Implantable Closed-Loop Epilepsy Prosthesis
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we present an implantable closed-loop epilepsy prosthesis, which is dedicated to automatically detect seizure onsets based on intracerebral electroencephalographic (icEEG) recordings from intracranial electrode contacts and provide an electrical stimulation feedback to the same contacts in order to disrupt these seizures. A novel epileptic seizure detector and a dedicated electrical stimulator were assembled together with common recording electrodes to complete the proposed prosthesis. The seizure detector was implemented in CMOS 0.18- μ m by incorporating a new seizure detection algorithm that models time-amplitude and -frequency relationship in icEEG. The detector was validated offline on ten patients with refractory epilepsy and showed excellent performance for early detection of seizures. The electrical stimulator, used for suppressing the developing seizure, is composed of two biphasic channels and was assembled with embedded FPGA in a miniature PCB. The stimulator efficiency was evaluated on cadaveric animal brain tissue in an in vitro morphologic electrical model. Spatial characteristics of the voltage distribution in cortex were assessed in an attempt to identify optimal stimulation parameters required to affect the suspected epileptic focus. The experimental results suggest that lower frequency stimulation parameters cause significant amount of shunting of current through the cerebrospinal fluid; however higher frequency stimulation parameters produce effective spatial voltage distribution with lower stimulation charge.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it