Search results for "seizure"
showing 3 items of 203 documents
N-ACETYL-ASPARTATE ABNORMALITIES IN INTERNAL-TEMPORAL EPILEPTICUS FOCUS USING PROTON MAGNETIC-RESONANCE SPECTROSCOPY
1995
International audience; Abstract: The aim of this study was to characterize the neurochemical abnormalities related to N-acetyl-aspartate which is a neuronal marker, within an epilepticus focus located in the internal-temporal area, using proton magnetic resonance spectroscopy, Eleven patients,with a mono-hippocampal epilepticus focus on clinical and per-critical electroencephalographical criteria, were matched with II controls by age, sex and laterality. Proton spectroscopy of a volume of 8 cm(3) was performed within the ipsilateral and the contralateral internal-temporal area and within the 2 hippocampus of controls. Volumetry of the ipsilateral and the contralateral hippocampus and of th…
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
2020
This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA. Bres EE et al. Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. Glia. 2020;1–33.
Seizure Prediction Using EEG Channel Selection Method
2022
Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each partici…