Interaction with slow waves during sleep improves discrimination of physiologic and pathologic high‐frequency oscillations (80–500 Hz)
Bibliographic record
Abstract
OBJECTIVE: To characterize the interaction between physiologic and pathologic high-frequency oscillations (HFOs) and slow waves during sleep, and to evaluate the practical significance of these interactions by automatically classifying channels as recording from normal or epileptic brain regions. METHODS: We automatically detected HFOs in intracerebral electroencephalography (EEG) recordings of 45 patients. We characterized the interaction between the HFOs and the amplitude and phase of automatically detected slow waves during sleep. We computed features associated with HFOs, and compared classic features such as rate, amplitude, duration, and frequency to novel features related to the interaction between HFOs and slow waves. To quantify the practical significance of the difference in these features we classified the channels as recording from normal/epileptic regions using logistic regression. We assessed the results in different brain regions to study differences in the HFO characteristics at the lobar level. RESULTS: We found a clear difference in the coupling between the phase of slow waves during sleep and the occurrence of HFOs. In channels recording physiologic activity, the HFOs tend to occur after the peak of the deactivated state of the slow wave, and in channels with epileptic activity, the HFOs occur more often before this peak. This holds for HFOs in the ripple (80-250 Hz) and fast ripple (250-500 Hz) bands, and different regions of the brain. When using this interaction to automatically classify channels as recording from normal/epileptic brain regions, the performance is better than when using other HFO characteristics. We confirmed differences in the HFO characteristics in mesiotemporal structures and in the occipital lobe. SIGNIFICANCE: We found the association between slow waves and HFOs to be different in normal and epileptic brain regions, emphasizing their different origin. This is also of practical significance, since it improves the separation between channels recording from normal and epileptic brain regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".