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

2002 ◽  
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

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

2003 ◽  
Vol 12 (06) ◽  
pp. 825-844 ◽  
Author(s):  
R. KUNZ ◽  
R. TETZLAFF
Keyword(s):  
Neural Networks ◽  
Electrical Activity ◽  
Correlation Dimension ◽  
Temporal Dynamics ◽  
Cellular Neural Networks ◽  
Brain Electrical Activity ◽  
Accurate Approximation ◽  
Spatio Temporal

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

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

2003 ◽  
Vol 12 (04) ◽  
pp. 399-416 ◽  
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.

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

2003 ◽  
Vol 13 (06) ◽  
pp. 489-498 ◽  
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.

scholarly journals Методика пространственно-временного анализа электрической активности головного мозга

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.

Real-time characterization of the spatio-temporal dynamics of deformable mirrors

2016 ◽  
Author(s):  
James Kilpatrick ◽  
Adela Apostol ◽  
Anatoliy Khizhnya ◽  
Vladimir Markov ◽  
Leonid Beresnev
Keyword(s):  
Real Time ◽  
Temporal Dynamics ◽  
Deformable Mirrors ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document