Physics Inspired Neural Network for Cortical Electromagnetic Activity of Neonates
Bibliographic record
Abstract
Neonatal convulsions are one of the most common emergency neurological events in the early period after birth with the frequency of 1.5 to 3 in 1000 live births. Consequently, neonatal intensive care units (NICU) continuously monitor electrical activity of preterm infants for both short-term and long-term interventions and/or treatments. These techniques commonly utilize only detection algorithms whose main purpose is to detect events in electroencephalography (EEG) recordings. In addition to those, estimation techniques can potentially provide insight into the brain development and indicate regions of higher convulsion rate. The estimation of electrical activity of the brain in adults has been a subject of considerable research interest in adults. To this purpose in this paper we investigate the possibility of estimating the cortical activity of the neonates using physics informed neural networks. The main idea behind this approach lies in the possibility of significantly reducing the computational intensity of the inverse approach by avoiding the need for inversion using artificial neural networks (ANN).
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".