Evidence map›Paper›PMID 31872068›Full record

ArticleNPJ digital medicine2019

Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit.

Mauricio Villarroel, Sitthichok Chaichulee, João Jorge, Sara Davis, Gabrielle Green, Carlos Arteta, Andrew Zisserman, Kenny McCormick, Peter Watkinson, Lionel Tarassenko

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mauricio Villarroel1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Sitthichok Chaichulee1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.ORCID 0000-0002-3159-7925
João Jorge1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.ORCID 0000-0003-3723-8103
Sara Davis2Neonatal Unit, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK.
Gabrielle Green2Neonatal Unit, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK.
Carlos Arteta3Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK.
Andrew Zisserman3Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK.ORCID 0000-0002-8945-8573
Kenny McCormick2Neonatal Unit, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK.
Peter Watkinson4Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Lionel Tarassenko1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

Indexed as

Medical imagingTranslational research

Identifiers

PMID31872068
PMCPMC6908711

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.