Machine learning microserver for neuromodulation device training
Bibliographic record
Abstract
Determining a treatment for those with refractory epilepsy often requires an observation period in an Epilepsy Monitoring Unit (EMU) where neural recordings are analyzed to localize a seizure onset zone. This region can be targeted by an implanted neuromodulation device to detect and inhibit ictal activity. Due to the patient-specific nature of epilepsy, on-device machine learning has been demonstrated to improve seizure detection accuracy. However, chronic implants experience considerable recording signal variability over time, leading to a degradation in treatment efficacy. This suggests the need for post-implantation learning which is impractical to perform on a power-constrained device. Presented here is a patient-localized microserver which enables continuous model adaptation aided by unsupervised machine learning. The system employs a one-class support vector machine (OC-SVM) to identify irregular neural activity for remote clinical assessment and device retraining. The system performance is demonstrated using 500 hours of human intracranial EEG (iEEG) where a clinical seizure detection rate of 97.05% is achieved.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".