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

  • Peter ChanEmail author
  • Gabriel Wong
  • Toan Dinh Nguyen
  • Tam Nguyen
  • John McNeil
  • Ingrid Hopper
Original Research
  • 16 Downloads

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.

Keywords

Contactless Monitoring Respiratory rate Infrared Critical care 

Notes

Author contributions

PYC: Designed the protocol, established ethics, acquired data, and wrote the paper. GW: Responsible for data collection and analysis. TN: Responsible for the image recognition algorithm used in the statistical analysis. TN: Responsible for technical guidance and equipment troubleshooting and supervision of the research project. JM: Responsible for project troubleshooting, protocol design and editing. IH: Responsible for project management and coordination, manuscript troubleshooting and general supervision.

Funding

Supported by National Health and Medical Research Council (NH&MRC) Equipment Grant.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Eastern Health Intensive Care ServicesEastern HealthMelbourneAustralia
  2. 2.Monash eResearch Centre, Monash UniversityMelbourneAustralia
  3. 3.St Vincent’s HospitalMelbourneAustralia
  4. 4.School of Public Health and Prevention MedicineMonash UniversityMelbourneAustralia

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