Automated pre-ictal phase detection algorithm from EEG signals
Bibliographic record
Abstract
Epilepsy poses a significant burden to society due to associated healthcare costs to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologists diagnose and determine the patient's treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating brain functions that might correspond to epilepsy. This research focuses on developing a new classification technique and the prediction of pre-ictal states that announce epileptic seizures, from the direct EEG data analysis. The approach involves the placement of electrodes on critical regions on the patient's head as a part of a telemetry system which communicates with the EEG recorder and the DSP unit that performs automated pre-ictal state detection based on the obtained EEG signal. Studying potential preconditions and alerting the patient about a possible seizure attack so that s/he can take safety precautions has a potential to improve patient care management.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".