Slow Activity in Focal Epilepsy During Sleep and Wakefulness
Bibliographic record
Abstract
INTRODUCTION: We aimed to test differences between healthy subjects and patients with respect to slow wave activity during wakefulness and sleep. METHODS: Fifteen patients affected by nonlesional focal epilepsy originating within temporal areas and fourteen matched controls underwent a 24-hour EEG recording. We studied the EEG power spectral density during wakefulness and sleep in delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz), and beta (16-20 Hz) bands. RESULTS: During sleep, patients with focal epilepsy showed higher power from delta to beta frequency bands compared with controls. The effect was widespread for alpha band and above, while localized over the affected hemisphere for delta (sleep cycle 1, P = .006; sleep cycle 2, P = .008; sleep cycle 3, P = .017). The analysis of interhemispheric differences showed that the only frequency band stronger over the affected regions was the delta band during the first 2 sleep cycles (sleep cycle 1, P = .014; sleep cycle 2, P = .002). During wakefulness, patients showed higher delta/theta activity over the affected regions compared with controls. CONCLUSIONS: Patients with focal epilepsy showed a pattern of power increases characterized by a selective slow wave activity enhancement over the epileptic regions during daytime and sleep. This phenomenon was stronger and asymmetric during the first sleep cycles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".