An Implantable Closedloop Asynchronous Drug Delivery System for the Treatment of Refractory Epilepsy
Bibliographic record
Abstract
In this paper, we present an implantable device for intra-cerebral electroencephalography (icEEG) data acquisition and real-time epileptic seizure detection with simultaneous focal antiepileptic drug injection feedback. This implantable device includes a neural signal amplifier, an asynchronous seizure detector, a drug delivery system (DDS) including a micropump, and a hybrid subdural electrode (HSE). The asynchronous detection algorithm is based on data-dependent analysis and validated with Matlab tools. The detector and DDS have a power saving mode. The HSE contacts are made of Platinum (Pt) encapsulated with polydimethylsiloxane (PDMS). Given the heterogeneity of electrographic seizure signals and seizure suppression threshold, the implantable device provides tunable parameters facility through an external transmitter to adapt to each individual's neurophysiology prior to clinical deployment. The proposed detector and DDS were assembled in Ø 50 mm and Ø 30 mm circular printed circuit boards, respectively. The detector was validated using icEEG recordings of seven patients who had previously undergone an intracranial investigation for epilepsy surgery. The triggering of the DDS was tested and a predefined seizure suppression dose was delivered ~16 s after electrographical seizure onsets. The device's power consumption was reduced by 12% in active mode and 49% in power saving mode compared to similar seizure detection algorithms implemented with synchronous architecture.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".