SPATIO-TEMPORAL DYNAMICS OF BRAIN ELECTRICAL ACTIVITY IN EPILEPSY: ANALYSIS WITH CELLULAR NEURAL NETWORKS (CNNs)

In this contribution a new procedure is proposed for the analysis of the spatio-temporal dynamics of brain electrical activity in epilepsy. Recent investigations1–3 have clarified that changes of estimates of the effective correlation dimension D2(k,m) from successive data segments allow a characterization of the epileptogenic process. These results provide important information for diagnostical purposes and enable a prediction of seizures in many cases. It will be shown that an accurate approximation of [Formula: see text] can be obtained by Cellular Neural Networks (CNNs),4,5 which form a unified paradigm. Moreover, the type of CNN presented here is optimized with respect to future implementations as VLSI realizations.6

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Brain electrical activity in epilepsy: characterization of the spatio-temporal dynamics with cellular neural networks based on a correlation dimension analysis

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353) ◽

10.1109/iscas.2000.856343 ◽

2002 ◽

Cited By ~ 11

Author(s):

R. Kunz ◽

R. Tetzlaff ◽

D. Wolf

Keyword(s):

Neural Networks ◽

Electrical Activity ◽

Correlation Dimension ◽

Temporal Dynamics ◽

Cellular Neural Networks ◽

Brain Electrical Activity ◽

Dimension Analysis ◽

Spatio Temporal

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Spatio-temporal distribution of brain electrical activity patterns in epilepsy: inputs for cellular neural networks

ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196) ◽

10.1109/iscas.2001.921314 ◽

2002 ◽

Cited By ~ 2

Author(s):

R. Tetzlaff ◽

R. Kunz

Keyword(s):

Neural Networks ◽

Electrical Activity ◽

Activity Patterns ◽

Temporal Distribution ◽

Cellular Neural Networks ◽

Brain Electrical Activity ◽

Spatio Temporal

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Spatio-temporal dynamics in Cellular Neural Networks with nonideal OTA's

CAS 2011 Proceedings (2011 International Semiconductor Conference) ◽

10.1109/smicnd.2011.6095831 ◽

2011 ◽

Cited By ~ 1

Author(s):

Liviu Goras ◽

Paul Puscasu Mihai

Keyword(s):

Neural Networks ◽

Temporal Dynamics ◽

Cellular Neural Networks ◽

Spatio Temporal

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ON THE SPATIO-TEMPORAL DYNAMICS OF A CLASS OF CELLULAR NEURAL NETWORKS

Journal of Circuits System and Computers ◽

10.1142/s0218126603000945 ◽

2003 ◽

Vol 12 (04) ◽

pp. 399-416 ◽

Cited By ~ 1

Author(s):

LIVIU GORAŞ ◽

TIBERIU DINU TEODORESCU ◽

ROMEO GHINEA

Keyword(s):

Neural Networks ◽

Linear Theory ◽

Computer Simulations ◽

Temporal Dynamics ◽

Linear Part ◽

Cellular Neural Networks ◽

Turing Patterns ◽

Root Locus ◽

Spatio Temporal ◽

The Stability

The stability and dynamics of a class of Cellular Neural Networks (CNNs) in the central linear part is investigated using the decoupling technique based on discrete spatial transforms, Nyquist and root locus techniques. The influence of the cell order and template neighborhood is discussed and computer simulations are presented. It is shown that, as in the case of Turing patterns, for 1D CNNs, the patterns predicted by the linear theory of the decoupling technique are often valid even when the nonlinearity has been reached.

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

10.1117/12.608643 ◽

2005 ◽

Cited By ~ 3

Author(s):

F. Gollas ◽

R. Tetzlaff

Keyword(s):

Neural Networks ◽

Electrical Activity ◽

Reaction Diffusion ◽

Cellular Neural Networks ◽

Brain Electrical Activity

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CELLULAR NEURAL NETWORKS (CNN) WITH LINEAR WEIGHT FUNCTIONS FOR A PREDICTION OF EPILEPTIC SEIZURES

International Journal of Neural Systems ◽

10.1142/s0129065703001790 ◽

2003 ◽

Vol 13 (06) ◽

pp. 489-498 ◽

Cited By ~ 5

Author(s):

R. TETZLAFF ◽

R. KUNZ ◽

C. NIEDERHÖFER

Keyword(s):

Neural Networks ◽

Electrical Activity ◽

Epileptic Seizures ◽

Cellular Neural Networks ◽

Pattern Detection ◽

Weight Functions ◽

Brain Electrical Activity ◽

Detection Algorithms ◽

Novel Approach

In this paper, we present a novel approach to the prediction of epileptic seizures using boolean CNN with linear weight functions. Three different binary pattern occurrence behaviours will be discussed and analysed for several invasive recordings of brain electrical activity. Furthermore analogic binary pattern detection algorithms will be introduced for a possible prediction of epileptic seizures.

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Методика пространственно-временного анализа электрической активности головного мозга

Письма в журнал технической физики ◽

10.21883/pjtf.2020.11.49498.18023 ◽

2020 ◽

Vol 46 (11) ◽

pp. 39

Author(s):

А.Е. Руннова ◽

М.О. Журавлев ◽

А.Р. Киселёв ◽

А.О. Сельский

Keyword(s):

Wavelet Transform ◽

Electrical Activity ◽

Continuous Wavelet Transform ◽

Temporal Dynamics ◽

Brain Activity ◽

Activity Patterns ◽

Spatial Dynamics ◽

Continuous Wavelet ◽

Brain Electrical Activity ◽

Spatio Temporal

In the framework of this work a new method based on continuous wavelet transform was proposed for analyzing the spatio-temporal dynamics of brain activity patterns. We described the example of this method application for the analysis of brain electrical activity signals. It is shown that this method has the ability to visually detect the occurrence and spatial dynamics of frequency patterns.

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