Mr. Frank Gollas Profile

Frank Gollas

at Johann Wolfgang Goethe-Univ Frankfurt am Main

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Publications (3)

Proceedings Article | 20 May 2009 Paper

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

Frank Gollas, Ronald Tetzlaff

Proceedings Volume 7365, 73650E (2009) https://doi.org/10.1117/12.821507

KEYWORDS: Brain, Epilepsy, Electroencephalography, Statistical analysis, Signal detection, Current controlled current source, Chromium, Nervous system, Time series analysis, Reliability

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Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

Proceedings Article | 22 May 2007 Paper

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

Christian Niederhöfer, Frank Gollas, Ronald Tetzlaff

Proceedings Volume 6592, 659202 (2007) https://doi.org/10.1117/12.721911

KEYWORDS: Electrodes, Feature extraction, Epilepsy, Error analysis, Detection and tracking algorithms, Electroencephalography, Databases, Brain, Applied physics, Network security

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Proceedings Article | 29 June 2005 Paper

Modeling brain electrical activity in epilepsy by reaction-diffusion cellular neural networks

F. Gollas, R. Tetzlaff

Proceedings Volume 5839, (2005) https://doi.org/10.1117/12.608643

KEYWORDS: Systems modeling, Brain, Electroencephalography, Complex systems, Epilepsy, Neural networks, Electrodes, Optimization (mathematics), Diffusion, Process modeling

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