Automated detection of a preseizure state: non‐linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems

Tetzlaff, Ronald; Niederhofer, C.; Fischer, Philipp

doi:10.1002/cta.344

Cited by 32 publications

(17 citation statements)

References 25 publications

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“…[6][7][8][9] Recent studies have shown that the approach of analyzing the dynamics of interacting complex systems with the nonlinear dynamics of interacting nonlinear elements can also be extended to the concepts of phase synchronization 10 and generalized synchronization.…”

Section: 2mentioning

confidence: 99%

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Dickten

Elger

Lehnertz

2013

Recent Advances in Predicting and Preventing Epileptic Seizures

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We advance our approach of analyzing the dynamics of interacting complex systems with the nonlinear dynamics of interacting nonlinear elements. We replace the widely used lattice-like connection topology of cellular neural networks (CNN) by complex topologies that include both short-and long-ranged connections. With an exemplary time-resolved analysis of asymmetric nonlinear interdependences between the seizure generating area and its immediate surrounding we provide first evidence for complex CNN connection topologies to allow for a faster network optimization together with an improved approximation accuracy of directed interactions.

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Section: 2mentioning

confidence: 99%

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Dickten

Elger

Lehnertz

2013

Recent Advances in Predicting and Preventing Epileptic Seizures

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“…Numerous interdisciplinary investigations (e.g. [22][23][24][25][26][27]) aim at a better understanding of neural dynamics related to epileptic seizure generation; many of them using methods from signal processing, complex systems theory and system biology. Several publications indicate that a possible transition from interictal to ictal states might be detected 625 by a higher-dimensional analysis of brain electrical activity [28][29][30][31][32] but still seizures cannot be anticipated with necessary sensitivity and specificity.…”

Section: Cellular Nonlinear Networkmentioning

confidence: 99%

Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks

Gollas

Niederhofer

Tetzlaff

2008

Circuit Theory & Apps

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SUMMARYAlthough 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.

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“…Previous studies [5], [6], [8]- [11], [13], [15], [16] have shown that interesting results are obtained by the application of algorithms based on Cellular Nonlinear Networks [2]- [4] and Volterra-Systems [14]. Especially, distinct changes of the relative mean square error prior to epileptic seizures could be observed in a EEG-signal [17] prediction.…”

Section: Introductionmentioning

confidence: 96%

Dynamics of EEG-signals in epilepsy: Spatio temporal analysis by Cellular Nonlinear Networks

Niederhofer

Gollas

Tetzlaff

2007

2007 18th European Conference on Circuit Theory and Design

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Meanwhile, numerous publications address the feature extraction problem in epilepsy. Up to now a precursor detection based on changes of EEG-signal features could not be performed with a sufficient sensitivity and specifity for an automated seizure warning system. Different approaches including procedures using stochastic models, as well as algorithms based on Cellular Nonlinear Networks (CNN) and Volterra-Systems have been discussed throughout previous publications. Therin interesting findings have been discussed involving e.g. signal prediction algorithms and the calculation of synchronisation measures. In this contribution new results obtained in a spatio temporal linear prediction of segmented electrode signals using long-term SEEG and ECoG recordings of patients in epilepsy will be discussed in detail.

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Automated detection of a preseizure state: non‐linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems

Cited by 32 publications

References 25 publications

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks

Dynamics of EEG-signals in epilepsy: Spatio temporal analysis by Cellular Nonlinear Networks

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