An automatic warning system for epileptic seizures recorded on intracerebral electroencephalograms /
Bibliographic record
Abstract
A seizure warning system for intracerebral EEG is proposed. It is designed for clinical use with the intention of identifying sections of EEG containing seizure activity and alerting medical staff as a seizure occurs. The system is based on data filtering, spectral feature extraction, probability analysis using Bayes' theorem, spatial and temporal context analysis, and user tuneability. The system was designed using 407 hours of EEG from 19 patients having 152 seizures. Once developed, the system was tested with a different set of EEGs from 19 patients having a total of 100 seizures during 389 hours. Average results for the testing data were promising, with 86% sensitivity, a false detection rate of 0.47/hour, and a delay time of 16 seconds. Compared to current clinical systems, this shows a 9% enhancement in sensitivity, and a false detection improvement by a factor of 9.6. The aim of tuneability was also reached.
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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) | 0.002 | 0.001 |
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".