A novel dual-stage classifier for automatic detection of epileptic seizures
Bibliographic record
Abstract
In long-term monitoring of electroencephalogram (EEG) for epilepsy, it is crucial for the seizure detection systems to have high sensitivity and low false detections to reduce uninteresting and redundant data that may be stored for review by the medical experts. However, a large number of features and the complex decision boundaries for classification of seizures eventually lead to a trade-off between sensitivity and false detection rate (FDR). Thus, no single classifier can fulfill the requirements of high sensitivity with a low FDR and at the same time be a computationally efficient system suitable for real-time application. We present a novel, simple, computationally efficient seizure detection system to enhance the sensitivity with a low FDR by proposing a dual-stage classifier. This overall system consists of a pre-processing unit, a feature extraction unit and a novel dual-stage classifier. The first stage of the proposed classifier detects all true seizures, but also many false patterns, whereas the second stage of the proposed classifier minimizes false detections by rejecting patterns that may be artifacts. The performance of the novel seizure detection system has been evaluated on 300 hours of single-channel depth electroencephalogram (SEEG) recordings obtained from fifteen patients. An overall improvement has been observed in terms of sensitivity, specificity and FDR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".