Neonatal seizures: Early-onset seizure syndromes and their consequences for development

Mizrahi, Eli M.; Clancy, Robert R.

doi:10.1002/1098-2779(2000)6:4<229::aid-mrdd2>3.0.co;2-y

Cited by 80 publications

(32 citation statements)

References 76 publications

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“…According to Mizrahi et al, 29 however, the quality of the seizures in neonates is the main determinant of longterm outcome; prognosis varies with the etiology of the neonatal seizure, 29 an aspect not measured in this study. As suggested by our results, and by evidence that neonatal seizures are also associated with a number of activitydependent changes in brain development (eg, altered synaptogenesis and reduction in neurogenesis), 30 neonatal seizures should not be considered benign events.…”

Section: Discussionmentioning

confidence: 75%

Factors Associated With Epilepsy in Children With Periventricular Leukomalacia

et al. 2007

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Children with cerebral palsy associated with periventricular leukomalacia frequently develop unprovoked epileptic seizures. This article reports an analysis of risk factors for epilepsy in children with radiologically confirmed periventricular leukomalacia. This cohort was screened for epilepsy and for an array of clinical and demographic factors. Of 154 subjects with radiologically confirmed periventricular leukomalacia, 40 (26.0%) had epilepsy. In the epileptic group, radiologic pathology other than periventricular leukomalacia was uncommon. Significant associations were found between epilepsy and cerebral palsy patterns other than spastic diparesis, mental handicap, cortical visual impairment, neonatal seizures, and severe periventricular leukomalacia. Only the presence of neonatal seizures was significantly associated with epilepsy once other risk factors were controlled in the regression model. Some previous studies have shown an association between neonatal seizures and later epilepsy for cerebral palsy in general. This is the first report of such an association for a single predominant type of cerebral pathology.

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Section: Discussionmentioning

confidence: 75%

Factors Associated With Epilepsy in Children With Periventricular Leukomalacia

et al. 2007

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“…Our observations, however, suggest that although bumetanide blocks the expression of seizures, it may not be efficient in preventing the long-lasting sequels of recurrent seizures. This is particularly important because seizures in neonates can cause long-lasting deteriorate effects (Ben-Ari 1985;Hauser et al 1998;Levene 2002;Massingale and Buttross 1993;Mizrahi and Clancy 2000;Moshe and Albala 1982;Tremblay et al 1984).…”

Section: Bumetanide Is An Anticonvulsive But Not An Antiepileptogenicmentioning

confidence: 99%

Bumetanide, an NKCC1 Antagonist, Does Not Prevent Formation of Epileptogenic Focus but Blocks Epileptic Focus Seizures in Immature Rat Hippocampus

Nardou

Ben-Ari

Khalilov³

2009

Journal of Neurophysiology

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Excitatory GABA action induced by high [Cl(-)](i) is thought to contribute to seizure generation in neonatal neurons although the mechanism of this effect remains unclear. We report that bumetanide, a NKCC1 antagonist, reduces driving force of GABA-mediated currents (DF(GABA)) in neonatal hippocampal neurons and blocks the giant depolarizing potentials (GDPs), a spontaneous pattern of network activity. In the preparation composed of two intact interconnected hippocampi, bumetanide did not prevent generation of kainate-induced seizures, their propagation to the contralateral hippocampus, and formation of an epileptogenic mirror focus. However, in the isolated mirror focus, bumetanide effectively blocked spontaneous epileptiform activity transforming it to the GDP-like activity pattern. Bumetanide partially reduced DF(GABA) and therefore the excitatory action of GABA in epileptic neurons. Therefore bumetanide is a potent anticonvulsive agent although it cannot prevent formation of the epileptogenic mirror focus. We suggest that an additional mechanism other than NKCC1-mediated contributes to the persistent increase of DF(GABA) in epileptic neurons.

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“…However, the diagnosis of electrographic seizure is non-trivial. The appearance of the discharge can transform in frequency, amplitude and morphology over time (Mizrahi and Clancy, 2000).…”

Section: Neonatal Seizure Detectionmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Neonatal seizures: Early-onset seizure syndromes and their consequences for development

Cited by 80 publications

References 76 publications

Factors Associated With Epilepsy in Children With Periventricular Leukomalacia

Factors Associated With Epilepsy in Children With Periventricular Leukomalacia

Bumetanide, an NKCC1 Antagonist, Does Not Prevent Formation of Epileptogenic Focus but Blocks Epileptic Focus Seizures in Immature Rat Hippocampus

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

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