How Much Does the Reduced EEG Montage Matter for Seizure Detection?: A Large-Cohort Simulation Study

ImportanceImplantable sub-scalp EEG systems with a small number of channels have emerged as promising solutions for long-term seizure monitoring in patients with epilepsy. How seizure detection performance varies by montage configuration is unknown. ObjectiveTo quantify how automated seizure detection performance differs between full and reduced montages, and how these differences vary by epilepsy characteristics. DesignRetrospective cross-sectional study. SettingSingle-center at the Hospital of the University of Pennsylvania Epilepsy Monitoring Unit (EMU). ParticipantsEEG data from 2281 consecutive EMU admissions between January 2017 and December 2024 were screened. Admissions with at least one annotated seizure and one interictal clip [≥]20 minutes from any seizure were included. ExposureComputational simulation of published sub-scalp device montages using standard 10-20 EEG channels. Main Outcomes and MeasuresThe primary outcome was event-based F1 scores evaluated for three published seizure detectors--a one-class support vector machine (SVM), a convolutional neural network (SPaRCNet), and a long short-term memory autoregressive model (NDD)--across montages. 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, 0.61 [0.30]), followed by NDD (0.56 [0.28]) and SVM (0.39 [0.25]). Performance decreased by at most 0.09 with reduced montages, depending on detectors. Patient factors accounted for the largest proportion of performance variance (29.2%), followed by detector choice (10.3%). Montage effects were minimal (0.4%), despite variation in optimal montage across detectors. Reduced-montage performance correlated moderately to highly with full-montage performance ({rho}=0.29-0.73), suggesting full-montage performance could help identify patients suitable for sub-scalp devices. Missed seizures were associated with lower amplitude and bandpowers than detected seizures, though they remained distinguishable from interictal data. Conclusions and RelevanceAutomated seizure detection achieved comparable accuracy, with only modest reductions, under simulated reduced montages. Performance differences were driven primarily by detector- and patient-level factors rather than montage. These findings support the feasibility of accurately detecting seizures with published sub-scalp devices and highlight the need for improved algorithms to optimize performance. Key FindingsO_ST_ABSQuestionC_ST_ABSHow do automated seizure detection algorithms perform with reduced-channel montages simulating published sub-scalp devices? FindingsIn this retrospective cross-sectional study, seizure detection performance decreased only modestly on reduced montages relative to the full montage (absolute F1 change -0.09 to 0.014), whereas patient- and algorithm-level factors accounted for most of performance variance (29.2% and 10.3%, respectively). Algorithm performance on full montage recordings was moderately correlated with performance on reduced channel montages ({rho}=0.29-0.73). MeaningReduced-montage sub-scalp devices are promising for ultra-long-term monitoring, but best performance requires selecting the right patients. Patient-specific seizure detectors will likely be required to optimize long-term performance.