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Epileptic seizures are generated by an abnormal synchronization of neurons. Since epileptic seizures are unforeseeable for the patients, epileptic seizures detection is an interesting issue in epileptology, that novel approaches to understand the mechanism of epileptic seizures. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable...
Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can “drive” some other structures. This paper focuses...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction...
In this contribution a new algorithm based on the spatio-temporal dynamics of reaction-diffusion cellular nonlinear networks (RD-CNN) for analyzing brain electrical activity in epilepsy is proposed. RD-CNN are determined in an identification process and then analyzed by means of Chuas Local Activity theory. Clinical manifestations of epileptic seizures are phenomena of abnormal, excessive, or synchronous...
This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted...
Epilepsy is one of the most common neurological disorders, affecting around 1 in 200 of the population. However, identifying epilepsy can be difficult because seizures tend to be relatively infrequent events and an electroencephalogram (EEG) does not always show abnormalities. The aim of this project is to develop a new methods that could improve the diagnosis of epilepsy, leading to earlier treatment...
Vagus nerve electric stimulation is a new method for preventing and treating epilepsy, pain disorders and depression with a subcutaneous surgically implanted device. The mechanisms of action of implanted stimulation device are still unknown. And the vagus nerve electric stimulator has several operating and stimulation parameters with the programming wand or the magnet. We had finished designing a...
Vagus nerve electric stimulation is a new method for preventing and treating epilepsy, pain disorders and depression with a subcutaneous surgically implanted device. The mechanisms of action of implanted stimulation device are still unknown. And the vagus nerve electric stimulator has several operating and stimulation parameters with the programming wand or the magnet. We had finished designing a...
This paper emphasizes on a VLSI design for SIRM fuzzy processor in biomedical application. A novel approach aims to identify and design a simple robust fuzzy system with minimum number of fuzzy rules to classify the epilepsy risk level of diabetic patients from cerebral blood flow and EEG signals is discussed in this paper. Four different types of fuzzy models are designed and tested with 200 patients...
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