Introduction: Scalp electroencephalography (EEG) is a cornerstone in the diagnosis and treatment of epilepsy, but routine EEG is often interpreted as normal without identification of epileptiform activity during expert visual review. The absence of interictal epileptiform activity on routine scalp EEGs can cause delays in receiving clinical treatment. These delays can be particularly problematic in the diagnosis and treatment of people with drug-resistant epilepsy (DRE) and those without structural abnormalities on MRI (i.e., MRI negative). Thus, there is a clinical need for alternative quantitative approaches that can inform diagnostic and treatment decisions when visual EEG review is inconclusive. In this study, we leverage a large population-level routine EEG database of people with and without focal epilepsy to investigate whether normal interictal EEG segments contain subtle deviations that could support the diagnosis of focal epilepsy. Data & Methods: We identified multiple epochs representing eyes-closed wakefulness from 19-channel routine EEGs of a large and diverse neurological patient population (N=13,652 recordings, 12,134 unique patients). We then extracted the average spectral power and phase-lag-index-based connectivity within 1-45Hz of each EEG recording using these identified epochs. We decomposed the power spectral density and phase-based connectivity information of all the visually reviewed normal EEGs (N=6,242) using unsupervised tensor decompositions to extract dominant patterns of spectral power and scalp connectivity. We also identified an independent set of routine EEGs of a cohort of focal epilepsy patients (N= 121) with various diagnostic classifications, including focal epilepsy origin (temporal, frontal), MRI (lesional, non-lesional), and response to anti-seizure medications (responsive vs. drug-resistant epilepsy). We analyzed visually normal interictal epochs from the EEGs using the power-spectral and phase-based connectivity patterns identified above and evaluated their potential in clinically relevant binary classifications. Results: We obtained six patterns with distinct interpretable spatio-spectral signatures corresponding to putative aperiodic, oscillatory, and artifactual activity recorded on the EEG. The loadings for these patterns showed associations with patient age and expert-assigned grades of EEG abnormality. Further analysis using a physiologically relevant subset of these loadings differentiated focal epilepsy patients from controls without history of focal epilepsy (mean AUC 0.78) but were unable to differentiate between frontal or temporal lobe epilepsy. In temporal lobe epilepsy, loadings of the power spectral patterns best differentiated drug-resistant epilepsy from drug-responsive epilepsy (mean AUC 0.73), as well as lesional epilepsy from non-lesional epilepsy (mean AUC 0.67), albeit with high variability across patients. Significance: Our findings from a large population sample of EEGs suggest that normal interictal EEGs of epilepsy patients contain subtle differences of predictive value that may improve the overall diagnostic yield of routine and prolonged EEGs. The presented approach for analyzing normal EEGs has the capacity to differentiate several diagnostic classifications of epilepsy, and can quantitatively characterize EEG activity in a scalable, expert-interpretable, and patient-specific fashion. Further technical development and clinical validation may yield normal EEG-derived computational biomarkers that could augment epilepsy diagnosis and assist clinical decision-making in the future.

The authors have declared no competing interest.

This study was supported in part by a Mayo Clinic Illinois Alliance Fellowship for Technology-based Healthcare Research, NSF grants IIS-2105233, IIS-2344731, and IIS-2337909, and NIH grants R01-NS092882 and UG3 NS123066.

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