aEEG vs cEEG’s sensivity for seizure detection in the setting of neonatal intensive care units: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Amplitude-integrated electroencephalography (aEEG)'s accuracy compared to conventional electroencephalography (cEEG) has not been fully established. The aim of our study was to conduct a systematic review on the sensitivity of the aEEG for neonatal seizure detection. METHODS: Studies from PubMed and Google Scholar databases comparing recordings of cEEG and aEEG in newborns were included according to the PRISMA method. A quality assessment using the QUADAS-2 tool was provided. A random-effect model was used to account for different sources of variations among studies. Publication biases were represented by a funnel plot, and funnel plot symmetry was assessed. RESULTS: Fourteen studies were reported; sensitivity of each diagnostic tool used (single-channel aEEG, two-channel aEEG, two-channel aEEG plus raw trace EEG) was compared to that of the gold-standard cEEG and to those of the other methods used. Overall sensitivity of the aEEG ranged from 31.25% to 90%. CONCLUSION: Our study provides evidence that sensitivity of aEEG varies significantly and that seizure detection rate is lower than that of cEEG. The two-channel aEEG with raw trace EEG shows a high sensitivity and might represent a valid alternative to the cEEG in the setting of neonatal intensive care units (NICUs).
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".