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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]

Background: Electroencephalography (EEG) is used for studying functional disorders of the brain. We provide an overview of the areas where EEG is most strongly indicated and the clinical benefit of the examination., Method: This article is based on articles from personal literature archives and literature searches in PubMed, textbook on methods in clinical neurophysiology, the work of one of the authors on a new European standard for EEG and the authors' clinical experience., Results: EEG is primarily indicated for assessing suspected epileptic seizures and encephalopathies. Standard EEG, sleep-deprived EEG and long-term EEG examinations are used in diagnosing epilepsy. EEG will identify epileptiform activity in about 90% of patients with epilepsy. Epileptiform EEG activity in these patients will provide support for the diagnosis of epilepsy, while the absence of such activity will not exclude the diagnosis. EEG is also important for classifying the type of epileptic syndrome. In cases of encephalopathy, pathology will take the form of an increase in slow activity on the EEG and will support the diagnosis, but as a rule the EEG finding is non-specific. Findings in some cases may point to the underlying aetiology., Interpretation: Electroencephalograms must be interpreted by an experienced clinical neurophysiologist or neurologist with specialist expertise in EEG interpretation, and must always be evaluated in the light of the clinical picture.