Detection of epileptic seizures in stereo-EEG using frequency-weighted energy
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new algorithm for seizure detection based on the evolution-like characteristics of a seizure. Most of the existing algorithms for automatic detection of the epileptic seizures in electroencephalograms (EEG) rely upon some pre-defined/patient-tunable detection threshold to classify the data as normal or abnormal. In this paper, we present a method for seizure detection in stereoencephalograms (SEEG) using frequency-weighted energy. The method does not require a threshold or any a priori information about the seizure for its detection. The method is gradient-based and any activity that exceeds the minimum duration satisfying our criteria is considered as a potential seizure activity. The performance of the algorithm is evaluated on 100 hours of single channel SEEG data obtained from five different patients. An overall sensitivity of 96.6% and a false detection rate of 0.21/h is obtained on the complete data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it