Nonlinear prediction of brain electrical activity in epilepsy with a Volterra RLS algorithm

Niederhofer, C.; Suna, Shaolong; Tetzlaff, Ronald

doi:10.1109/iscas.2002.1010294

Cited by 13 publications

(9 citation statements)

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“…A seizure precursor may be detected by changes of the so-called signal features derived from EEG signals. Many publications [3][4][5][6][7][8][9][10] have been addressed to this field of research, but the problem remains unsolved. It has been shown that algorithms based on CNN [11][12][13] provided new results in the field of EEG-signal analysis.…”

Section: F Gollas C Niederhöfer and R Tetzlaffmentioning

confidence: 99%

“…The second approach is based on the well-known signal prediction problem. In [6] an algorithm based on Volterra systems [14] has been introduced for a prediction of EEG signals of single electrode contacts. In this publication, the signal prediction problem will be treated by using delay-type discrete-time CNN (DTCNN) with polynomial and linear weight functions.…”

Section: F Gollas C Niederhöfer and R Tetzlaffmentioning

confidence: 99%

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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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Section: F Gollas C Niederhöfer and R Tetzlaffmentioning

confidence: 99%

Section: F Gollas C Niederhöfer and R Tetzlaffmentioning

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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“…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: 99%

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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“…Considering both features -the error and the predictor coefficients -for consecutive segments the resulting time series can be analysed with respect to distinct changes in the feature values prior to impending epileptic seizures. Original EEG-signal values and the predicted signal values obtained by the predictor given in(2) …”

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confidence: 99%

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

2007

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In previous publications, 1-6 several approaches targeting the problem of seizure prediction 7 in epilepsy 8 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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Nonlinear prediction of brain electrical activity in epilepsy with a Volterra RLS algorithm

Cited by 13 publications

References 1 publication

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

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

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

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