$\mathcal{S}^{3}$1DCNN: A Compact Stacked Spectral-Spatial Attention 1DCNN for Seizure Prediction with Wearables
Bibliographic record
Abstract
Seizure prediction has become a crucial field of research that aims to improve the lives of patients with drug-resistant epilepsy by reducing their anxiety and allowing the implementation of precautionary measures. Recently, deep learning has shown remarkable advancements in epilepsy prediction. However, this progress comes with increased computational demands and memory usage, which makes it unsuitable for low-power wearable devices. This work proposes a compact stacked spectral-spatial attention 1DCNN ($S^{3}$1DCNN) leveraging the short-time Fourier transform (STFT). This model aims to enhance the interpretable ability to analyze non-stationary electroencephalography (EEG) signals, making it suitable for implementation in wearable biomedical devices. The results demonstrate that the proposed method outperforms recent state-of-the-art methods, achieving an average sensitivity of 92.1 %, an average false prediction rate (FPR) of 0.0081h, an average area under the ROC curve (AUC) of 0.980, and an estimated energy consumption of 0.21 µJ per inference on the American Epilepsy Society Seizure Prediction Challenge (AES) dataset. It demonstrates our method's promising application potential in low-power and energy-efficient wearable devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".