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 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 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".