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Erschienen in: Journal of Clinical Monitoring and Computing 6/2020

02.12.2019 | Original Research

Estimation of respiratory rate using infrared video in an inpatient population: an observational study

verfasst von: Peter Chan, Gabriel Wong, Toan Dinh Nguyen, Tam Nguyen, John McNeil, Ingrid Hopper

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 6/2020

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Abstract

Respiratory rate (RR) is one of the most sensitive markers of a deteriorating patient. Despite this, there is significant inter-observer discrepancy when measured by clinical staff, and modalities used in clinical practice such as ECG bioimpedance are prone to error. This study utilized infrared thermography (IRT) to measure RR in a critically ill population in the Intensive Care Unit. This study was carried out in a Single Hospital Centre. Respiratory rate in 27 extubated ICU patients was counted by two observers and compared to ECG Bioimpedance and IRT-derived RR at distances of 0.4–0.6 m and > 1 m respectively. IRT-derived RR using two separate computer vision algorithms outperformed ECG derived RR at distances of 0.4–0.6 m. Using an Autocorrelation estimator, mean bias was − 0.667 breaths/min. Using a Fast Fourier Transform estimator, mean bias was − 1.000 breaths/min. At distances greater than 1 m no statistically significant signal could be obtained. Over all frequencies, there was a significant relationship between the RR estimated using IRT and via manual counting, with Pearson correlation coefficients between 0.796 and 0.943 (p < 0.001). Correlation between counting and ECG-derived RR demonstrated significance only at > 19 bpm (r = 0.562, p = 0.029). Overall agreement between IRT-derived RR at distances of 0.4–0.6 m and gold standard counting was satisfactory, and outperformed ECG derived bioimpedance. Contactless IRT derived RR may be feasible as a routine monitoring modality in wards and subacute inpatient settings.
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Metadaten
Titel
Estimation of respiratory rate using infrared video in an inpatient population: an observational study
verfasst von
Peter Chan
Gabriel Wong
Toan Dinh Nguyen
Tam Nguyen
John McNeil
Ingrid Hopper
Publikationsdatum
02.12.2019
Verlag
Springer Netherlands
Erschienen in
Journal of Clinical Monitoring and Computing / Ausgabe 6/2020
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-019-00437-2

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