Dynamic Changes of Ictal High‐Frequency Oscillations in Neocortical Epilepsy: Using Multiple Band Frequency Analysis
Bibliographic record
Abstract
PURPOSE: To characterize the spatial and temporal course of ictal high-frequency oscillations (HFOs) recorded by subdural EEG in children with intractable neocortical epilepsy. METHODS: We retrospectively studied nine children (four girls, five boys; 4-17 yr) who presented with intractable extrahippocampal localization-related epilepsy and who underwent extraoperative video subdural EEG (1000 Hz sampling rate) and cortical resection. We performed multiple band frequency analysis (MBFA) to evaluate the frequency, time course, and distribution of ictal HFOs. We compared ictal HFO changes before and after clinical onset and postsurgical seizure outcomes. RESULTS: Seventy-eight of 79 seizures showed HFOs. We observed wide-band HFOs ( approximately 250 Hz, approximately 120 electrodes) in six patients either with partial seizures alone (three patients) or with epileptic spasms (three patients). Three patients with partial seizures that secondarily generalized had wide-band HFOs ( approximately 170 Hz) before clinical onset and sustained narrow-band HFOs (60-164 Hz) with electrodecremental events after clinical onset ( approximately 28 electrodes). In four postoperatively seizure-free patients, more electrodes recorded higher-frequency HFOs inside the resection area than outside before and after clinical seizure onset. In five patients with residual seizures, electrodes recorded more HFOs that were of higher or equal frequency outside the surgical area than inside after clinical onset. CONCLUSION: For partial seizures alone and epileptic spasms, more electrodes recorded only wide-band HFOs; for partial seizures that secondarily generalized, fewer electrodes recorded wide-band HFOs, but in these seizures electrodes also recorded subsequent sustained narrow-band ictal HFOs. Resection of those brain regions having electrodes with ictal, higher HFOs resulted in postsurgical seizure-free outcomes.
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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 | 0.001 |
| 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".