Interictal scalp fast oscillations as a marker of the seizure onset zone
Bibliographic record
Abstract
OBJECTIVE: This study aims to identify if oscillations at frequencies higher than the traditional EEG can be recorded on the scalp EEG of patients with focal epilepsy and to analyze the association of these oscillations with interictal discharges and the seizure onset zone (SOZ). METHODS: The scalp EEG of 15 patients with focal epilepsy was studied. We analyzed the rates of gamma (40-80 Hz) and ripple (>80 Hz) oscillations, their co-occurrence with spikes, the number of channels with fast oscillations inside and outside the SOZ, and the specificity, sensitivity, and accuracy of gamma, ripples, and spikes to determine the SOZ. RESULTS: Gamma and ripples frequently co-occurred with spikes (77.5% and 63% of cases). For all events, the proportion of channels with events was consistently higher inside than outside the SOZ: spikes (100% vs 70%), gamma (82% vs 33%), and ripples (48% vs 11%); p < 0.0001. The mean rates (events/min) were higher inside than outside the SOZ: spikes (2.64 ± 1.70 vs 0.69 ± 0.26, p = 0.02), gamma (0.77 ± 0.71 vs 0.20 ± 0.25, p = 0.02), and ripples (0.08 ± 0.12 vs 0.04 ± 0.09, p = 0.04). The sensitivity to identify the SOZ was spikes 100%, gamma 82%, and ripples 48%; the specificity was spikes 30%, gamma 68%, and ripples 89%; and the accuracy was spikes 43%, gamma 70%, and ripples 81%. CONCLUSION: The rates and the proportion of channels with gamma and ripple fast oscillations are higher inside the SOZ, indicating that they can be used as interictal scalp EEG markers for the SOZ. These fast oscillations are less sensitive but much more specific and accurate than spikes to delineate the SOZ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".