A Fully-Asynchronous Low-Power Implantable Seizure Detector for Self-Triggering Treatment
Bibliographic record
Abstract
In this paper, we present a new asynchronous seizure detector that is part of an implantable integrated device intended to identify electrographic seizure onset and trigger a focal treatment to block the seizure progression. The proposed system has a low-power front-end bioamplifier and a seizure detector with intelligent mechanism to reduce power dissipation. This system eliminates the unnecessary clock gating during normal neural activity monitoring mode and reduces power dissipation in the seizure detector; as a result, this device is suitable for long-term implantable applications. The proposed system includes analog and digital building blocks with programmable parameters for extracting electrographic seizure onset information from real-time EEG recordings. Sensitivity of the detector is enhanced by optimizing the variable parameters based on specific electrographic seizure onset activities of each patient. The detection algorithm was validated using Matlab tools and implemented in standard 0.13 μm CMOS process with total die area of 1.5 × 1.5 mm². The fabricated chip is validated offline using intracranial EEG recordings from two patients with refractory epilepsy. Total power consumption of the chip is 9 μW and average detection delay is 13.7 s after seizure onset, well before the onset of clinical manifestation. The proposed system achieves an accurate detection performance with 100% sensitivity and no false alarms during the analyses of 15 seizures and 19 non-seizure datasets.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".