series of signal predictors and according to that to series of prediction errors. These prediction error series are arranged in a 2 dimensional representation called error profile.⁹ This profile enables the choice of optimal positions for implanting long time electrodes, by means of which perhaps a mostly effective seizure prediction may become possible. So far data of different patients have been studied in detail and some distinct electrode points were found showing distinct changes before a seizure onset.

Citation Download Citation

Christian Niederhöfer, Frank Gollas, and Ronald Tetzlaff "Seizure prediction by delay-type single-layer discrete-time cellular nonlinear networks (DTCNN)?", Proc. SPIE 6592, Bioengineered and Bioinspired Systems III, 659202 (22 May 2007); https://doi.org/10.1117/12.721911

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