Prediction of brain electrical activity in epilepsy using a higher-dimensional prediction algorithm for discrete time cellular neural networks (DTCNN)

2004 ◽  
Author(s):  
F. Gollas ◽  
C. Niederhofer ◽  
R. Tetzlaff
Keyword(s):  
Neural Networks ◽  
Electrical Activity ◽  
Discrete Time ◽  
Cellular Neural Networks ◽  
Prediction Algorithm ◽  
Brain Electrical Activity ◽  
Higher Dimensional

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.

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

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