The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures. This study deals with a preliminary investigation to detect epileptic components in the electroencephalogram (EEG) waveform, which results in a reduction of analysis time by the expert neurologist. As an alternative to the Fast Fourier Transform (FFT) spectral analysis approach, an Auto Regressive (AR), a...
Epilepsy is one of the most common diseases related to the disfunctioning of the central nervous system. For the patients whose drug therapy turns out to be ineffective, it is a common method to identify the locations of sources that trigger the seizure in the brain tissue and resect them through surgical means for treatment. The success of the surgical operation depends on the accurate localization...
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...
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...
In this work we applied the direct directed transfer function (dDTF) to fMRI simultaneously recorded with EEG. The objective of the current study is to explore the functional brain network formed by the regions involved in blood oxygen level dependent (BOLD) response related to interictal spikes in temporal lobe epilepsy (TLE). Since fMRI response have been often observed in extratemporal regions,...
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...
Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.