A novel morphology-based classifier for automatic detection of epileptic seizures
Bibliographic record
Abstract
Most of the automatic seizure detection schemes reported in the literature are complex for detecting seizures that are of (a) short duration, (b) minimal amplitude evolution, or (c) non-rhythmic mixed frequency epileptic activity. We present a novel morphology-based classifier to detect epileptic seizures for intracranial EEG recording. The method characterizes epileptic seizure by detecting continual presence of sharp half-waves in the EEG. Performance is evaluated on single channel intracranial EEG of seven patients, and compared to two previously developed methods for intracranial EEG recordings by our research group. The method detects seizure of varying types (rhythmic, non-rhythmic, short- and long- seizures) with a sensitivity of 100%, a false detection rate of 0.1/h and an average onset delay of 9.1 s. The method outperforms the two previously developed methods and is computationally simple for real-time application. Preliminary results on seven patients data are very promising.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".