STFT-Based Segmentation in Model-Based Seizure Detection
Bibliographic record
Abstract
To aid the review of long-term electroencephalograph (EEG), it is necessary to develop automatic seizure detection methods. In the literature, numerous seizure detection methods based on parameterization of the EEG have been presented. Recently a new patient-specific model-based method using Statistically Optimal Null Filters (SONF) has been proposed for seizure detection [1], This method uses stationary segments of a template seizure to generate the necessary seizure model (basis functions) that is used for all subsequent seizure detections. In this approach, the necessary stationary segments within the template are manually identified based on the constancy of the dominant rhythm. The manual selection of stationary segments is cumbersome in practice. In this paper, we present short-time-Fourier-transform (STFT) based automatic segmentation of template seizure resulting in practically usable model-based seizure detection. To assess the performance of the proposed algorithm, a comparison with the visual (manual) method of epoch selection on simulated as well as on the template seizures of five different patients is done. The overall performance improvements are evident in terms of enhanced seizure detection sensitivity and reduced number of false positives.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".