Generalized periodic epileptiform discharges in critically ill children: Clinical features, and outcome

Akman, Cigdem I.; Khaled, Karine J. Abou; Segal, Eric; Micic, Vesna; Riviello, James J.

doi:10.1016/j.eplepsyres.2013.06.019

Cited by 21 publications

(24 citation statements)

References 25 publications

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“…Adults and children with generalized or lateralized periodic discharges are more likely to develop NCS or NCSE (Jette, Claassen et al 2006, Foreman, Claassen et al 2012, Ong, Gilmore et al 2012, Akman, Abou Khaled et al 2013, Gaspard, Manganas et al 2013, Pedersen, Rasmussen et al 2013). The presence of lateralized rhythmic delta activity (LRDA) appears to have the same high association with seizures as LPDs and is also a reasonable indication for CCEEG (Gaspard, Manganas et al 2013).…”

Section: Indications For Cceegmentioning

confidence: 99%

Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part I

Herman

Abend

Bleck

et al. 2015

Journal of Clinical Neurophysiology

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416

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Section: Indications For Cceegmentioning

confidence: 99%

Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part I

Herman

Abend

Bleck

et al. 2015

Journal of Clinical Neurophysiology

Self Cite

490

416

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“…Recent research has focused on feature normalization techniques for age independent seizure detection [17,22]. However, little attention has been given to methods for optimal training dataset selection with respect to age independent seizure detection [15]. This study shows that an age-independent SVM seizure detection system can successfully be used for seizure detection in both adult and neonatal patients.…”

Section: Adult Versus Neonatal Seizure Detectionmentioning

confidence: 89%

“…In comatose patients often the distinctness of periodic IEDs waxes and wanes and could develop into electrographic seizures. However, it is often quite difficult to distinguish GPDs from seizure activity which raises the question if the effect of both GPDs and electrographic seizures is the same for the patient (14)(15)(16) . activity ranges at least from 0.01 Hz to several hundred Hz (17).…”

Section: Transientsmentioning

confidence: 99%

“…Neonatal seizure detection is considered much more difficult compared to adult seizure detection. This is because adult seizures are generally characterized by less complex waveforms, mainly due to the completed maturation of the adult brain [15]. Until now most neonatal seizure detection procedures are based on adapted adult seizure detection systems [7].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the marked EEG differences between neonates and adults, the development of a specific neonatal classifier is warranted [16]. It has been shown that a Support Vector Machine (SVM) classifier trained on neonatal EEG data can successfully be used for the detection of epileptic seizures in adults [15]. Moreover, our recently introduced feature baseline correction (FBC) technique compensates for differences in feature values between neonatal patients [17].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative EEG and machine learning methods for the detection of epileptic seizures and cerebral asymmetry

Bogaarts¹

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. The 10-20 system The electrical cortical activity is usually recorded at equally distributed standardized locations (international 10-20 system) on the scalp (9). The electrode locations are defined by dividing the head into 10% and 20% angular intervals using the inion and nasion as landmarks (figure 1). The first letter of the electrode refers to the brain region it is placed on, and numbers refer to the left (odd) and right (even) hemisphere. Montages Measuring an EEG signal always involves a differential signal between two electrodes. In practice EEG signals are recorded using differential Background EEG The background activity describes the general patterns that are observed in the EEG. These patterns are generally described as rhythms from a certain frequency range. In increasing order of frequency the following frequency bands are used: delta (<4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz) and gamma (>30 Hz) (4). Each frequency band is associated with well-known properties and deviations or abnormalities are mostly related to pathologies or brain damage. Normal background properties are also related to age and circadian rhythm. For example slow activity in the delta and theta range is normal Visual inspection of the EEG Reviewing EEG recordings is a complex and time consuming task which can only be performed by experienced clinicians (4). The EEG is reviewed as multiple time series tiled horizontally, as illustrated in figure 2 and 3. Using a variety of montages, a clinical neurophysiologist looks for abnormalities in background patterns and transient phenomena. Traditionally the EEG is reviewed visually in the time domain. Conventionally, the frequency content is band-pass filtered between 0.5 and 50 Hz although physiological and pathological EEG Figure 6 Example of an SVM classifier separating two classes, red and blue, in a 2dimensional space. The solid line indicates the decision boundary, defined by the Support Vectors. Each Support Vector, indicated with circles, lie on, or within the margin which is indicat...

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Continuous EEG Monitoring for Status Epilepticus

Dhakar

Hirsch

2017

Status Epilepticus

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Generalized periodic epileptiform discharges in critically ill children: Clinical features, and outcome

Cited by 21 publications

References 25 publications

Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part I

Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part I

Quantitative EEG and machine learning methods for the detection of epileptic seizures and cerebral asymmetry

Continuous EEG Monitoring for Status Epilepticus

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