Wireless skin sensors for electrocardiogram and heart rate monitoring in the neonatal intensive care unit: a prospective feasibility, safety, and accuracy study
Bibliographic record
Abstract
Objectives: Assess feasibility, safety, and accuracy of electrocardiogram (ECG) and heart rate (HR) monitoring in neonates, using a new wireless skin sensor. Methods: Prospective observational study in infants of any gestational age admitted in the neonatal intensive care unit. ECG/HR signals were simultaneously recorded from a standard wired and new wireless system with bedside annotations. Feasibility was evaluated as signal coverage, gap numbers/durations, and sources of gaps. Safety was appraised by changes in skin condition and pain after/upon wireless sensor removal. Accuracy was measured using bias and 95% limits of agreement, and the coefficient of determination. The ability of the wireless sensors to detect normal and abnormal HR values was evaluated using a Clark Error Grid. Additionally, user satisfaction from parents and nurses were appraised using a short questionnaire. Results: 25 infants had 757 h of recorded signals over 96 days. ECG coverage was 99.9% [IQR: 99.9%-99.95%] for the wired vs 97.8% [IQR: 81.6%-99.9%; p < 0.00] for the wireless system, while HR coverage was 99.4% [IQR: 98.6%-99.9%] vs 89.7% [IQR: 75.6%-97.6%; p < 0.00]. Wireless ECG gaps were <5 s in 97% of cases, and HR gaps <30 s in 85%. All ECG gaps and 57% of HR gaps were due to Bluetooth disconnection (BD). 78% of BD in wireless HR were during kangaroo care (78%). Of 192 skin photographs (96 pairs), 98% were taken, showing increased but low skin scores post-removal, with median pain scores also low. Accuracy metrics showed strong agreement, with the Clark Error Grid indicating 97% of paired signals led to the same clinical outcome. Among 23 nurse and 18 parent responses, satisfaction with the wireless system was high. Conclusion: ECG and HR monitoring using a new wireless skin sensor was feasible, safe, and accurate when compared to the wired standard. Future adjustments in the technology are needed to improve signal coverage during handling and KC and test the sensors in unstable and more immature patients. Limitations included challenges in recruiting unstable neonates, variability introduced by multiple raters completing pain assessments, and inability to apply safety metrics to the wired standard of care.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".