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Objective Methods Results Significance The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource‐limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE‐AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings.We assessed the diagnostic accuracy of a “fixed‐and‐frozen” AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard.We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16–91] years). SCORE‐AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%–94%) versus 94% (95% CI = 92%–96%). There was no significant difference between SCORE‐AI and the experts for any metric or category. SCORE‐AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p = .63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p = .07).SCORE‐AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (nD77) and VaD (nD77) and healthy elderly normal controls (NC) (nD77).We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria. [ABSTRACT FROM AUTHOR]

Objective: Recording seizures on video-EEG has a high diagnostic value. However, bilateral convulsive seizures constitute a risk for the patients. Our aim was to investigate the diagnostic yield and associated risks of provocation methods in short-term video-EEGs., Methods: We extracted data on seizures and provocation methods from a large database of short-term video-EEGs with standardized annotations using SCORE (Standardized Computer-based Organized reporting of EEG)., Results: 2742 paroxysmal clinical episodes were recorded in 11 919 consecutive EEGs. Most epileptic seizures (54%) were provoked. Hyperventilation provoked most of typical absence seizures (55%), intermittent photic stimulation (IPS) provoked myoclonic seizures (25%) and most of bilateral convulsive seizures (55%), while 43% of focal seizures were precipitated by sleep. All but one of the 16 bilateral convulsive seizures were provoked by IPS or sleep. Latency between start of generalized photoparoxysmal EEG response and bilateral convulsive seizures were ≤3 s in all but one patient., Significance: The large, structured database provides evidence for the diagnostic utility of various provocation methods in short-term video-EEGs. The risk of bilateral convulsive seizures is relatively small, but it cannot be prevented by stopping IPS after 3 s. A priori knowledge about seizure semiology helps planning patient-tailored provocation strategy in short-term video-EEGs., (© 2024 International League Against Epilepsy.)