Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures

Spike height improves prediction of future seizure risk

OBJECTIVE

We evaluated whether interictal epileptiform discharge (IED) rate and morphological characteristics predict seizure risk.

METHODS

We evaluated 10 features from automatically detectable IEDs in a stereotyped population with self-limited epilepsy with centrotemporal spikes (SeLECTS). We tested whether the average value or the most extreme values from each feature predicted future seizure risk in cross-sectional and longitudinal models.

RESULTS

10,748 individual centrotemporal IEDs were analyzed from 59 subjects at 81 timepoints. In cross-sectional models, increases in average spike height, spike duration, slow wave rising slope, slow wave falling slope, and the most extreme values of slow wave rising slope each improved prediction of an increased risk of a future seizure compared to a model with age alone (p < 0.05, each). In longitudinal model, spike rising height improved prediction of future seizure risk compared to a model with age alone (p = 0.04) CONCLUSIONS: Spike height improves prediction of future seizure risk in SeLECTS. Several other morphological features may also improve prediction and should be explored in larger studies.

SIGNIFICANCE

Discovery of a relationship between novel IED features and seizure risk may improve clinical prognostication, visual and automated IED detection strategies, and provide insights into the underlying neuronal mechanisms that contribute to IED pathology.

A New Score for Sharp Discharges in the EEG Predicts Epilepsy

PURPOSE

A challenge in EEG interpretation is to correctly classify suspicious focal sharp activity as epileptiform or not. A predictive score was developed from morphologic features of the first focal sharp discharge, which can help in this decision.

METHODS

From a clinical standard EEG database, the authors identified 2,063 patients without a previous epilepsy diagnosis who had a focal sharp discharge in their EEG. Morphologic features (amplitude, area of slow wave, etc.) were extracted using an open source one-click algorithm in EEGLAB, masked to clinical classification. A score was developed from these features and validated with the clinical diagnosis of epilepsy over 2 to 6 years of follow-up. Independent external validation was performed in Kural long-term video-EEG monitoring dataset.

RESULTS

The score for the first focal sharp discharge had a moderate predictive performance for the clinical designation as the EEG being epileptiform (area under the receiver operating characteristics curve = 0.86). Best specificity was 91% and sensitivity 55%. The score also predicted a future epilepsy diagnosis (area under the receiver operating characteristics curve = 0.70). Best specificity was 86% and sensitivity 38%. Validation on the external dataset had an area under the receiver operating characteristics curve = 0.80. Clinical EEG identification of focal interictal epileptiform discharges had an area under the receiver operating characteristics curve = 0.73 for prediction of epilepsy. The score was based on amplitude, slope, difference from background, slow after-wave area, and age. Interrater reproducibility was high (ICC = 0.91).

CONCLUSIONS

The designation of the first focal sharp discharge as epileptiform depends on reproducible morphologic features. Characteristic features were amplitude, slope, slow after-wave area, and difference from background. The score was predictive of future epilepsy. Halford semiquantitative scale had similar diagnostic performance but lower reproducibility.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

Digital Semiology: A Prototype for Standardized, Computer-Based Semiologic Encoding of Seizures

Video-EEG monitoring (VEM) is imperative in seizure classification and presurgical assessment of epilepsy patients. Analysis of VEM is currently performed in most institutions using a freeform report, a time-consuming process resulting in a non-standardized report, limiting the use of this essential diagnostic tool. Herein we present a pilot feasibility study of our experience with “Digital Semiology” (DS), a novel seizure encoding software. It allows semiautomated annotation of the videos of suspected events from a predetermined, hierarchal set of options, with highly detailed semiologic descriptions, somatic localization, and timing. In addition, the software's semiologic extrapolation functions identify characteristics of focal seizures and PNES, sequences compatible with a Jacksonian march, and risk factors for SUDEP. Sixty episodes from a mixed adult and pediatric cohort from one level 4 epilepsy center VEM archives were analyzed using DS and the reports were compared with the standard freeform ones, written by the same epileptologists. The behavioral characteristics appearing in the DS and freeform reports overlapped by 78–80%. Encoding of one episode using DS required an average of 18 min 13 s (standard deviation: 14 min and 16 s). The focality function identified 19 out of 43 focal episodes, with a sensitivity of 45.45% (CI 30.39–61.15%) and specificity of 87.50% (CI 61.65–98.45%). The PNES function identified 6 of 12 PNES episodes, with a sensitivity of 50% (95% CI 21.09–78.91%) and specificity of 97.2 (95% CI 88.93–99.95%). Eleven events of GTCS triggered the SUDEP risk alert. Overall, these results show that video recordings of suspected seizures can be encoded using the DS software in a precise manner, offering the added benefit of semiologic alerts. The present study represents an important step toward the formation of an annotated video archive, to be used for machine learning purposes. This will further the goal of automated VEM analysis, ultimately contributing to wider utilization of VEM and therefore to the reduction of the treatment gap in epilepsy.

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.

When should we obtain a routine EEG while managing people with epilepsy?

More than eight decades after its discovery, routine electroencephalogram (EEG) remains a safe, noninvasive, inexpensive, bedside test of neurological function. Knowing when a routine EEG should be obtained while managing people with epilepsy is a critical aspect of optimal care. Despite advances in neuroimaging techniques that aid diagnosis of structural lesions in the central nervous system, EEG continues to provide critical diagnostic evidence with implications on treatment. A routine EEG performed after a first unprovoked seizure can support a clinical diagnosis of epilepsy and differentiate those without epilepsy, classify an epilepsy syndrome to impart prognosis, and characterize seizures for antiseizure management. Despite a current viral pandemic, EEG services continue, and the value of routine EEG is unchanged.

Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms

Importance

The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias.

Objective

To assess the reliability of experts in detecting IEDs in routine EEGs.

Design, Setting, and Participants

This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016.

Main Outcomes and Measures

Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts.

Results

Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%).

Conclusions and Relevance

This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Eignung eines mobilen Trockenelektroden-EEG-Gerätes im Rahmen der Epilepsiediagnostik

EEG-Aufzeichnungen bei Verdacht auf Epilepsie erfolgen routinemäßig mit einer durchschnittlichen Ableitezeit von 20–30 Min. mittels stationärer Geräte. Längere und häufigere Ableitungen, auch in der Häuslichkeit der Patienten, erhöhen die Wahrscheinlichkeit, Ereignisse zu erfassen. Die technische Qualität und medizinische Auswertbarkeit der EEG-Aufzeichnungen sind Grundvoraussetzungen für ein Home-Monitoring. Die HOMEEPI Studie prüft die technische Verwertbarkeit und Wirksamkeit eines mobilen EEG-Gerätes mit Trockenelektroden (Fourier ONE) im Vergleich zu einem konventionellen EEG-Gerät bei 49 Patienten mit Verdacht auf Epilepsie. Die Studienergebnisse basieren auf Intra- und Interratervergleichen und belegen eine vergleichbare Qualität der EEG-Aufzeichnungen und eine hohe Übereinstimmungsrate der medizinischen Befunde.

Automated diagnosis of encephalitis in pediatric patients using EEG rhythms and slow biphasic complexes

Slow biphasic complexes (SBC) have been identified in the EEG of patients suffering for inflammatory brain diseases. Their amplitude, location and frequency of appearance were found to correlate with the severity of encephalitis. Other characteristics of SBCs and of EEG traces of patients could reflect the grade of pathology. Here, EEG rhythms are investigated together with SBCs for a better characterization of encephalitis. EEGs have been acquired from pediatric patients: ten controls and ten encephalitic patients. They were split by neurologists into five classes of different severity of the pathology. The relative power of EEG rhythms was found to change significantly in EEGs labeled with different severity scores. Moreover, a significant variation was found in the last seconds before the appearance of an SBC. This information and quantitative indexes characterizing the SBCs were used to build a binary classification decision tree able to identify the classes of severity. True classification rate of the best model was 76.1% (73.5% with leave-one-out test). Moreover, the classification errors were among classes with similar severity scores (precision higher than 80% was achieved considering three instead of five classes). Our classification method may be a promising supporting tool for clinicians to diagnose, assess and make the follow-up of patients with encephalitis.

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.

Interictal epileptiform discharges vary across age groups

OBJECTIVE

To investigate whether the occurrence and morphology of interictal epileptiform discharges (IEDs) in scalp-EEG change by age.

METHODS

10,547 patients who had a standard or sleep deprived EEG recording reported using the SCORE standard were included. 875 patients had at least one EEG with focal IEDs. Focal IED morphology was analyzed by age using quantitative measures in EEGLAB and by visual classification based on the SCORE standard. We present distributions of IED measures by age group, with medians, interquartiles, 5th and 95th percentiles.

RESULTS

Focal IEDs occurred most frequently in children and elderly. IED morphology and localization depended on age (p < 0.001). IEDs had higher amplitudes, sharper peaks, larger slopes, shorter durations, larger slow-wave areas and wider distributions in children. These morphological characteristics diminished and the IEDs became more lateralized with increasing age. Spike asymmetry was stable across all age groups.

CONCLUSIONS

IEDs have age-dependent characteristics. A spike detector, human or computer, should not operate with the same set of thresholds for patients at various age. With increasing age, focal IEDs are less sharp, have lower amplitudes, have less prominent slow-waves and they become more lateralized. Our findings can help EEG readers in detecting and correctly describing IEDs in patients of various age.

SIGNIFICANCE

EEG readers should always consider patient age when interpreting interictal epileptiform discharges.

Assessment of the technical usability and efficacy of a new portable dry-electrode EEG recorder: First results of the HOMEONE study

OBJECTIVES

The HOME project is intended to provide evidence of diagnostic and therapeutic yield of a patient-controlled EEG home-monitoring for neurological outpatients.

METHODS

This study evaluated the technical and practical usability and efficacy of a new portable dry-electrode EEG recorder in comparison to conventional EEG devices based on technical assessments and inter-rater comparisons of EEG record examinations of office-based practitioners and two experienced neurologists.

RESULTS

The technical assessment was based on channel-wise comparisons of band power values derived from power spectra as observed in two recording modalities. Slight yet significant differences were observed only in the Delta-frequency band (1.5-4 Hz). The fraction of automatically detected artifact segments was larger in the new portable recordings than in conventional recordings (20% vs. 11%, median). Overall, 93% of raters' stated diagnostic findings gathered from conventional devices were concordant with stated diagnostic findings gathered from the new portable device.

CONCLUSION

The new EEG device was shown to have technical comparability to and a high concordance rate of diagnostic findings with conventional EEG devices.

SIGNIFICANCE

The new portable dry-electrode EEG device is suitable to meet the HOME projects' goal of establishing a patient-controlled EEG home-monitoring in the routine care of neurological outpatients.

TRIAL REGISTRATION

DRKS DRKS00012685. Registered 09 August 2017, retrospectively registered.

Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

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.

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.

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.

Diagnostic and therapeutic yield of a patient-controlled portable EEG device with dry electrodes for home-monitoring neurological outpatients-rationale and protocol of the HOMEONE pilot study

Background

The HOME^ONE study is part of the larger HOME project, which aims to provide evidence of diagnostic and therapeutic yield (“change of management”) of a patient-controlled portable EEG device with dry electrodes for the purposes of EEG home-monitoring neurological outpatients.

Methods

The HOME^ONE study is the first step in the process of investigating whether outpatient EEG home-monitoring changes the diagnosis and treatment of patients in comparison to conventional EEG (“change of management”). Both EEG devices (conventional and portable) will be systematically compared via a two-phase intra-individual assessment.

In the first phase (pilot study phase), both EEG devices will be used within neurologist practices (all other things being equal). This pilot study (involving 130 patients) will evaluate the technical usability and efficacy of the new portable dry electrode EEG recorder in comparison to conventional EEG devices. Judgements will be based on technical assessments and EEG record examinations of private practitioners and two experienced neurologists (percent of concordant readings and kappa values).

The second phase (feasibility study phase) aims to assess patients’ acceptability and feasibility of the EEG home-monitoring and will provide insights into the extent diagnostic and therapeutic yields can be expected.

For this purpose, a conventional EEG will be recorded in neurologist practices. Thereafter, the practice staff will instruct the patients on how the portable EEG device functions. The patients will subsequently use the devices in their home environment.

The evaluation will compare the before and after documented diagnostic findings and the therapeutic consequences of the private practitioners with those of two experienced neurologists.

Discussion

To the best of our knowledge, this will be the first study of its kind to examine new approaches to diagnosing unclear consciousness disorders or other disorders of the CNS or the cardiovascular system through the use of a patient-controlled portable EEG device with dry electrodes for the purpose of home-monitoring neurological outpatients. If the two phases of the HOME^ONE study provide sufficient evidence of diagnostic and therapeutic yields, this would justify (indication-specific) full-scale randomized controlled trials or observational studies.

Trial registration

DRKS DRKS00012685. Registered 9 August 2017, retrospectively registered.

Ictal EEG source imaging in presurgical evaluation: High agreement between analysis methods

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There was good agreement between different methods of ictal EEG source imaging.

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Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%).

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Agreement between all methods did not necessarily imply accuracy of localization.

Purpose

To determine the agreement between five different methods of ictal EEG source imaging, and to assess their accuracy in presurgical evaluation of patients with focal epilepsy. It was hypothesized that high agreement between methods was associated with higher localization-accuracy.

Methods

EEGs were recorded with a 64-electrode array. Thirty-eight seizures from 22 patients were analyzed using five different methods phase mapping, dipole fitting, CLARA, cortical-CLARA and minimum norm. Localization accuracy was determined at sub-lobar level. Reference standard was the final decision of the multidisciplinary epilepsy surgery team, and, for the operated patients, outcome one year after surgery.

Results

Agreement between all methods was obtained in 13 patients (59%) and between all but one methods in additional six patients (27%). There was a trend for minimum norm being less accurate than phase mapping, but none of the comparisons reached significance. Source imaging in cases with agreement between all methods was not more accurate than in the other cases. Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%).

Conclusion

There was good agreement between different methods of ictal source imaging. However, good inter-method agreement did not necessarily imply accurate source localization, since all methods faced the limitations of the inverse solution.

Etiological associations and outcome predictors of acute electroencephalography in childhood encephalitis

OBJECTIVES

To examine EEG features in a retrospective 13-year cohort of children with encephalitis.

METHODS

354 EEGs from 119 patients during their admission were rated blind using a proforma with demonstrated inter-rater reliability (mean k=0.78). Patients belonged to 12 etiological groups that could be grouped into infectious and infection-associated (n=47), immune-mediated (n=36) and unknown (n=33). EEG features were analyzed between groups and for risk of abnormal Liverpool Outcome Score and drug resistant epilepsy (DRE) at last follow up.

RESULTS

86% children had an abnormal first EEG and 89% had at least one abnormal EEG. 55% had an abnormal outcome, and 13% had DRE after median follow-up of 7.3years (2.0-15.8years). Reactive background on first EEGs (9/11, p=0.04) and extreme spindles (4/11, p<0.001) distinguished patients with anti-N-Methyl-d-Aspartate Receptor encephalitis. Non-reactive EEG background (48% first EEGs) predicted abnormal outcome (OR 3.8, p<0.001). A shifting focal seizure pattern, seen in FIRES (4/5), anti-voltage gated potassium channel (2/3), Mycoplasma (1/10), other viral (1/10) and other unknown (1/28) encephalitis, was most predictive of DRE after multivariable analysis (OR 11.9, p<0.001).

CONCLUSIONS

Non-reactive EEG background and the presence of shifting focal seizures resembling migrating partial seizures of infancy are predictors of abnormal outcome and DRE respectively in childhood encephalitis.

SIGNIFICANCE

EEG is a sensitive but non-discriminatory marker of childhood encephalitis. We highlight the EEG features that predict abnormal outcome and DRE.

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.

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.

Standardized computer-based organized reporting of EEG: SCORE

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, "episodes" (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists.

Interrater reliability of ICU EEG research terminology

BACKGROUND

Standardized research terminology critical to the establishment of a multicenter intensive care unit (ICU) electroencephalogram (EEG) database was originally proposed in 2005 and has been modified many times since. However, interrater agreement (IRA) of the revised terminology has not been investigated.

METHODS

After a brief tutorial, investigators of ICU EEG research centers (n = 16) took an 82-question EEG certification test comprising 10-second EEG samples, which assessed the use of main term 1 (pattern location), main term 2 (pattern type), and modifiers from the most recently revised terminology.

RESULTS

Kappa values for main terms 1 and 2 were 0.87 and 0.92, respectively. Agreement was 93% for determination of amplitude and 80% for determination of frequency. Kappa values for each of the "plus" modifiers (fast, rhythmic, and sharp/spike activity) were 0.54, 0.62, and 0.16 respectively.

CONCLUSIONS

Main terms 1 and 2 have high IRA and are reasonable for use in multicenter research. There is a suggestion that assessment of amplitude has good reliability, while assessment of frequency may have less reliability. The fast and rhythmic "plus" modifiers have moderate IRA, while sharp/spike modifier has only slight IRA implying that further refinement and assessment of terminology modifiers may be necessary.

Morphology-based automatic seizure detector for intracerebral EEG recordings

In this paper, a new seizure detection system aimed at assisting in a rapid review of prolonged intracerebral EEG recordings is described. It is based on quantifying the sharpness of the waveform, one of the most important electrographic EEG features utilized by experts for an accurate and reliable identification of a seizure. The waveform morphology is characterized by a measure of sharpness as defined by the slope of the half-waves. A train of abnormally sharp waves resulting from subsequent filtering are used to identify seizures. The method was optimized using 145 h of single-channel depth EEG from seven patients, and tested on another 158 h of single-channel depth EEG from another seven patients. Additionally, 725 h of depth EEG from 21 patients was utilized to assess the system performance in a multichannel configuration. Single-channel test data resulted in a sensitivity of 87% and a specificity of 71%. The multichannel test data reported a sensitivity of 81% and a specificity of 58.9%. The new system detected a wide range of seizure patterns that included rhythmic and nonrhythmic seizures of varying length, including those missed by the experts. We also compare the proposed system with a popular commercial system.

Interobserver reproducibility of electroencephalogram interpretation in critically ill children

Correct outcome prediction after cardiac arrest in children may improve clinical decision making and family counseling. Investigators have used EEG to predict outcome with varying success, but a limiting issue is the potential lack of reproducibility of EEG interpretation. Therefore, the authors aimed to evaluate interobserver agreement using standardized terminology in the interpretation of EEG tracings obtained from critically ill children after cardiac arrest. Three pediatric neurophysiologists scored 74 EEG samples using standardized categories, terminology, and interpretation rules. Interobserver agreement was evaluated using kappa and intraclass correlation coefficients. Agreement was substantial for the categories of continuity, burst suppression, sleep architecture, and overall rating. Agreement was moderate for seizure occurrence and interictal epileptiform discharge type. Agreement was fair for interictal epileptiform discharge presence, beta activity, predominant frequency, and fastest frequency. Agreement was slight for maximum voltage and focal slowing presence. The variability of interrater agreement suggests that some EEG features are superior to others for use in a predictive algorithm. Using only reproducible EEG features is needed to ensure the most accurate and consistent predictions. Because even seizure identification had only moderate agreement, studies of nonconvulsive seizures in critically ill patients must be conducted and interpreted cautiously.

Web-based collection of expert opinion on routine scalp EEG: software development and interrater reliability

Computerized detection of epileptiform transients (ETs), characterized by interictal spikes and sharp waves in the EEG, has been a research goal for the last 40 years. A reliable method for detecting ETs would assist physicians in interpretation and improve efficiency in reviewing long-term EEG recordings. Computer algorithms developed thus far for detecting ETs are not as reliable as human experts, primarily due to the large number of false-positive detections. Comparing the performance of different algorithms is difficult because each study uses individual EEG test datasets. In this article, we present EEGnet, a distributed web-based platform for the acquisition and analysis of large-scale training datasets for comparison of different EEG ET detection algorithms. This software allows EEG scorers to log in through the web, mark EEG segments of interest, and categorize segments of interest using a conventional clinical EEG user interface. This software platform was used by seven board-certified academic epileptologists to score 40 short 30-second EEG segments from 40 patients, half containing ETs and half containing artifacts and normal variants. The software performance was adequate. Interrater reliability for marking the location of paroxysmal activity was low. Interrater reliability of marking artifacts and ETs was high and moderate, respectively.

Long-term outcome of childhood absence epilepsy: Dutch Study of Epilepsy in Childhood

SUMMARY

We determined long-term outcome and the predictive value of baseline and EEG characteristics on seizure activity evolution in 47 children with newly diagnosed childhood absence epilepsy (CAE) included in the Dutch Study of Epilepsy in Childhood. All children were followed for 12-17 years. The children were subdivided in three groups for the analyses: those becoming seizure-free (I) within 1 month after enrolment; (II) 1-6 months after enrolment; and (III) more than 6 months after enrolment or having seizures continuing during follow-up. No significant differences were observed between groups in sex, age at onset, occurrence of febrile seizures, and positive first-degree family history for epilepsy. All groups had high remission rates after 12-17 years. Significantly more relapses occurred in group III than in group I. Total duration of epilepsy and mean age at final remission were 3.9 and 9.5 years, respectively, being significantly longer and higher in group III than in groups I and II. In all groups only a small number of children (total 13%) developed generalized tonic-clonic seizures. In conclusion, our children with CAE had an overall good prognosis with few children (7%) still having seizures after 12-17 years. Remission rate in children with CAE cannot be predicted on the basis of baseline and EEG characteristics. The early clinical course (i.e. the first 6 months) has some predictive value with respect to the total duration of absence epilepsy.

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

The significance of rhythmic and periodic EEG patterns in critically ill patients is unclear. A universal terminology is needed to facilitate study of these patterns, and consistent observer agreement should be demonstrated in its use. The authors evaluated inter- and intraobserver agreement using the standardized terminology (Hirsch et al., J Clin Neurophysiol 2005;22:128-135) recently proposed by the American Clinical Neurophysiology Society. Trained electroencephalographers viewed a series of 10-second EEG samples from critically ill adults (phase I), a set of >/=20-minute EEGs from the same patient cohort (phase II), and then reevaluated the first sample set (phase III). The readers used the proposed terminology to "score" each EEG. For each possible term, interobserver agreement (phases I and II) and intraobserver agreement (phase III) were calculated using pairwise kappa (kappa) values. Moderate agreement beyond chance was seen for the presence/absence of rhythmic or periodic patterns and for localization of these patterns. Agreement for other terms was slight to fair. Inter- and intraobserver agreement were consistently lower for optional terms than mandatory terms. Even when standardized terminology is used, the description of rhythmic and periodic EEG patterns varies significantly. Further refinement of the proposed terminology is required to improve inter- and intraobserver agreement.

Inter-observer variability of the EEG diagnosis of seizures in comatose patients

OBJECTIVE

To assess the inter-observer agreement of the electroencephalogram (EEG) diagnosis of (non-convulsive) seizures in comatose patients.

DESIGN/SETTING/PATIENTS

Nine clinicians with different levels of experience in clinical neurophysiology were asked to evaluate in a strictly controlled way 90 epochs (10s each) of 30 EEG's of 23 comatose patients admitted to the intensive care unit (ICU). For each EEG clinicians had to decide whether there was an electrographic seizure or not. Furthermore, Young's EEG criteria for (non-convulsive) seizures were scored in detail for all EEG's. Agreement was determined by calculating kappa values.

RESULTS

The inter-observer agreement of an EEG diagnosis of seizure was limited. The overall kappa score for the five experienced raters was 0.5, and the kappa score for less experienced raters was 0.29. Kappa values for the individual Young's criteria were highly variable, indicating discrepancies in the interpretation of specific phenomena. Especially, some types of periodic discharges gave rise to different interpretations.

CONCLUSIONS

The EEG diagnosis of (non-convulsive) seizures in ICU patients is not very reliable, even when strict criteria such as proposed by Young are applied. There is a need for less ambiguous EEG criteria for (non-convulsive) seizures and status epilepticus.

How confident are we of the diagnosis of epilepsy?

The diagnosis of a first seizure or epilepsy may be subject to interobserver variation and inaccuracy with possibly far-reaching consequences for the patients involved. We reviewed the current literature. Studies on the interobserver variation of the diagnosis of a first seizure show that such a diagnosis is subject to considerable interobserver disagreement. Interpretation of the electroencephalogram (EEG) findings is also subject to interobserver disagreement and is influenced by the threshold of the reader to classify EEG findings as epileptiform. The accuracy of the diagnosis of epilepsy varies from a misdiagnosis rate of 5% in a prospective childhood epilepsy study in which the diagnosis was made by a panel of three experienced pediatric neurologists to at least 23% in a British population-based study, and may be even higher in everyday practice. The level of experience of the treating physician plays an important role. The EEG may be helpful but one should be reluctant to make a diagnosis of epilepsy mainly on the EEG findings without a reasonable clinical suspicion based on the history. Being aware of the possible interobserver variation and inaccuracy, adopting a systematic approach to the diagnostic process, and timely referral to specialized care may be helpful to prevent the misdiagnosis of epilepsy.

Clinical diagnosis of syncopes (including so-called breath-holding spells) without electroencephalography or ocular compression

A recent article in this journal suggested that ocular compression during electroencephalography was useful in distinguishing "breath-holding spells and syncope" from epileptic seizures. The method proposed involved measurement of the RR interval on the simultaneously recorded electrocardiographic trace and determining both the absolute RR lengthening and the change in RR interval as compared with the baseline value. It is argued by the present author that this is not an appropriate way to come to a diagnosis in episodic loss of consciousness in children. It is pointed out that so-called "breath-holding spells" are reflex syncopes and that the diagnosis of reflex syncopes should be by clinical history, even if this means delaying the diagnosis until a future consultation. Published evidence on the nature and clinical diagnosis of reflex syncopes in infants and children is reviewed in depth. It is concluded that routine electroencephalography is not an appropriate investigation when the diagnosis of episodic loss of consciousness is in doubt and has the implicit danger of false positive "abnormality". Aside from scientific exploration of the developing autonomic nervous system, the only current indication for diagnostic ocular compression is to induce a syncope so that its nature may be better understood. Such a circumstance might be a history of an apparent reflex syncope but with atypical features, including prolonged post-syncopal unconsciousness such as might indicate epileptic absence status. Several additional investigations of a primarily cardiological nature may be indicated in some cases, but a wait-and-see policy is usually to be preferred.

The office management of epilepsy

Epilepsy in children is mostly diagnosed and treated in an ambulatory office setting. This article reviews the literature and offers opinions about the best practice from the time of diagnosis through to remission and beyond. The diagnosis and assignment of an epilepsy syndrome may be difficult, and even experts disagree in many cases. Regular review of the basic diagnosis and semiology of seizures is suggested throughout treatment. Workup should always include an electroencephalogram and usually magnetic resonance imaging. Antiepileptic drugs (AEDs) suppress seizures but appear to have little effect on long-term remission, and the choice of AED is for the most part arbitrary with most AEDs having a similar success rate when used as the first drug. Families have a great need for accurate information, and their ability to cope with the unpredictable nature of seizures may be assisted by "rescue" home benzodiazepines. Surveillance for drug toxicity and side effects is a critical clinical skill that is not assisted by routine blood tests or AED serum levels. Most children with epilepsy do not have many seizures and need not have significant restrictions on their activities. In the long run, comorbidities (especially learning and behavior problems) have a greater impact on social function than the epilepsy. Management of these problems may extend well beyond remission of the epilepsy. The child neurologist needs to prepare children with persistent epilepsy for transfer to adult epilepsy services.