Evidence map›Paper›PMID 27778207›Full record

ArticleJournal of clinical monitoring and computing2017

Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects.

Carina Barbosa Pereira, Xinchi Yu, Michael Czaplik, Vladimir Blazek, Boudewijn Venema, Steffen Leonhardt

Abstract read
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In one paragraph

Article in Journal of clinical monitoring and computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

18 citing papers in PubMed, 64 citations in OpenAlex.

  1. Article
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  4. Review
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  6. Article
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  8. Breathing Pattern Monitoring by Using Remote Sensors.Sensors (Basel, Switzerland) · 2022
    Article
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  10. Article
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  12. Article
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  15. Review
  16. Review
  17. Article
  18. Evaluation of the breathing mode by infrared thermography.Brazilian journal of otorhinolaryngology
    Observational
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

6 authors at 2 institutions in 2 countries.

Carina Barbosa PereiraChair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany. pereira@hia.rwth-aachen.de.ORCID http://orcid.org/0000-0003-1788-4562
Xinchi YuChair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
Michael CzaplikDepartment of Anesthesiology, University Hospital RWTH Aachen, Aachen, Germany.
Vladimir BlazekChair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
Boudewijn VenemaChair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
Steffen LeonhardtChair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
RWTH Aachen University · DECzech Technical University in Prague · CZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diverse studies have demonstrated the importance of monitoring breathing rate (BR). Commonly, changes in BR are one of the earliest and major markers of serious complications/illness. However, it is frequently neglected due to limitations of clinically established measurement techniques, which require attachment of sensors. The employment of adhesive pads or thoracic belts in preterm infants as well as in traumatized or burned patients is an additional paramount issue. The present paper proposes a new robust approach, based on data fusion, to remotely monitor BR using infrared thermography (IRT). The algorithm considers not only temperature modulation around mouth and nostrils but also the movements of both shoulders. The data of these four sensors/regions of interest need to be further fused to reach improved accuracy. To investigate the performance of our approach, two different experiments (phase A: normal breathing, phase B: simulation of breathing disorders) on twelve healthy volunteers were performed. Thoracic effort (piezoplethysmography) was simultaneously acquired to validate our results. Excellent agreements between BR estimated with IRT and gold standard were achieved. While in phase A a mean correlation of 0.98 and a root-mean-square error (RMSE) of 0.28 bpm was reached, in phase B the mean correlation and the RMSE hovered around 0.95 and 3.45 bpm, respectively. The higher RMSE in phase B results predominantly from delays between IRT and gold standard in BR transitions: eupnea/apnea, apnea/tachypnea etc. Moreover, this study also demonstrates the capability of IRT to capture varied breathing disorders, and consecutively, to assess respiratory function. In summary, IRT might be a promising monitoring alternative to the conventional contact-based techniques regarding its performance and remarkable capabilities.

Indexed as

RespirationRespiratory RateSignal Processing, Computer-AssistedAlgorithmsBayes TheoremFemaleHealthy VolunteersHumansMaleModels, BiologicalModels, StatisticalMonitoring, PhysiologicMovementPilot ProjectsVideo RecordingBreathing disordersData fusionPhysiological monitoringRespiratory rateThermal imaging

Identifiers

PMID27778207
OpenAlexW2539390599

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.