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In this work, the spectral exponent y derived from the wavelet-based representation for 1/f processes is used to analyze multi-channel electrocorticogram (ECoG) data obtained from a subject with temporal lobe epilepsy. The computational results show that the spectral exponents of different channels of the ECoG data exhibit different characteristics of spectral exponent y. Also during an epileptic...
A consensus feature-ranking approach has been applied to the study of localization-related temporal lobe epilepsy (TLE) in order to evaluate the relative discriminative power of individual attributes. Cases were selected on the basis of a postoperative outcome free of disabling seizures (i.e., Engel class I) in order to establish a definitive laterality of focal epileptogenicity. Several quantitative...
The article emphasizes clinical significance and analysis of electroencephalogram (EEG) test with nonlinear analysis applied on patients with unilateral temporal lobe epilepsy (TLE). The entire epileptic onset of seizures in 22 patients with TLE were recorded with the synchronous 18-lead EEG. According to the shape of EEG waves and the corresponding symptoms of the clinic during their seizures, data...
Epilepsy is a neurological disorder characterized by recurrent seizures which affects about 1% people worldwide. During the past decades, some mechanisms involved in ictogenesis (generation of seizures) have been identified and, to some extent, partially understood. However, regarding epileptogenesis (process by which a neuronal system becomes epileptic), underlying mechanisms remain elusive. This...
We describe a novel algorithm for the prediction of epileptic seizures using scalp EEG. The method is based on the analysis of the positive zero-crossing interval series of the EEG signal and its first and second derivatives as a measure of brain dynamics. In a moving-window analysis, we estimated the probability density of these intervals and computed the differential entropy. The resultant entropy...
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 paper addresses the automated false positives-free detection of epileptic events by the fusion of information extracted from simultaneously recorded electroencephalographic- and electrocardiographic time-series. The approach relies on the biomedical prior knowledge for the coupling of the brain- and heart systems through the central autonomic network during temporal lobe epileptic events: neurovegetative...
The aim of this work is to develop a new method for automatic detection and classification of EEG patterns using continuous wavelet transforms (CWT) and artificial neural networks (ANN). Our method consists of EEG data selection, feature extraction and classification stage. For the data selection we use temporal lobe seizures for evaluation recorded from patients during 84 hours at hospital. In feature...
Results in literature show that the convergence of the Short-Term Maximum Lyapunov Exponent (STLmax) time series, extracted from intracranial EEG recorded from patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. When the STLmax profiles of different electrode sites converge (high entrainment) a seizure is likely to occur. In this paper Renyipsilas Mutual information...
Current pharmacological, electrophysiological, and surgical treatments are not always effective for all epileptic syndromes. In analyzing the clinical utility, traditional EEG analysis provides a coarse representation of the neuronal activity and we hypothesize that for chronic, in vivo epilepsy research more specific electrophysiological techniques are necessary. In order to increase our understanding...
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