Modeling and interpretation of scalp-EEG and depth-EEG signals during interictal activity

Cosandier-Rimélé, D.; Badier, Jean‐Michel; Chauvel, Patrick; Wendling, Fabrice

doi:10.1109/iembs.2007.4353281

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…This could also move the neuromonitoring unit from hospital to home and potentially allow for longer recording periods, if needed. Alternatives to invasive electrode monitoring that may emerge in the near future include attempts to increase the successful localization of seizure foci with noninvasive monitoring through improved source localization algorithms that attempt to deconvolve signal spread through the scalp and skull . Higher spatial and temporal resolution of abnormal cortical and even subcortical activity may be achieved with observations of intrinsic optical signals, and methods to assess tissue oxygenation may become adjunctive to ECoG and can also be used to create functional maps .…”

Section: Future Of Surgical Monitoringmentioning

confidence: 99%

Subdural grid and depth electrode monitoring in pediatric patients

et al. 2017

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Summary Invasive electroencephalographic monitoring with implantable subdural electrodes and intraparenchymal depth electrodes has become a basic tenet of epilepsy surgery. Improved localization of epileptic foci justifies the secondary procedure and monitoring period in many patients. Informed use of invasive monitoring in conjunction with imaging and functional studies makes epilepsy surgery a smaller, safer, and more effective endeavor. Herein we review the history, indications, implementation, and foreseeable future of grid, strip, and depth electrode use.

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Section: Future Of Surgical Monitoringmentioning

confidence: 99%

Subdural grid and depth electrode monitoring in pediatric patients

et al. 2017

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“…The signals recorded from intracranial EEG are on a different scale of activity than the brain activity recorded from scalp EEG. Scalp electrodes provide the global information, whereas the intracranial one provides the local information from the brain structure (Cosandier-Rimélé et al, 2007). Low voltage or high frequency components that cannot be seen easily in scalp EEG can be seen clearly in intracranial EEG.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction in Scalp EEG using One Dimensional Local Binary Pattern based Features

Fathima¹,

Joseph²,

Bedeeuzzaman³

2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Seizure prediction will deeply improve the quality of life of epileptic patients. In this paper, a new method of automatic seizure prediction is presented using one dimensional local binary pattern (1D-LBP) based features in scalp electroencephalogram (EEG). In the feature extraction stage, the preictal and interictal EEG signals were transformed to the 1D-LBP domain and histogram features were extracted. These features were submitted to two different types of classifiers: linear discriminant analysis (LDA) and support vector machine (SVM). In order to reduce the false prediction rate (FPR), a simple post processing stage was also incorporated. The classification using SVM showed improvement over LDA in terms of sensitivity, prediction time and FPR. The proposed method was evaluated using the scalp EEG recording from 13 patients with a total number of 47 seizures. It could achieve a sensitivity of 96.15%, an average prediction time of 51.25 minutes with an FPR of 0.463.

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