Optimizing Deep Learning Models for Real-Time Seizure Detection Based on Electroencephalogram
Bibliographic record
Abstract
Accurate and real-time seizure detection is crucial for epilepsy management. Deep learning offers promising solutions, but optimal performance in clinical settings requires efficient and accurate models. This paper focuses on optimizing deep learning models for real-time seizure detection from EEG signals. We evaluate and optimize various architectures on the TUH Seizure dataset, focusing on combining model and architecture in order to leverage strengths, hyperparameter tuning and feature extraction. Our optimized MobileNEp (Mobile Network for Epilepsy) model demonstrates superior efficiency and general seizure detection, reaching an AUC of 0.9363. Optimized Transformers models show better specialization for specific seizure types, achieving AUCs of 0.987 for absence and 0.991 for tonic-clonic seizures. This research highlights the importance of model optimization in developing effective deep learning tools for real-time EEG seizure detection in clinical settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".