Dataset Information

Seizure identification in the ICU using quantitative EEG displays.

ABSTRACT:

Objective

To evaluate the diagnostic accuracy of 2 quantitative EEG display tools, color density spectral array (CDSA) and amplitude-integrated EEG (aEEG), for seizure identification in the intensive care unit (ICU).

Methods

A set of 27 continuous EEG recordings performed in pediatric ICU patients was transformed into 8-channel CDSA and aEEG displays. Three neurophysiologists underwent 2 hours of training to identify seizures using these techniques. They were then individually presented with a series of CDSA and aEEG displays, blinded to the raw EEG, and asked to mark any events suspected to be seizures. Their performance was compared to seizures identified on the underlying conventional EEG.

Results

The 27 EEG recordings contained 553 discrete seizures over 487 hours. The median sensitivity for seizure identification across all recordings was 83.3% using CDSA and 81.5% using aEEG. However, among individual recordings, the sensitivity ranged from 0% to 100%. Factors reducing the sensitivity included low-amplitude, short, and focal seizures. False-positive rates were generally very low, with misidentified seizures occurring once every 17-20 hours.

Conclusions

Both CDSA and aEEG demonstrate acceptable sensitivity and false-positive rates for seizure identification among critically ill children. Accuracy of these tools would likely improve during clinical use, when findings can be correlated in real-time with the underlying raw EEG. In the hands of neurophysiologists, CDSA and aEEG displays represent useful screening tools for seizures during continuous EEG monitoring in the ICU. The suitability of these tools for bedside use by ICU nurses and physicians requires further study.

SUBMITTER: Stewart CP

PROVIDER: S-EPMC2974462 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Publications

Seizure identification in the ICU using quantitative EEG displays.

Stewart C P CP Otsubo H H Ochi A A Sharma R R Hutchison J S JS Hahn C D CD

Neurology 20100922 17

<h4>Objective</h4>To evaluate the diagnostic accuracy of 2 quantitative EEG display tools, color density spectral array (CDSA) and amplitude-integrated EEG (aEEG), for seizure identification in the intensive care unit (ICU).<h4>Methods</h4>A set of 27 continuous EEG recordings performed in pediatric ICU patients was transformed into 8-channel CDSA and aEEG displays. Three neurophysiologists underwent 2 hours of training to identify seizures using these techniques. They were then individually pre ...[more]

PMID: 20861452

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Project description:BackgroundEpilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure.MethodsDespite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels.ResultsIn patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied.ConclusionsWe conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a "focus") where seizures start.

Project description:ObjectiveAnterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug-resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.MethodsWe analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure-free and non-seizure-free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave-one-patient-out cross-validation scheme within the MC data set and then tested using the out-of-sample CC data set. Finally, we compared the predictive performance of normal scalp EEG-derived features against MRI abnormalities.ResultsWe found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10-25 Hz range. The Naïve Bayes classification based on those features predicted 1-year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10-25 Hz range provided better predictability than other combinations and (b) normal scalp EEG-derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).SignificanceThese results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug-resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

Dataset Information

Seizure identification in the ICU using quantitative EEG displays.

Objective

Methods

Results

Conclusions

Publications

Seizure identification in the ICU using quantitative EEG displays.

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