Optimum Window Size and Overlap for Robust Probabilistic Prediction of Seizures with iEEG
Bibliographic record
Abstract
Epilepsy is a brain disorder that can significantly affect a patient's health. Therefore, seizure prediction techniques have gained a lot of attention to minimize the potential damages caused by epilepsy and improve the quality-of-life of epileptic patients. In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) signals as ictal or interical, in order to efficiently perform human seizure prediction. One of the most important parameters in predicting seizure is the size of the sliding window, whose optimization may significantly affect performance, as well as overlapping between windows. In this study, an optimum sliding window and overlapping rate are proposed for efficient seizure prediction. They allow accurate prediction of seizure events from a large set of EEG data. Applied to iEEG recordings of eight patients in the Freiburg EEG database, the proposed approach exhibits a sensitivity of 68% and specificity of 100% using 2-second-long window and 50% overlapping via 10 fold-cross validation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".