discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.OBJECTIVE To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.DESIGN, SETTING, AND PARTICIPANTS A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.MAIN OUTCOMES AND MEASURES SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.RESULTS SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). CONCLUSIONS AND RELEVANCEIn this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Caregiver QOL in PNES does not differ from caregiver QOL in ES, while patient QOL is worse in PNES. Caregiver burden emerges as a consistent correlate of caregiver QOL both in ES and PNES. These findings advocate for consideration of caregiver burden and QOL in PNES in clinical practice and for future research paradigms.

Background Identifying nutrition- and lifestyle-based risk factors for cognitive impairment and dementia may aid future primary prevention efforts. Objective We aimed to examine the association of serum vitamin D levels with incident all-cause dementia, clinically characterized Alzheimer’s disease (AD), MRI markers of brain aging, and neuropsychological function. Methods Framingham Heart Study participants had baseline serum 25-hydroxyvitamin D (25(OH)D) concentrations measured between 1986 and 2001. Vitamin D status was considered both as a continuous variable and dichotomized as deficient (<10 ng/mL), or at the cohort-specific 20th and 80th percentiles. Vitamin D was related to the 9-year risk of incident dementia (n= 1663), multiple neuropsychological tests (n= 1291) and MRI markers of brain volume, white matter hyperintensities and silent cerebral infarcts (n = 1139). Results In adjusted models, participants with vitamin D deficiency (n = 104, 8% of the cognitive sample) displayed poorer performance on Trail Making B-A (β = −0.03 to −0.05 ±0.02) and the Hooper Visual Organization Test (β = −0.09 to −0.12 ±0.05), indicating poorer executive function, processing speed, and visuo-perceptual skills. These associations remained when vitamin D was examined as a continuous variable or dichotomized at the cohort specific 20th percentile. Vitamin D deficiency was also associated with lower hippocampal volumes (β = −0.01 ±0.01) but not total brain volume, white matter hyperintensities, or silent brain infarcts. No association was found between vitamin D deficiency and incident all-cause dementia or clinically characterized AD. Conclusions In this large community-based sample, low 25(OH)D concentrations were associated with smaller hippocampal volume and poorer neuropsychological function.

Aim. Caregiver burden (CB) in epilepsy constitutes an understudied area. Here we attempt to identify the magnitude of this burden, the factors associated with it, and its impact to caregiver quality of life (QOL). Methods. 48 persons with epilepsy (PWE) underwent video-EEG monitoring and their caregivers completed questionnaires providing demographic, disease-related, psychiatric, cognitive, sleep, QOL, and burden information. Results. On regression analysis, higher number of antiepileptic drugs, poorer patient neuropsychological performance, lower patient QOL score, and lower caregiver education level were associated with higher CB. Time allocated to patient care approximated but did not attain statistical significance. A moderate inverse correlation between CB and caregiver QOL physical component summary score and a stronger inverse correlation between CB and caregiver QOL mental component summary score were seen. Conclusion. In a selected cohort of PWE undergoing video-EEG monitoring, we identified modest degree of CB, comparable to that reported in the literature for other chronic neurological conditions. It is associated with specific patient and caregiver characteristics and has a negative effect on caregiver QOL.

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 PARTICIPANTSThis 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.

The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. Methods: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. Results: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio ], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). Interpretation: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes.

Obtaining an emergent EEG for the diagnosis of nonconvulsive status epilepticus and conconvulsive seizures in the intensive care unit raises logistic problems in most hospitals. Previous studies have looked into the hairline EEG for a broader population than the critically ill, with controversial conclusions. The authors created a montage sufficiently simple to be performed and interpreted by residents and rapidly achievable to meet the time constraints of a busy on-call schedule. Seven electrodes (Fp1, Fp2, T3, T4, O1, O2, and Cz), easily applied without the need for tape measure by using only anatomic landmarks (pupils, ears, vertex, and inion), were used to configure three different montages: double diamond, circumferential, and Cz referential. EEG records obtained with the full 10-20 system in critically ill patients were reformatted into these montages and reviewed retrospectively independently by neurology attending physicians with expertise in EEG interpretation and senior neurology residents. A comparison was done with the previously studied hairline EEG. The average sensitivity of the study montage for seizure detection was 92.5%, whereas the average specificity was 93.5%. These results suggest that the seven-electrode montage could potentially be a quick and reliable EEG montage for the detection of seizures in the intensive care unit, when technical support is not available. Further prospective studies are required to validate these promising results in a larger population sample.

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