A Multi-Feature Nonlinear-SVM Seizure Detection Algorithm with Patient-Specific Channel Selection and Feature Customization
Bibliographic record
Abstract
The design, optimization, and validation results of a patient-specific seizure detection algorithm are presented. The algorithm employs both mono-variate (spectral energy) and bivariate (narrow-band phase synchrony) features. The computational complexity of both feature extraction and brain state classification is optimized to enable the algorithms integration into a low-power implantable/wearable microprocessor. The patient specificity of the algorithm includes (a) the nonlinear RBF-SVM classifier's hyperplane characteristics, (b) band selection for phase extraction, and (c) channel selection (dimensionality reduction) for spectral energy extraction. The algorithms performance is validated on pre-recorded EEG data from 23 patients (969 hours, 198 seizures in total) and shows a seizure detection sensitivity and specificity of 96.87% and 99.95%, respectively. A comparison to the state of the art in terms of various design and performance parameters is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".