A Hierarchical Wavelet Bispectrum Framework for Automated Seizure Detection
Bibliographic record
Abstract
Seizure detection with scalp EEG recordings is a challenging task due to the non-stationarity of neuron dynamics. Relying on static and linear features, traditional methods are often incapable of capturing the nonlinear, transient synchronization that occurs during seizure events. To address this issue, we propose a patient-specific framework based on hierarchical dynamic functional connectivity analysis. The core idea is the integration of the Cross-Wavelet Bispectrum (XWB), quantifying Quadratic Phase Coupling (QPC). We extract a physiology-informed multi-perspective feature vector, which is then processed by downstream ensemble classifiers. The framework achieved a maximal sensitivity of 99.76% and an AUROC of > 0.999 using raw data at an optimal 8-second window length on the CHB-MIT dataset, significantly outperforming linear baselines (sensitivity <75%). Contrary to common practices of normalization, our results indicate that raw inputs have better performance. This illustrates that the absolute spectral magnitude in raw data holds crucial discriminative value. Lastly, feature importance analysis confirms that wavelet-based features dominate the decision-making process, which verifies that modeling non-stationary phase coupling is essential for robust seizure detection.
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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 | 0.002 |
| 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 | 0.000 | 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".