Bibliographic record
Abstract
A new on-line adaptive methodology is introduced for detecting a suspected epileptic seizure from an electroencephalogram (EEG). This is achieved by using the angle between two progressive oblique spaces. Two seizure indices are introduced. In the absence of seizure, the EEG remains short time stationary, and hence the seizure indices remain approximately unity. If and when abnormality or epileptic seizure activity occurs, the EEG becomes non-stationary causing the seizure indices to drop in value. The dips in the seizure indices indicate the time at which the seizure activity occurs, while the magnitude of the dips indicate the strength of the abnormality. Simulation is carried out to show that the methodology can be used to detect the signal burst in a stationary or a short time stationary environment adaptively. Probability of error detection is given for both seizure indices. Results for real data collected from epileptic patients are given to substantiate the methodology. The proposed methodology provides an objective criterion for detecting suspected epileptic seizure. It can be used to analyze routine and 24 hour EEG records. Since the detection is on-line and adaptive, if and when a possible epileptic seizure activity is detected, the methodology can be applied to activate appropriate neuro-transmitters (thalamic, cerebellar, or vagal) instantly as a preventive measure. The algorithm can be implemented in VLSI form as an implantable device.>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".