A portable dry-electrode ECG device for rapid and accurate neonatal heart rate monitoring during resuscitation
Bibliographic record
Abstract
Each year, around 10% of infants globally will require resuscitation at birth. Pediatricians can use stethoscope, electrocardiogram (ECG) or pulse oximetry to determine heart rate (HR) which is used to guide resuscitation steps. HR must be acquired accurately and quickly. However, current HR detection modalities are either inaccurate or too slow. This work offers a novel infant heart rate detector (iHRD) using single-lead dry electrode ECG that can display HR accurately within the first 10 seconds of initial contact. A research ethics board approved validation study is conducted on 50 healthy newborns comparing iHRD's HR with clinical HR monitors at a community hospital. 3-minute newborn single-lead ECGs and HR are recorded, and HR is annotated every 2 seconds. Statistical HR analysis is performed to ensure iHRD's feasibility and reliability. With 2741 HR datapoints, excluding outliers, the iHRD detected HR with 94.5% accuracy with time from contact to HR display under 10 seconds. Overall, the iHRD using dry electrode single-lead ECG showed good results in providing reliable HR quickly for neonatal resuscitation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".