Mathematical Models for Seizure Source Localization in Neonates Using Machine Learning and Finite Element Method
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, monitoring of the neonatal brain activity is a standard procedure implemented in neonatal intensive care units (NICUs). As a result, the amount of data generated by continuous monitoring during the infant stay at NICU is rather large and thus impossible to be completely analyzed/overviewed by pediatric neurologist. To this purpose various automated systems for seizure detection have been proposed as these seizures are important to be adequately monitored in order to attempt to remedy them and/or reduce detrimental effects this condition may have on the development of a patient. Electroencephalography is a commonly used technique to detect temporal changes and detect these seizures but lacks desired spatial resolution. To this purpose advanced signal processing algorithms are needed but their accuracy often relies on adequate geometry information which is often missing as neonatal patients are rarely subjected to high energy image acquisition. In this paper we propose a source localization algorithm that uses machine learning and inverse finite-element electromagnetic (EM) models that have potential of estimating seizure locations without the need for obtaining accurate geometry information for every patient. As a preliminary approach we evaluate the proposed algorithm using computer simulated large data set using simplified geometry of spherically shaped neonatal head.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".