High-Frequency Oscillations in the Scalp EEG of Intensive Care Unit Patients With Altered Level of Consciousness
Bibliographic record
Abstract
PURPOSE: In comatose patients, distinguishing between nonconvulsive status epilepticus and diffuse structural or metabolic encephalopathies is often challenging. Both conditions can generate periodic discharges on EEG with similar morphology and periodicity. We investigated the occurrence of high-frequency oscillations-potential biomarkers of epileptogenesis-on scalp EEG of comatose patients with periodic discharges in the EEG. METHODS: Fifteen patients were included. Patients were divided into three groups, according to underlying etiology: Group 1, seizure related; group 2, structural; group 3, nonstructural. EEG recordings were compared with respect to the presence and rates of gamma (30-80 Hz) and ripples (80-250 Hz). RESULTS: Patients were 23 to 106 years old (median, 68 years); 60% were female. 206 channels were eligible for analysis (median, 15 channels/patient). Overall, 43% of channels showed gamma, and 24% had ripples. Group 2 showed the highest proportion of channels with gamma (47%), followed by group 1 (38%) and group 3 (36%). Mean gamma rates were higher in group 2 (4.65 gamma/min/channel) than in group 1 (1.52) and group 3 (1.44) (P < 0.001). Group 2 showed the highest proportion of channels with ripples (29.2%), followed by group 1 (15%) and group 3 (24.2%). Mean ripple rates were higher in group 2 (5.09 ripple/min/channel) than in group 1 (0.96) and group 3 (0.83) (P < 0.001). CONCLUSIONS: Fast oscillations, including high-frequency oscillations, can be detected in scalp EEG of patients with altered consciousness. High rates of fast activity may suggest an underlying structural brain lesion. Future studies are needed to determine whether fast oscillations in the setting of acute/subacute brain lesions are a biomarker of subsequent development of human epilepsy.
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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 | 0.000 | 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) | 0.001 | 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".