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

Peter Chan, Gabriel Wong, Toan Nguyen, Tam Nguyen, John McNeil, Ingrid Hopper

Research output: Contribution to journalArticleResearchpeer-review

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 0.4-0.6m and >1m respectively. IRT-derived RR using two separate computer vision algorithms outperformed ECG derived RR at distances of 0.4-0.6m. 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 1m 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 >19bpm (r=0.562, p=0.029). Overall agreement between IRT-derived RR at distances of 0.4-0.6m 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.
Original languageEnglish
Number of pages10
JournalJournal of Clinical Monitoring and Computing
DOIs
Publication statusAccepted/In press - 2 Dec 2019

Keywords

  • Contactless
  • Monitoring
  • Respiratory rate
  • Infrared
  • Critical carE

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