Distilling Invariant Representations With Domain Adversarial Learning for Cross-Subject Children Seizure Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Seizure prediction based on electroencephalogram (EEG) has great potential to improve patients’ life quality. Due to the high heterogeneity in distributions of EEG signals among different patients, conventional studies usually show poor generalization ability when transferring the model to new patients, which also leads to difficulties in clinical applications. To alleviate the challenging issue concerning cross-subject domain shift, we propose a transformer-based domain adversarial model. Our model first adopts a pretrained general neural network to extract common features from the EEG signals of available patients. Then, we design a distiller module and a domain discriminator module to perform domain adaptation training based on a small amount of labeled data from the new-coming patient. During the adaptation process, conditional domain adversarial training with the addition of label information is employed to remove patient-related information from the extracted features to learn a common seizure feature space among different patients. Our proposed seizure prediction method is evaluated on the CHB-MIT EEG database. The proposed model achieves a sensitivity of 79.5%, a false alarm rate (FPR) of 0.258/h, and an AUC of 0.814. Experimental results demonstrate that the proposed method can effectively reduce interpatient domain disparity compared to state-of-the-art methods.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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