Automatic seizure detection in newborns and infants
Bibliographic record
Abstract
Current used methods of seizure detection were not designed to detect seizures in newborns and infants. Since seizure patterns in this age group art very different from patterns in older subjects, the standard method performs poorly. The authors present a method aimed at detecting these specific patterns, which an often very focal, slow and of particularly gradual onset. The EEG of each channel is broken down into overlapping 10-second epochs, for which the spectrum is computed. Features, including frequency, amplitude and width of the dominant spectral peak, are compared to a constantly updated background. The method was evaluated on 10 recordings from the Montreal Children's Hospital (average age 32 days) and 9 recordings from the Texas Children's Hospital (average age 5 days). Recording lasted an average of 5.5 hours and there was an average of 8.2 seizures/recording. Analysis by the existing "adult" method yielded an average detection rate of only 28% and an average of 1.9 false detections/hour. The new method yielded an average detection rate of 74% and an average of 2.4 false detections/hour. Although the false detection rate should be reduced the detection performance is now comparable to that in adults and is sufficiently encouraging to envisage clinical implementation.
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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".