How much does the reduced electroencephalographic montage matter for seizure detection? A large-cohort simulation study.

objectiveSubscalp electroencephalographic (EEG) systems with few channels have emerged as promising solutions for ultra-long-term seizure monitoring, but the impact of montage configuration on automated seizure detection is unclear. We compared automated detection performance between full-scalp and simulated reduced montages approximating published subscalp devices, and assessed variation by epilepsy type, lateralization, and localization.

methodsWe conducted a retrospective cross-sectional study of consecutive epilepsy monitoring unit (EMU) admissions from January 2017 to December 2024 at the Hospital of the University of Pennsylvania. Admissions with at least one clinician-annotated seizure and at least one interictal segment of ≥20 min from any seizure were included. We simulated reduced bipolar montages from standard 10-20 scalp EEG. Three validated detectors, a one-class support vector machine (SVM), a convolutional neural network (SPaRCNet), and a long short-term memory autoregressive model with neural dynamic divergence method (NDD), were applied to montages and evaluated using event-level F1 scores. We additionally evaluated the contributions of patient, detector, and montage to performance variance and associations between performance and epilepsy characteristics using linear mixed-effects models.

resultsA total of 466 admissions from 436 patients (mean [SD] age = 39.0 [14.4] years; 54.4% female) met inclusion criteria, comprising 1683 seizures and 1527 interictal clips. SPaRCNet achieved the highest performance (mean [SD] F1 = .61 [.30]), followed by NDD (.56 [.28]) and SVM (.39 [.25]). Absolute decreases in F1 score with reduced montages were modest (≤.09). Patient admission accounted for the most of performance variance (29.2%), followed by detector (10.3%), whereas montage contributed minimally (.4%). Performance between full and reduced montages was correlated (ρ = .29-.73). SIGNIFICANCE: Automated seizure detection performance was primarily driven by patient and algorithm factors rather than montage. Findings support the feasibility of seizure monitoring with reduced montages approximating chronic subscalp device geometries, despite the need for improved detection algorithms, and suggest that EMU-based full-montage performance could help identify candidates for these devices.