Dataset Information

Automatic detection of fast oscillations (40-200 Hz) in scalp EEG recordings.

ABSTRACT: We aim to automatically detect fast oscillations (40-200 Hz) related to epilepsy on scalp EEG recordings.The detector first finds localized increments of the signal power in narrow frequency bands. A simple classification based on two features, a narrowband to wideband signal amplitude ratio and an absolute narrowband signal amplitude, then allows for an important reduction in the number of false positives.When compared to an expert, the performance in 15 focal epilepsy patients resulted in 3.6 false positives per minute at 95% sensitivity, with at least 40% of the detected events being true positives. In most of the patients the channels showing the highest number of events according to the expert and the automatic detector were the same.A high sensitivity is achieved with the proposed automatic detector, but results should be reviewed by an expert to remove false positives.The time required to mark fast oscillations on scalp EEG recordings is drastically reduced with the use of the proposed detector. Thus, the automatic detector is a useful tool in studies aiming to create a better understanding of the fast oscillations visible on the scalp.

SUBMITTER: von Ellenrieder N

PROVIDER: S-EPMC3774655 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automatic detection of fast oscillations (40-200 Hz) in scalp EEG recordings.

von Ellenrieder Nicolás N Andrade-Valença Luciana P LP Dubeau François F Gotman Jean J

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 20110921 4

<h4>Objective</h4>We aim to automatically detect fast oscillations (40-200 Hz) related to epilepsy on scalp EEG recordings.<h4>Methods</h4>The detector first finds localized increments of the signal power in narrow frequency bands. A simple classification based on two features, a narrowband to wideband signal amplitude ratio and an absolute narrowband signal amplitude, then allows for an important reduction in the number of false positives.<h4>Results</h4>When compared to an expert, the performa ...[more]

PMID: 21940200

Similar Datasets

Project description:Cognitive processes are carried out during wakefulness by means of extensive interactions between cortical and subcortical areas. In psychiatric conditions, such as psychosis, these processes are altered. Interestingly, REM sleep where most dreams occurs, shares electrophysiological, pharmacological, and neurochemical features with psychosis. Because of this fact, REM sleep is considered a natural model of psychosis. Ketamine is a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that at sub-anesthetic dose induces psychotomimetic-like effects in humans and animals, and is employed as a pharmacological model of psychosis. Oscillations in the gamma frequency band of the electroencephalogram (EEG), mainly at about 40 Hz, have been involved in cognitive functions. Hence, the present study was conducted to analyze the EEG low gamma (30-45 Hz) band power and coherence of the cat, in natural (REM sleep) and pharmacological (sub-anesthetic doses of ketamine) models of psychosis. These results were compared with the gamma activity during alert (AW) and quiet wakefulness (QW), as well as during non-REM (NREM) sleep. Five cats were chronically prepared for polysomnographic recordings, with electrodes in different cortical areas. Basal recordings were obtained and ketamine (5, 10, and 15 mg/kg, i.m.) was administrated. Gamma activity (power and coherence) was analyzed in the abovementioned conditions. Compared to wakefulness and NREM sleep, following ketamine administration gamma coherence decreased among all cortical regions studied; the same coherence profile was observed during REM sleep. On the contrary, gamma power was relatively high under ketamine, and similar to QW and REM sleep. We conclude that functional interactions between cortical areas in the gamma frequency band decrease in both experimental models of psychosis. This uncoupling of gamma frequency activity may be involved in the cognitive features shared by dreaming and psychosis.

Dataset Information

Automatic detection of fast oscillations (40-200 Hz) in scalp EEG recordings.

Publications

Automatic detection of fast oscillations (40-200 Hz) in scalp EEG recordings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets