OBJECTIVE
Subscalp 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.
METHODS
We 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.
RESULTS
A 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.
J. Kojima, Hao-Er Shi, Svanik Jaikumar et al.· Epilepsia· 0 citations
Objective. Temporal lobe epilepsy (TLE) is associated with disrupted functional integrity in the amygdala-hippocampus complex. Cortico-cortical evoked potentials (CCEPs) can characterize this disruption and have been proposed as biomarkers of the epileptogenic zone (EZ), but their study is typically limited by the spatial sampling bias inherent to whole-brain intracranial EEG. We investigated how epileptogenicity shapes effective connectivity in the amygdala-hippocampus complex, whether structural connectivity underlies it, and ultimately derived a multimodal EZ biomarker. Methods. We retrospectively included 71 patients (50 adults, 21 children) who underwent single-pulse electrical stimulation protocols with intracranial contacts in the amygdala or hippocampus; 15 also underwent diffusion MRI. CCEPs were visually detected, and the latency and amplitude of the first response peak (D1) were extracted. Structural connectivity metrics (tract length, quantitative and fractional anisotropy, mean diffusivity) were derived between the same contacts. A Bayesian linear mixed model (BLMM) related D1 latency to clinical, neurophysiological, and structural predictors, handling missing DTI values jointly within the model. A corrected latency score was then built to discriminate epileptogenic from non-epileptogenic contacts. Results. Among 6257 possible stimulation-recording pairs, 1027 CCEPs were detected with a significantly higher rate in the hippocampus compared to the amygdala. The BLMM identified robust associations between D1 latency and epileptogenicity, epilepsy type, ipsilateral stimulation, stimulation site (hippocampus/amygdala), and quantitative and fractional anisotropy. The resulting EZ score, obtained by extracting the EZ term's contribution from the BLMM equation, demonstrated an ability to discriminate epileptogenic contacts, showing a balanced accuracy of 78% (sensitivity 86%, specificity 71%), and the resulting EZ probability, based on an elastic net logistic regression, showed a balanced accuracy of 84% (sensitivity 86%, specificity 82%). Discussion. These findings suggest that effective connectivity results from the interplay of opposing physiological (here amygdala vs. hippocampus) and pathological (epilepsy-related) influences rather than a simple facilitation within the EZ, and that white matter microstructure independently contributes to this timing. Connectivity is slower within the EZ itself, with an even greater delay observed in its vicinity compared to other brain areas. The resulting EZ score offers a practical, closed-form tool to strengthen EZ localization, and paves the way toward a structurally informed, CCEP-based framework extendable to other brain regions.
O. Feys, M. Josyula, N. Sinha et al.· medRxiv· 0 citations
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