Spike count and morphology in the classification of epileptiform discharges.

Purpose: The purpose of this study is to investigate the impact of Bergen Epileptiform Morphology Score (BEMS) and interictal epileptiform discharge (IED) candidate count in EEG classification. Methods: We included 400 consecutive patients from a clinical SCORE EEG database during 2013-2017 who had focal sharp discharges in their EEG, but no previous diagnosis of epilepsy. Three blinded EEG readers marked all IED candidates. BEMS and IED candidate counts were combined to classify EEGs as epileptiform or non-epileptiform. Diagnostic performance was assessed and then validated in an external dataset. Results: Interictal epileptiform discharge (IED) candidate count and BEMS were moderately correlated. The optimal criteria to classify an EEG as epileptiform were either one spike at BEMS > = 58, two at > = 47, or seven at > = 36. These criteria had almost perfect inter-rater reliability (Gwet's AC1 0.96), reasonable sensitivity of 56-64%, and high specificity of 98-99%. The sensitivity was 27-37%, and the specificity was 93-97% for a follow-up diagnosis of epilepsy. In the external dataset, the sensitivity for an epileptiform EEG was 60-70%, and the specificity was 90-93%. Conclusion: Quantified EEG spike morphology (BEMS) and IED candidate count can be combined to classify an EEG as epileptiform with high reliability but with lower sensitivity than regular visual EEG review.

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.

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.

Visual EEG reviewing times with SCORE EEG.

OBJECTIVE: Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but there is no published data on how long it takes to review and report an EEG in clinical routine. Estimates of reporting times may inform workforce planning and automation initiatives for EEG. The SCORE standard has recently been adopted to standardize clinical EEG reporting, but concern has been expressed about the time spent reporting. METHODS: Elapsed times were extracted from 5889 standard and sleep-deprived EEGs reported between 2015 and 2017 reported using the SCORE EEG software. RESULTS: The median review time for standard EEG was 12.5 min, and for sleep deprived EEG 20.9 min. A normal standard EEG had a median review time of 8.3 min. Abnormal EEGs took longer than normal EEGs to review, and had more variable review times. 99% of EEGs were reported within 24 h of end of recording. Review times declined by 25% during the study period. CONCLUSION: Standard and sleep-deprived EEG review and reporting times with SCORE EEG are reasonable, increasing with increasing EEG complexity and decreasing with experience. EEG reports can be provided within 24 h. SIGNIFICANCE: Clinical standard and sleep-deprived EEG reporting with SCORE EEG has acceptable reporting times.