22 May 2007 Seizure prediction by delay-type single-layer discrete-time cellular nonlinear networks (DTCNN)?
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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.
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Christian Niederhöfer, Christian Niederhöfer, Frank Gollas, Frank Gollas, Ronald Tetzlaff, 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); doi: 10.1117/12.721911; https://doi.org/10.1117/12.721911

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