Interobserver agreement in the interpretation of EEG patterns in critically ill adults

Pitfalls of using video-EEG for a trial endpoint in children aged <4 years with focal seizures

OBJECTIVE

Double-blind, randomized, and placebo-controlled trial SP0967 (NCT02477839/2013-000717-20) did not demonstrate superior efficacy of lacosamide versus placebo in patients aged ≥1 month to <4 years with uncontrolled focal seizures, per ≤72 h video-electroencephalogram (video-EEG)-based primary endpoints (reduction in average daily frequency of focal seizures at end-of-maintenance [EOM] versus end-of-baseline [EOB], patients with ≥50% response). This was unexpected because randomized controlled trial SP0969 (NCT01921205) showed efficacy of lacosamide in patients aged ≥4 to <17 years with uncontrolled focal seizures. SP0969's primary endpoint was based on seizure diary instead of video-EEG, an issue with the latter being inter-reader variability. We evaluated inter-reader agreement in video-EEG interpretation in SP0967, which to our knowledge, are the first such data for very young children with focal seizures from a placebo-controlled trial.

METHODS

Local investigator and central reader agreement in video-EEG interpretation was analyzed post hoc.

RESULTS

Analysis included 105 EOB and 98 EOM video-EEGs. Local investigators and central reader showed poor agreement based on ≥2 focal seizures at EOB (Kappa = 0.01), and fair agreement based on ≥2 focal seizures at EOM (Kappa = 0.23). Local investigator and central reader seizure count interpretations varied substantially, particularly for focal seizures, but also primary generalized and unclassified epileptic seizures, at both timepoints.

INTERPRETATION

High inter-reader variability and low inter-reader reliability of the interpretation of seizure types and counts prevent confident conclusion regarding the lack of efficacy of lacosamide in this population. We recommend studies in very young children do not employ video-EEGs exclusively for accurate study inclusion or as an efficacy measure.

A machine learning approach to seizure detection in a rat model of post-traumatic epilepsy

Epilepsy is a common neurologic condition frequently investigated using rodent models, with seizures identified by electroencephalography (EEG). Given technological advances, large datasets of EEG are widespread and amenable to machine learning approaches for identification of seizures. While such approaches have been explored for human EEGs, machine learning approaches to identifying seizures in rodent EEG are limited. We utilized a predesigned deep convolutional neural network (DCNN), GoogLeNet, to classify images for seizure identification. Training images were generated through multiplexing spectral content (scalograms), kurtosis, and entropy for two-second EEG segments. Over 2200 h of EEG data were scored for the presence of seizures, with 95.6% of seizures identified by the DCNN and a false positive rate of 34.2% (1.52/h), as compared to visual scoring. Multiplexed images were superior to scalograms alone (scalogram-kurtosis-entropy 0.956 ± 0.010, scalogram 0.890 ± 0.028, t(7) = 3.54, p < 0.01) and a DCNN trained specifically for the individual animal was superior to using DCNNs across animals (intra-animal 0.960 ± 0.0094, inter-animal 0.811 ± 0.015, t(30) = 5.54, p < 0.01). For this dataset the DCNN approach is superior to a previously described algorithm utilizing longer local line lengths, calculated from wavelet-decomposition of EEG, to identify seizures. We demonstrate the novel use of a predesigned DCNN constructed to classify images, utilizing multiplexed images of EEG spectral content, kurtosis, and entropy, to rapidly and objectively identifies seizures in a large dataset of rat EEG with high sensitivity.

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.

Electroencephalographic Seizures in Emergency Department Patients After Treatment for Convulsive Status Epilepticus

PURPOSE

It is unknown how often and how early EEG is obtained in patients presenting with status epilepticus. The Established Status Epilepticus Treatment Trial enrolled patients with benzodiazepine-refractory seizures and randomized participants to fosphenytoin, levetiracetam, or valproate. The use of early EEG, including frequency of electrographic seizures, was determined in Established Status Epilepticus Treatment Trial participants.

METHODS

Secondary analysis of 475 enrollments at 58 hospitals to determine the frequency of EEG performed within 24 hours of presentation. The EEG type, the prevalence of electrographic seizures, and characteristics associated with obtaining early EEG were recorded. Chi-square and Wilcoxon rank-sum tests were calculated as appropriate for univariate and bivariate comparisons. Odds ratios are reported with 95% confidence intervals.

RESULTS

A total of 278 of 475 patients (58%) in the Established Status Epilepticus Treatment Trial cohort underwent EEG within 24 hours (median time to EEG: 5 hours [interquartile range: 3-10]). Electrographic seizure prevalence was 14% (95% confidence interval, 10%-19%; 39/278) in the entire cohort and 13% (95% confidence interval, 7%-21%) in the subgroup of patients meeting the primary outcome of the Established Status Epilepticus Treatment Trial (clinical treatment success within 60 minutes of randomization). Among subjects diagnosed with electrographic seizures (39), 15 (38%; 95% confidence interval, 25%-54%) had no clinical correlate on the video EEG recording.

CONCLUSIONS

Electrographic seizures may occur in patients who stop seizing clinically after treatment of convulsive status epilepticus. Clinical correlates might not be present during electrographic seizures. These findings support early initiation of EEG recordings in patients suffering from convulsive status epilepticus, including those with clinical evidence of treatment success.

Quantitative Assessment of Electroencephalogram Reactivity in Comatose Patients on Extracorporeal Membrane Oxygenation

Objective assessment of the brain's responsiveness in comatose patients on Extracorporeal Membrane Oxygenation (ECMO) support is essential to clinical care, but current approaches are limited by subjective methodology and inter-rater disagreement. Quantitative electroencephalogram (EEG) algorithms could potentially assist clinicians, improving diagnostic accuracy. We developed a quantitative, stimulus-based algorithm to assess EEG reactivity features in comatose patients on ECMO support. Patients underwent a stimulation protocol of increasing intensity (auditory, peripheral, and nostril stimulation). A total of 129 20-s EEG epochs were collected from 24 patients (age [Formula: see text], 10 females, 14 males) on ECMO support with a Glasgow Coma Scale[Formula: see text]8. EEG reactivity scores ([Formula: see text]-scores) were calculated using aggregated spectral power and permutation entropy for each of five frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Parameter estimation techniques were applied to [Formula: see text]-scores to identify properties that replicate the decision process of experienced clinicians performing visual analysis. Spectral power changes from audio stimulation were concentrated in the [Formula: see text] band, whereas peripheral stimulation elicited an increase in spectral power across multiple bands, and nostril stimulation changed the entropy of the [Formula: see text] band. The findings of this pilot study on [Formula: see text]-score lay a foundation for a future prediction tool with clinical applications.

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking

Epilepsy affects 3.4 million people in the United States, and, despite the availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impact quality of life. High-frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last 2 decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time consuming and can still produce variable results because of their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed, and accuracy. Here, we present HFOApp: a free, open-source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multichannel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify, and characterize HFOs within electrophysiological datasets.

Can EEG accurately predict 2-year neurodevelopmental outcome for preterm infants?

OBJECTIVE

Establish if serial, multichannel video electroencephalography (EEG) in preterm infants can accurately predict 2-year neurodevelopmental outcome.

DESIGN AND PATIENTS

EEGs were recorded at three time points over the neonatal course for infants <32 weeks' gestational age (GA). Monitoring commenced soon after birth and continued over the first 3 days. EEGs were repeated at approximately 32 and 35 weeks' postmenstrual age (PMA). EEG scores were based on an age-specific grading scheme. Clinical score of neonatal morbidity risk and cranial ultrasound imaging were completed.

SETTING

Neonatal intensive care unit at Cork University Maternity Hospital, Ireland.

MAIN OUTCOME MEASURES

Bayley Scales of Infant Development III at 2 years' corrected age.

RESULTS

Sixty-seven infants were prospectively enrolled in the study and 57 had follow-up available (median GA 28.9 weeks (IQR 26.5-30.4)). Forty had normal outcome, 17 had abnormal outcome/died. All EEG time points were individually predictive of abnormal outcome; however, the 35-week EEG performed best. The area under the receiver operating characteristic curve (AUC) for this time point was 0.91 (95% CI 0.83 to 1), p<0.001. Comparatively, the clinical course AUC was 0.68 (95% CI 0.54 to 0.80, p=0.015), while abnormal cranial ultrasound was 0.58 (95% CI 0.41 to 0.75, p=0.342).

CONCLUSION

Multichannel EEG is a strong predictor of 2-year outcome in preterm infants particularly when recorded around 35 weeks' PMA. Infants at high risk of brain injury may benefit from early postnatal EEG recording which, if normal, is reassuring. Postnatal clinical complications can contribute to poor outcome; therefore, we state that a later EEG around 35 weeks has a role to play in prognostication.

Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists

We aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.

Electroencephalography Technologist Inter-rater Agreement and Interpretation of Pediatric Critical Care Electroencephalography

OBJECTIVES

Electroencephalography (EEG) technologists commonly screen continuous EEG. Until now, the inter-rater agreement or sensitivity for important EEG findings has been unknown in this group.

METHODS

Twenty-nine EEG technologists and three clinical neurophysiologists interpreted 90 five-minute samples of pediatric critical care EEG. Inter-rater agreement was examined with Cohen's kappa and Fleiss' kappa for EEG findings. A gold-standard consensus agreement was developed for examining sensitivity and specificity for seizures or discontinuity. Kruskal-Wallis tests with Benjamani-Hochberg corrections for multiple comparisons were utilized to examine associations between correct scoring and certification status and years of experience.

RESULTS

Aggregate agreement was moderate for seizures and fair for EEG background continuity among EEG technologists. Individual agreement for seizures and continuity varied from slight to substantial. For individual EEG technologists, sensitivity for seizures ranged from 44 to 93% and sensitivity for continuity ranged from 81 to 100%. Raters with Certified Long Term Monitoring credentials were more likely to identify seizures correctly.

SIGNIFICANCE

This is the first study to evaluate inter-rater agreement and interpretation correctness among EEG technologists interpreting pediatric critical care EEG. EEG technologists demonstrated better aggregate agreement for seizure detection than other EEG findings, yet individual results and internal consistency varied widely. These data provide important insight into the common practice of utilizing EEG technologists for screening critical care EEG.

Übersetzung der Standardisierten Terminologie für EEG bei Intensivstationspatienten der American Clinical Neurophysiological Society: Version 2012 (Hirsch et al. American Clinical Neurophysiology Society’s Standardized Critical Care EEG Terminology: 2012 version. J Clin Neurophysiol 2013; 30: 1–27)

2012 erarbeitete eine EEG-Expertengruppe der American Clinical Neurophysiology Society (ACNS) eine standardisierte Terminologie für EEG Muster, die bei kritisch kranken Patienten häufig sind. Bis dahin existierte keine einheitlich akzeptierte Nomenklatur für diese EEG Muster, wie zum Beispiel periodische Entladungen, fluktuierende rhythmische Muster und Kombinationen der beiden. Dabei bestand auch kein Konsens welche Muster mit welchem neuronalen Schaden korrelieren, welche Muster behandelt werden müssen oder wie aggressiv die Behandlung erfolgen sollte. Um diese Fragen anzugehen entwickelte diese Gruppe eine standardisierte Terminologie zunächst für den wissenschaftlichen Einsatz. Ziel war es die Kommunikation zu erleichtern, indem Begriffe mit klinischer Konnotation vermieden wurden und um damit multizentrische Forschung zu erleichtern.

MEG Reporting

The report generated by the magnetoencephalographer's interpretation of the patient's magnetoencephalography examination is the magnetoencephalography laboratory's most important product and is a representation of the quality of the laboratory and the clinical acumen of the personnel. A magnetoencephalography report is not meant to enumerate all the technical details that went into the test nor to fulfill some imagined requirements of the electronic health record. It is meant to clearly and concisely answer the clinical question posed by the referring doctor and to convey the key findings that may inform the next step in the patient's care. The graphical component of a magnetoencephalography report is ordinarily the most welcomed by the referring doctor. Much of the text of the report may be glossed over, so the illustrations must be sufficiently annotated to provide clear and unambiguous findings. The particular images chosen for the report will be a function of the analysis software but should be selected and edited for maximum clarity. There should be a composite pictorial summary slide at the beginning or at the end of the report, which accurately conveys the gist of the report. Along with representative source localizations, reports should contain examples of the simultaneously recorded EEG that enable the referring physician to determine whether epileptic discharges occurred and whether they are consistent with the patient's previously recorded spikes. Information and images (e.g., statistics, magnetic field patterns) that provide convincing evidence of the validity of the source location should also be included.

A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns

OBJECTIVE

To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature.

METHODS

We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA.

RESULTS

Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49).

CONCLUSIONS

This EEG spectrogram nomenclature has high IRA among electroencephalographers.

SIGNIFICANCE

The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Bedside interpretation of simplified continuous EEG after cardiac arrest

BACKGROUND

Continuous EEG-monitoring (cEEG) in the ICU is recommended to assess prognosis and detect seizures after cardiac arrest but implementation is often limited by the lack of EEG-technicians and experts. The aim of the study was to assess ICU physicians ability to perform preliminary interpretations of a simplified cEEG in the post cardiac arrest setting.

METHODS

Five ICU physicians received training in interpretation of simplified cEEG - total training duration 1 day. The ICU physicians then interpreted 71 simplified cEEG recordings from 37 comatose survivors of cardiac arrest. The cEEG included amplitude-integrated EEG trends and two channels with original EEG-signals. Basic EEG background patterns and presence of epileptiform discharges or seizure activity were assessed on 5-grade rank-ordered scales based on standardized EEG terminology. An EEG-expert was used as reference.

RESULTS

There was substantial agreement (κ 0.69) for EEG background patterns and moderate agreement (κ 0.43) for epileptiform discharges between ICU physicians and the EEG-expert. Sensitivity for detecting seizure activity by ICU physicians was limited (50%), but with high specificity (87%).

CONCLUSIONS

After cardiac arrest, preliminary bedside interpretations of simplified cEEGs by trained ICU physicians may allow earlier detection of clinically relevant cEEG changes, prompting changes in patient management as well as additional evaluation by an EEG-expert. This strategy requires awareness of limitations of both the simplified electrode montage and the cEEG interpretations performed by ICU physicians. cEEG evaluation by an expert should not be delayed.

A standardised assessment scheme for conventional EEG in preterm infants

OBJECTIVE

To develop a standardised scheme for assessing normal and abnormal electroencephalography (EEG) features of preterm infants. To assess the interobserver agreement of this assessment scheme.

METHODS

We created a standardised EEG assessment scheme for 6 different post-menstrual age (PMA) groups using 4 EEG categories. Two experts, not involved in the development of the scheme, evaluated this on 24 infants <32 weeks gestational age (GA) using random 2 hour EEG epochs. Where disagreements were found, the features were checked and modified. Finally, the two experts independently evaluated 2 hour EEG epochs from an additional 12 infants <37 weeks GA. The percentage of agreement was calculated as the ratio of agreements to the sum of agreements plus disagreements.

RESULTS

Good agreement in all patients and EEG feature category was obtained, with a median agreement between 80% and 100% over the 4 EEG assessment categories. No difference was found in agreement rates between the normal and abnormal features (p = 0.959).

CONCLUSIONS

We developed a standard EEG assessment scheme for preterm infants that shows good interobserver agreement.

SIGNIFICANCE

This will provide information to Neonatal Intensive Care Unit (NICU) staff about brain activity and maturation. We hope this will prove useful for many centres seeking to use neuromonitoring during critical care for preterm infants.

Quantitative EEG Metrics Differ Between Outcome Groups and Change Over the First 72 h in Comatose Cardiac Arrest Patients

BACKGROUND

Forty to sixty-six percent of patients resuscitated from cardiac arrest remain comatose, and historic outcome predictors are unreliable. Quantitative spectral analysis of continuous electroencephalography (cEEG) may differ between patients with good and poor outcomes.

METHODS

Consecutive patients with post-cardiac arrest hypoxic-ischemic coma undergoing cEEG were enrolled. Spectral analysis was conducted on artifact-free contiguous 5-min cEEG epochs from each hour. Whole band (1-30 Hz), delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), α/δ power ratio, percent suppression, and variability were calculated and correlated with outcome. Graphical patterns of quantitative EEG (qEEG) were described and categorized as correlating with outcome. Clinical outcome was dichotomized, with good neurologic outcome being consciousness recovery.

RESULTS

Ten subjects with a mean age = 50 yrs (range = 18-65) were analyzed. There were significant differences in total power (3.50 [3.30-4.06] vs. 0.68 [0.52-1.02], p = 0.01), alpha power (1.39 [0.66-1.79] vs 0.27 [0.17-0.48], p < 0.05), delta power (2.78 [2.21-3.01] vs 0.55 [0.38-0.83], p = 0.01), percent suppression (0.66 [0.02-2.42] vs 73.4 [48.0-97.5], p = 0.01), and multiple measures of variability between good and poor outcome patients (all values median [IQR], good vs. poor). qEEG patterns with high or increasing power or large power variability were associated with good outcome (n = 6). Patterns with consistently low or decreasing power or minimal power variability were associated with poor outcome (n = 4).

CONCLUSIONS

These preliminary results suggest qEEG metrics correlate with outcome. In some patients, qEEG patterns change over the first three days post-arrest.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring

PURPOSE

Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns.

METHODS

Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients.

RESULTS

Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68).

CONCLUSIONS

This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

Definition and Classification of Periodic and Rhythmic Patterns

The growing use of continuous video-EEG recording in the inpatient setting, in particular in patients with varying degrees of encephalopathy, has yielded a window to the brain with an excellent temporal resolution. This increasingly available tool has become more than an instrument to detect nonconvulsive seizures (its primary use), and clinical indications span from ischemia detection in acute brain injuries, neuroprognostication of comatose patients, to monitoring the degree of encephalopathy. In this context, abnormal findings such as periodic discharges and rhythmic delta activity were increasingly recognized; however, significant subjectivity remained in the interpretation of these findings pertaining to key features regarding their spatial involvement, prevalence of occurrence, duration, associated morphologic features, and behavior. In 2005, the American Clinical Neurophysiology Society proposed standardized definitions and classification of electroencephalographic rhythmic and periodic patterns. This was subsequently revised in 2011 and in 2012 and is now being used by centers worldwide, with the final version published in early 2013 as an official guideline of the ACNS. The resulting uniform terminology has allowed for significant advances in the understanding of the pathophysiology, epileptogenic potential, and overall clinical implication of these patterns. Investigators across multiple institutions are now able to collaborate while exploring diagnostic and therapeutic algorithms to these patterns, an effort that may soon provide definitive evidence guiding treating clinicians on the management of these patients.

Stability of Early EEG Background Patterns After Pediatric Cardiac Arrest

PURPOSE

We aimed to determine whether EEG background characteristics remain stable across discrete time periods during the acute period after resuscitation from pediatric cardiac arrest.

METHODS

Children resuscitated from cardiac arrest underwent continuous conventional EEG monitoring. The EEG was scored in 12-hour epochs for up to 72 hours after return of circulation by an electroencephalographer using a Background Category with 4 levels (normal, slow-disorganized, discontinuous/burst-suppression, or attenuated-featureless) or 2 levels (normal/slow-disorganized or discontinuous/burst-suppression/attenuated-featureless). Survival analyses and mixed-effects ordinal logistic regression models evaluated whether the EEG remained stable across epochs.

RESULTS

EEG monitoring was performed in 89 consecutive children. When EEG was assessed as the 4-level Background Category, 30% of subjects changed category over time. Based on initial Background Category, one quarter of the subjects changed EEG category by 24 hours if the initial EEG was attenuated-featureless, by 36 hours if the initial EEG was discontinuous or burst-suppression, by 48 hours if the initial EEG was slow-disorganized, and never if the initial EEG was normal. However, regression modeling for the 4-level Background Category indicated that the EEG did not change over time (odds ratio = 1.06, 95% confidence interval = 0.96-1.17, P = 0.26). Similarly, when EEG was assessed as the 2-level Background Category, 8% of subjects changed EEG category over time. However, regression modeling for the 2-level category indicated that the EEG did not change over time (odds ratio = 1.02, 95% confidence interval = 0.91-1.13, P = 0.75).

CONCLUSIONS

The EEG Background Category changes over time whether analyzed as 4 levels (30% of subjects) or 2 levels (8% of subjects), although regression analyses indicated that no significant changes occurred over time for the full cohort. These data indicate that the Background Category is often stable during the acute 72 hours after pediatric cardiac arrest and thus may be a useful EEG assessment metric in future studies, but that some subjects do have EEG changes over time and therefore serial EEG assessments may be informative.

EEG Factors After Pediatric Cardiac Arrest

PURPOSE

We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest.

METHODS

Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-hour epochs for up to 72-hours after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs.

RESULTS

We performed EEG monitoring in 89 consecutive children, which yielded 453 EEG segments. We identified two factors, which were not correlated. The background features were factor loaded with the features continuity, voltage, and frequency. The intermittent features were factor loaded with the features of seizures, periodic patterns, and interictal discharges. Factor scores were calculated for each EEG segment. Linear, mixed-effect, regression results indicated that the factor scores did not change over time for the background features factor (coefficient, 0.18; 95% confidence interval, 0.04-0.07; P = 0.52) or the intermittent features factor (coefficient, -0.003; 95% confidence interval, -0.02 to 0.01; P = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores.

CONCLUSIONS

Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple background and intermittent features. Furthermore, the factor scores would be expected to remain stable during the acute period for most subjects.

Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review

Background

Electroencephalography (EEG) is a well-established tool for assessing brain function that is available at the bedside in the intensive care unit (ICU). This review aims to discuss the relevance of electroencephalographic reactivity (EEG-R) in patients with impaired consciousness and to describe the neurophysiological mechanisms involved.

Methods

We conducted a systematic search of the term “EEG reactivity and coma” using the PubMed database. The search encompassed articles published from inception to March 2018 and produced 202 articles, of which 42 were deemed relevant, assessing the importance of EEG-R in relationship to outcomes in patients with impaired consciousness, and were therefore included in this review.

Results

Although definitions, characteristics and methods used to assess EEG-R are heterogeneous, several studies underline that a lack of EEG-R is associated with mortality and unfavorable outcome in patients with impaired consciousness. However, preserved EEG-R is linked to better odds of survival. Exploring EEG-R to nociceptive, auditory, and visual stimuli enables a noninvasive trimodal functional assessment of peripheral and central sensory ascending pathways that project to the brainstem, the thalamus and the cerebral cortex. A lack of EEG-R in patients with impaired consciousness may result from altered modulation of thalamocortical loop activity by afferent sensory input due to neural impairment. Assessing EEG-R is a valuable tool for the diagnosis and outcome prediction of severe brain dysfunction in critically ill patients.

Conclusions

This review emphasizes that whatever the etiology, patients with impaired consciousness featuring a reactive electroencephalogram are more likely to have a favorable outcome, whereas those with a nonreactive electroencephalogram are prone to having an unfavorable outcome. EEG-R is therefore a valuable prognostic parameter and warrants a rigorous assessment. However, current assessment methods are heterogeneous, and no consensus exists. Standardization of stimulation and interpretation methods is needed.

American Clinical Neurophysiology Society Guideline 7: Guidelines for EEG Reporting

This EEG Guideline incorporates the practice of structuring a report of results obtained during routine adult electroencephalography. It is intended to reflect one of the current practices in reporting an EEG and serves as a revision of the previous guideline entitled "Writing an EEG Report." The goal of this guideline is not only to convey clinically relevant information, but also to improve interrater reliability for clinical and research use by standardizing the format of EEG reports. With this in mind, there is expanded documentation of the patient history to include more relevant clinical information that can affect the EEG recording and interpretation. Recommendations for the technical conditions of the recording are also enhanced to include post hoc review parameters and type of EEG recording. Sleep feature documentation is also expanded upon. More descriptive terms are included for background features and interictal discharges that are concordant with efforts to standardize terminology. In the clinical correlation section, examples of common clinical scenarios are now provided that encourages uniformity in reporting. Including digital samples of abnormal waveforms is now readily available with current EEG recording systems and may be beneficial in augmenting reports when controversial waveforms or important features are encountered.

Interrater Agreement of EEG Interpretation After Pediatric Cardiac Arrest Using Standardized Critical Care EEG Terminology

PURPOSE

We evaluated interrater agreement of EEG interpretation in a cohort of critically ill children resuscitated after cardiac arrest using standardized EEG terminology.

METHODS

Four pediatric electroencephalographers scored 10-minute EEG segments from 72 consecutive children obtained 24 hours after return of circulation using the American Clinical Neurophysiology Society's (ACNS) Standardized Critical Care EEG terminology. The percent of perfect agreement and the kappa coefficient were calculated for each of the standardized EEG variables and a predetermined composite EEG background category.

RESULTS

The overall background category (normal, slow-disorganized, discontinuous, or attenuated-featureless) had almost perfect agreement (kappa 0.89).The ACNS Standardized Critical Care EEG variables had agreement that was (1) almost perfect for the seizures variable (kappa 0.93), (2) substantial for the continuity (kappa 0.79), voltage (kappa 0.70), and sleep transient (kappa 0.65) variables, (3) moderate for the rhythmic or periodic patterns (kappa 0.55) and interictal epileptiform discharge (kappa 0.60) variables, and (4) fair for the predominant frequency (kappa 0.23) and symmetry (kappa 0.31) variables. Condensing variable options led to improved agreement for the continuity and voltage variables.

CONCLUSIONS

These data support the use of the standardized terminology and the composite overall background category as a basis for standardized EEG interpretation for subsequent studies assessing EEG background for neuroprognostication after pediatric cardiac arrest.

The Nature of EEG Reactivity to Light, Sound, and Pain Stimulation in Neurosurgical Comatose Patients Evaluated by a Quantitative Method

EEG reactivity (EEG-R) is regarded as an important parameter in coma prognosis but knowledge is sparse on the nature of EEG changes due to different kinds of stimulation and their prognostic significance. EEG-R was quantified in a study of 39 comatose neurosurgical patients. Six 30-second standardized visual, auditory, and painful stimulations were applied. EEG-R in the delta, theta, alpha, and beta band was normalized in z-scores as the power of a stimulation epoch relative to average power of 6 resting epochs. Outcome measure was 3 months Glasgow Outcome Scale. Increase in EEG activity was related to poor outcome, was more common (13.4% of tests), and grew continuously during the 30-second stimulation epoch. Decrease in EEG activity was related to good outcome, was rarer (2.5%), and peaked around 15 seconds. Pain was the most provocative stimulation (20.4%) followed by sound (8.7%) and eye-opening (6.7%). Discrimination between good (n = 6) and poor (n = 33) outcome was best in the theta and alpha bands for pain stimulation in the first 10-20 seconds and for sound stimulation in the first 5 to 10 seconds, eye-opening did not discriminate. Increase in activity predicted poor outcome with a high specificity 100% (CI = 52%-100%) and a modest sensitivity of 39% (CI = 23%-58%). Decrease in activity predicted good outcome with a high specificity of 100% (CI = 87%-100%) and a modest sensitivity of 33% (CI = 6%-76%). This quantitative study reveals new knowledge about the nature of EEG-R, which contribute to the development of more reliable and objective clinical procedures for outcome prediction.

Electroencephalographic Patterns Recorded by Continuous EEG Monitoring in Patients with Change of Consciousness in the Neurological Intensive Care Unit

INTRODUCTION

Our aim was to examine the frequency of various electrographic patterns including periodic discharges (PD), repetitive spike waves (RSW), rhythmic delta activities (RDA), nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE) in continuous EEG monitoring (cEEG) of the critically ill patients with change of consciousness and the presence of specific clinical and laboratory findings associated with these important patterns in this study.

METHODS

Patients with changes of consciousness in the neurological intensive care unit (NICU) were consecutively monitored with cEEG during 2 years. Their clinical, electrophysiological, radiological and laboratory findings were evaluated retrospectively.

RESULTS

This sample consisted of 57 (25 men) patients with a mean age of 68.2 years. Mean duration of cEEG monitoring was 2532.6 minutes. The most common electrographic patterns were PD (33%) and NCS-NCSE (26.3%). The presence of NCS-NCSE was significantly associated with PD (57.9%, p<0.001). PD and NCS-NCSE were the mostly seen in patients with acute stroke and hypoxic encephalopathy. Duration of monitoring was significantly longer in the group with PD and NCS-NCSE (p:0.004, p:0.014). Detection of any electrographic pattern in EEG before monitoring was associated with the presence of any pattern in cEEG (59.3%, p<0.0001). Convulsive or nonconvulsive seizure during monitoring was common in patients with electrographic patterns (p<0.0001). 66.7% of NCS-NCSE was seen within the first 12 hours and 26.7% was seen within the 12-24 hours of the monitoring.

CONCLUSION

Detection of any electrographic pattern in EEG before monitoring was associated with the presence of any important pattern in cEEG monitoring. This association suggest that at least 24 hours-monitoring of these patients could be useful for the diagnosis of clinical and/or electrographic seizures.

American Clinical Neurophysiology Society Guideline 7: Guidelines for EEG Reporting

This EEG Guideline incorporates the practice of structuring a report of results obtained during routine adult electroencephalography. It is intended to reflect one of the current practices in reporting an EEG and serves as a revision of the previous guideline entitled "Writing an EEG Report." The goal of this guideline is not only to convey clinically relevant information, but also to improve interrater reliability for clinical and research use by standardizing the format of EEG reports. With this in mind, there is expanded documentation of the patient history to include more relevant clinical information that can affect the EEG recording and interpretation. Recommendations for the technical conditions of the recording are also enhanced to include post hoc review parameters and type of EEG recording. Sleep feature documentation is also expanded upon. More descriptive terms are included for background features and interictal discharges that are concordant with efforts to standardize terminology. In the clinical correlation section, examples of common clinical scenarios are now provided that encourages uniformity in reporting. Including digital samples of abnormal waveforms is now readily available with current EEG recording systems and may be beneficial in augmenting reports when controversial waveforms or important features are encountered.

ACNS Critical Care EEG Terminology: Value, Limitations, and Perspectives

The interpretation of the EEG in the critically ill remains a clinical challenge. Because continuous EEG monitoring plays an increasing role in patients' care, it is important that research efforts investigate the clinical significance of periodic and rhythmic discharges and of background abnormalities. The 2012 American Clinical Neurophysiology Society critical care EEG terminology was designed to provide a comprehensive and objective vocabulary for that purpose. The interrater reliability of most of the proposed terms has been established, confirming that they represent a solid basis for research. Studies using the terminology have already started to define the clinical and prognostic values of several known or newly described EEG patterns. Yet, as the field of critical care EEG evolves, improvements will be required to further enhance the clarity of the terminology and incorporate new findings from ongoing research.

Understanding and Managing the Ictal-Interictal Continuum in Neurocritical Care

OPINION STATEMENT

Continuous electroencephalographic (EEG) monitoring has become an invaluable tool for the assessment of brain function in critically ill patients. However, interpretation of EEG waveforms, especially in the intensive care unit (ICU) setting is fraught with ambiguity. The term ictal-interictal continuum encompasses EEG patterns that are potentially harmful and can cause neuronal injury. There are no clear guidelines on how to treat EEG patterns that lie on this continuum. We advocate the following approaches in a step wise manner: (1) identify and exclude clear electrographic seizures and status epilepticus (SE), i.e., generalized spike-wave discharges at 3/s or faster; and clearly evolving discharges of any type (rhythmic, periodic, fast activity), whether focal or generalized; (2) exclude clear interictal patterns, i.e., spike-wave discharges, periodic discharges, and rhythmic patterns at 1/s or slower with no evolution, unless accompanied by a clear clinical correlate, which would make them ictal regardless of the frequency; (3) consider any EEG patterns that lie in between the above two categories as being on the ictal-interictal continuum; (4) compare the electrographic pattern of the ictal-incterictal continuum to the normal background and unequivocal seizures (if present) from the same patient; (5) when available, correlate ictal-interictal continuum pattern with other markers of neuronal injury such as neuronal specific enolase (NSE) levels, brain imaging findings, depth electrode recordings, data from microdialysis, intracranial pressure fluctuations, and brain oxygen measurement; and (6) perform a diagnostic trial with preferably a nonsedating antiepileptic drug with the endpoint being both clinical and electrographic improvement. Minimize the use of anesthetics or multiple AEDs unless there is clear supporting evidence from ancillary tests or worsening of the EEG patterns over time, which could indicate possible neuronal injury.

Prospective assessment and validation of rhythmic and periodic pattern detection in NeuroTrend: A new approach for screening continuous EEG in the intensive care unit

BACKGROUND

NeuroTrend is a computational method that analyzes long-term scalp EEGs in the ICU according to ACNS standardized critical care EEG terminology (CCET) including electrographic seizures. At present, it attempts to become a screening aid for continuous EEG (cEEG) recordings in the ICU to facilitate the review process and optimize resources.

METHODS

A prospective multicenter study was performed in two neurological ICUs including 68 patients who were subjected to video-cEEG. Two reviewers independently annotated the first minute of each hour in the cEEG according to CCET. These segments were also screened for faster patterns with frequencies higher than 4 Hz. The matching annotations (2911 segments) were then used as gold standard condition to test sensitivity and specificity of the rhythmic and periodic pattern detection of NeuroTrend.

RESULTS

Interrater agreement showed substantial agreement for localization (main term 1) and pattern type (main term 2) of the CCET. The overall detection sensitivity of NeuroTrend was 94% with high detection rates for periodic discharges (PD = 80%) and rhythmic delta activity (RDA = 82%). Overall specificity was moderate (67%) mainly because of false positive detections of RDA in cases of general slowing. In contrast, a detection specificity of 88% for PDs was reached. Localization revealed only a slight agreement between reviewers and NeuroTrend.

CONCLUSIONS

NeuroTrend might be a suitable screening tool for cEEG in the ICU and has the potential to raise efficiency of long-term EEG monitoring in the ICU. At this stage, pattern localization and differentiation between RDA and general slowing need improvement. This article is part of a Special Issue entitled "Status Epilepticus".

Quantification of EEG reactivity in comatose patients

OBJECTIVE

EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches.

METHODS

In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis.

RESULTS

The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case.

CONCLUSION

Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity.

SIGNIFICANCE

Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity.

Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE

This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings.

METHODS

Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC).

RESULTS

Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test.

CONCLUSIONS

IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA.

SIGNIFICANCE

This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

The standardization debate: A conflation trap in critical care electroencephalography

PURPOSE

Persistent uncertainty over the clinical significance of various pathological continuous electroencephalography (cEEG) findings in the intensive care unit (ICU) has prompted efforts to standardize ICU cEEG terminology and an ensuing debate. We set out to understand the reasons for, and a satisfactory resolution to, this debate.

METHOD

We review the positions for and against standardization, and examine their deeper philosophical basis.

RESULTS

We find that the positions for and against standardization are not fundamentally irreconcilable. Rather, both positions stem from conflating the three cardinal steps in the classic approach to EEG, which we term "description", "interpretation", and "prescription". Using real-world examples we show how this conflation yields muddled clinical reasoning and unproductive debate among electroencephalographers that is translated into confusion among treating clinicians. We propose a middle way that judiciously uses both standardized terminology and clinical reasoning to disentangle these critical steps and apply them in proper sequence.

CONCLUSION

The systematic approach to ICU cEEG findings presented herein not only resolves the standardization debate but also clarifies clinical reasoning by helping electroencephalographers assign appropriate weights to cEEG findings in the face of uncertainty.

Small-world characteristics of EEG patterns in post-anoxic encephalopathy

Post-anoxic encephalopathy (PAE) has a heterogenous outcome which is difficult to predict. At present, it is possible to predict poor outcome using somatosensory evoked potentials in only a minority of the patients at an early stage. In addition, it remains difficult to predict good outcome at an early stage. Network architecture, as can be quantified with continuous electroencephalography (cEEG), may serve as a candidate measure for predicting neurological outcome. Here, we explore whether cEEG monitoring can be used to detect the integrity of neural network architecture in patients with PAE after cardiac arrest. From 56 patients with PAE treated with mild therapeutic hypothermia, 19-channel cEEG data were recorded starting as soon as possible after cardiac arrest. Adjacency matrices of shared frequencies between 1 and 25 Hz of the EEG channels were obtained using Fourier transformations. Number of network nodes and connections, clustering coefficient (C), average path length (L), and small-world index (SWI) were derived. Outcome was quantified by the best cerebral performance category (CPC)-score within 6 months. Compared to non-survivors, survivors showed significantly more nodes and connections. L was significantly higher and C and SWI were significantly lower in the survivor group than in the non-survivor group. The number of nodes, connections, and the L were negatively correlated with the CPC-score. C and SWI correlated positively with the CPC-score. The combination of number of nodes, connections, C, and L showed the most significant difference and correlation between survivors and non-survivors and CPC-score. Our data might implicate that non-survivors have insufficient distribution and differentiation of neural activity for regaining normal brain function. These network differences, already present during hypothermia, might be further developed as early prognostic markers. The predictive values are however still inferior to current practice parameters.

Interrater agreement for Critical Care EEG Terminology

OBJECTIVE

The interpretation of critical care electroencephalography (EEG) studies is challenging because of the presence of many periodic and rhythmic patterns of uncertain clinical significance. Defining the clinical significance of these patterns requires standardized terminology with high interrater agreement (IRA). We sought to evaluate IRA for the final, published American Clinical Neurophysiology Society (ACNS)-approved version of the critical care EEG terminology (2012 version). Our evaluation included terms not assessed previously and incorporated raters with a broad range of EEG reading experience.

METHODS

After reviewing a set of training slides, 49 readers independently completed a Web-based test consisting of 11 identical questions for each of 37 EEG samples (407 questions). Questions assessed whether a pattern was an electrographic seizure; pattern location (main term 1), pattern type (main term 2); and presence and classification of eight other key features ("plus" modifiers, sharpness, absolute and relative amplitude, frequency, number of phases, fluctuation/evolution, and the presence of "triphasic" morphology).

RESULTS

IRA statistics (κ values) were almost perfect (90-100%) for seizures, main terms 1 and 2, the +S modifier (superimposed spikes/sharp waves or sharply contoured rhythmic delta activity), sharpness, absolute amplitude, frequency, and number of phases. Agreement was substantial for the +F (superimposed fast activity) and +R (superimposed rhythmic delta activity) modifiers (66% and 67%, respectively), moderate for triphasic morphology (58%), and fair for evolution (21%).

SIGNIFICANCE

IRA for most terms in the ACNS critical care EEG terminology is high. These terms are suitable for multicenter research on the clinical significance of critical care EEG patterns. A PowerPoint slide summarizing this article is available for download in the Supporting Information section http://dx.doi.org/10.1111/epi.12653/supinfo.

Diagnostic accuracy of microEEG: a miniature, wireless EEG device

Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device ("microEEG") to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). Two hundred twenty-five ED patients with AMS underwent 3 EEGs. Two EEGs, EEG1 (Nicolet Monitor, "reference") and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. The remaining study, EEG3, was recorded with microEEG using an electrode cap immediately before or after EEG1/EEG2. The official EEG1 interpretation was considered the gold standard (EEG1-GS). EEG1, 2, and 3 were de-identified and blindly interpreted by two independent readers. A generalized mixed linear model was used to estimate the sensitivity and specificity of these interpretations relative to EEG1-GS and to compute a diagnostic odds ratio (DOR). Seventy-nine percent of EEG1-GS were abnormal. Neither the DOR nor the κf representing interrater reliabilities differed significantly between EEG1, EEG2, and EEG3. The mean setup time was 27 min for EEG1/EEG2 and 12 min for EEG3. The mean electrode impedance of EEG3 recordings was 12.6 kΩ (SD: 31.9 kΩ). The diagnostic accuracy of microEEG was comparable to that of the reference system and was not reduced when the EEG electrodes had high and unbalanced impedances. A common practice with many scientific instruments, measurement of EEG device DA provides an independent and quantitative assessment of device performance.

EEG interpretation reliability and interpreter confidence: a large single-center study

The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.

Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial

OBJECTIVES

Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the impact of EEG on clinical management and outcomes of ED patients with AMS.

METHODS

This was a randomized controlled trial at two urban teaching hospitals. Adult patients (≥18 years old) with AMS were included. Excluded patients had immediately correctable AMS (e.g., hypoglycemia) or were admitted before enrollment. Patients were randomized to routine care (control) or routine care plus EEG (intervention). Research assistants used a scalp electrode set with a miniature, wireless EEG device (microEEG) to record standard 30-minute EEGs at presentation, and results were reported to the ED attending physician by an off-site epileptologist within 30 minutes. Primary outcomes included changes in ED management (differential diagnosis, diagnostic work-up, and treatment plan from enrollment to disposition) as determined by surveying the treating physicians. Secondary outcomes were length of ED and hospital stay, intensive care unit (ICU) requirement, and in-hospital mortality.

RESULTS

A total of 149 patients were enrolled (76 control and 73 intervention). Patients in the two groups were comparable at baseline. EEG in the intervention group revealed abnormal findings in 93% (95% confidence interval [CI] = 85% to 97%), including NCS in 5% (95% CI = 2% to 13%). Using microEEG was associated with change in diagnostic work-up in 49% (95% CI = 38% to 60%) of cases and therapeutic plan in 42% (95% CI = 31% to 53%) of cases immediately after the release of EEG results. Changes in probabilities of differential diagnoses and the secondary outcomes were not statistically significant between the groups.

CONCLUSIONS

An EEG can be obtained in the ED with minimal resources and can affect clinical management of AMS patients.

Best-case kappa scores calculated retrospectively from EEG report databases

PURPOSE

The most popular metric for interrater reliability in electroencephalography is the kappa (κ) score. κ calculation is laborious, requiring EEG readers to read the same EEG studies. We introduce a method to determine the best-case κ score (κBEST) for measuring interrater reliability between EEG readers, retrospectively.

METHODS

We incorporated 1 year of EEG reports read by four adult EEG readers at our institution. We used SQL queries to determine EEG findings for subsequent analysis. We generated logistic regression models for particular EEG findings, dependent on patient age, location acuity, and EEG reader. We derived a novel measure, the κBEST statistic, from the logistic regression coefficients.

RESULTS

Increasing patient age and location acuity were associated with decreased sleep and increased diffuse abnormalities. For certain findings, EEG readers exhibited the dominant influence, manifesting directly as lower between-reader κBEST scores for certain EEG findings. Within-reader κBEST control scores were higher than between-reader scores, suggesting internal consistency.

CONCLUSIONS

The κBEST metric can measure significant interrater reliability differences between any number of EEG readers and reports, retrospectively, and is generalizable to other domains (e.g., pathology or radiology reporting). We suggest using this metric as a guide or starting point for focused quality control efforts.