Seizure Anticipation: Do Mathematical Measures Correlate with Video‐EEG Evaluation?
Bibliographic record
Abstract
PURPOSE: Analysis of the EEG signal has recently shown evidence of dynamic changes preceding ictal onset in seizures selected from patients with clear epileptogenic foci. Most of the studies were conducted in highly selected EEG epochs and patients. In addition, these studies relied mostly on mathematical approaches and neglected clinical and visual EEG parameters. We therefore performed a systematic comparison of a nonlinear method (the similarity measure) with classic visual inspection of the EEG and the patient's clinical state. METHODS: We analyzed the dynamics of long epochs of intracranial EEG containing 129 electroclinical and 45 electrographic seizures in 13 successive unselected patients undergoing presurgical evaluation. RESULTS: (a) The similarity measure detected preictal dynamical changes of the EEG signal in two thirds of the seizures whether or not a clear focus was identified, and whether seizures were electroclinical or purely electrographic. The mean duration of preictal changes was 12 min. (b) The preictal changes were correlated with various visually detectable EEG changes in 78.9% of electroclinical seizures. (c) 81.5% of the preictal dynamic changes were correlated with changes of vigilance or behavior. (d) Fluctuations of the dynamics were not necessarily followed by seizures. CONCLUSIONS: Our results indicate that EEG dynamics frequently change before seizures. These preictal changes are most often associated with the EEG changes accompanying transitions between states of vigilance. The preictal dynamic changes may represent physiologic changes acting as facilitating factors or pathologic changes reflecting a network dysfunction.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".