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

scholarly journals Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura Gagliano ◽  
Elie Bou Assi ◽  
Dang K. Nguyen ◽  
Mohamad Sawan
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Focal Epilepsy ◽  
Single Layer ◽  
Epileptic Seizures ◽  
Novel Approach ◽  
Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

scholarly journals A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

2021 ◽  
Vol 8 (4) ◽  
pp. 180-187
Author(s):  
Saleh Lashkari ◽  
Ali Moghimi ◽  
Hamid Reza Kobravi ◽  
Mohamad Amin Younessi Heravi
Keyword(s):  
Phase Space ◽  
Electrical Activity ◽  
Morphological Characteristics ◽  
Epileptic Seizures ◽  
Absence Epilepsy ◽  
Brain Electrical Activity ◽  
Phase Space Methods ◽  
Wave Discharge ◽  
Spike Wave

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.

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