Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study

Hochhausen, Nadine; Pereira, Carina Barbosa; Leonhardt, Steffen; Rossaint, Rolf; Czaplik, Michael

doi:10.3390/s18051618

Cited by 33 publications

(28 citation statements)

References 34 publications

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“…Physiologically, RF reflects the cardiorespiratory changes during various physical activities (Nicolò et al., 2017). Pathologically, RF could be used to differentiate the severities of patients (Massaroni et al., 2018) and indicate the condition of recovery from anesthesia (Hochhausen et al., 2018) and the necessity of admission to the intensive care unit (Al-Khalidi et al., 2011). Despite its clinical significance, the clinical RF monitoring still relies on the manual recording, which is inaccurate and often neglected (Flenady et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

et al. 2019

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Background: It is known that the respiration-modulated photoplethysmographic (PPG) signals could be used to derive respiratory frequency (RF) and that PPG signals could be measured from different body sites. However, the accuracy of RF derived from PPG signals of different body sites has not been comprehensively investigated. Objective: This study aims to investigate the difference in the accuracy of PPG-derived RFs between measurements from different body sites, respectively, for normal and deep breathing conditions. Methods: Under normal and deep breathing patterns, the PPG signals were recorded sequentially in a randomized order from six body sites [finger, wrist under (anatomically volar), wrist upper (dorsal), earlobe, and forehead] of 36 healthy subjects. Simultaneously, the reference respiratory signal was measured by a respiratory belt on the chest. Using the frequency demodulation approach, respiratory signals were extracted from PPG signals for calculating RF by power spectral density. The bias between PPG-derived and reference RFs was then analyzed statistically using analysis of variance and non-parametric tests, Bland-Altman analysis, and linear regression to investigate the difference in RF bias between different sites. Results: The RF bias was significantly influenced by the breathing pattern and measurement site (both p < 0.001). Under normal breathing, the RF bias was insignificant in the arm, forehead, and wrist under (all p > 0.05) and significant in the other sites (all p < 0.05). Significant linear relationship between PPG-derived and reference RFs existed at all the sites ( p < 0.001) except the wrist upper ( p > 0.05). The linearity between PPG-derived and reference RFs was highest at the forehead (slope of best-fit line: 0.90, R 2 : 0.64), followed by the earlobe, finger, arm, and wrist under (slope: 0.71, R 2 : 0.40). Under deep breathing, there was no significant RF bias in all the measurement sites ( p > 0.05) except forehead ( p = 0.048). The effect of measurement site on RF bias was not significant ( p > 0.05). The finger had the smallest RF bias and the narrowest limits of agreement. Conclusion: This study has demonstrated that the accuracy of PPG-derived RF depends on the measurement site and breathing pattern. The best sites are the forehead and finger, respectively, for normal and deep breathing patterns.

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Section: Introductionmentioning

confidence: 99%

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

et al. 2019

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“…There have been increasing demands for contactless, unobtrusive, feasible, and reliable techniques for monitoring patient respiration. Infrared thermography has emerged as a promising monitoring tool in various medical settings, such as fever screening [ 12 ], monitoring of thermoregulation in neonates [ 13 ], and in the PACU but only in small numbers (n = 28) [ 14 ]. The RR measurement algorithm using thermography is based on temperature fluctuations at the region of interest (around the nostrils) during the respiratory cycle.…”

Section: Discussionmentioning

confidence: 99%

Non-contact thermography-based respiratory rate monitoring in a post-anesthetic care unit

Kwon

Ikeda

Kim

et al. 2020

J Clin Monit Comput

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In patients at high risk of respiratory complications, pulse oximetry may not adequately detect hypoventilation events. Previous studies have proposed using thermography, which relies on infrared imaging, to measure respiratory rate (RR). These systems lack support from real-world feasibility testing for widespread acceptance. This study enrolled 101 spontaneously ventilating patients in a post-anesthesia recovery unit. Patients were placed in a 45° reclined position while undergoing pulse oximetry and bioimpedance-based RR monitoring. A thermography camera was placed approximately 1 m from the patient and pointed at the patient’s face, recording continuously at 30 frames per second for 2 min. Simultaneously, RR was manually recorded. Offline imaging analysis identified the nares as a region of interest and then quantified nasal temperature changes frame by frame to estimate RR. The manually calculated RR was compared with both bioimpedance and thermographic estimates. The Pearson correlation coefficient between direct measurement and bioimpedance was 0.69 (R 2 = 0.48), and that between direct measurement and thermography was 0.95 (R 2 = 0.90). Limits of agreement analysis revealed a bias of 1.3 and limits of agreement of 10.8 (95% confidence interval 9.07 to 12.5) and − 8.13 (− 6.41 to − 9.84) between direct measurements and bioimpedance, and a bias of −0.139 and limits of agreement of 2.65 (2.14 to 3.15) and − 2.92 (− 2.41 to 3.42) between direct measurements and thermography. Thermography allowed tracking of the manually measured RR in the post-anesthesia recovery unit without requiring patient contact. Additional work is required for image acquisition automation and nostril identification. Electronic supplementary material The online version of this article (10.1007/s10877-020-00595-8) contains supplementary material, which is available to authorized users.

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“…In absence of other movement artifacts, these systems estimate the respiratory rate, and many of them have been validated while subjects lying in bed, including neonates (Rehoumaa et al, 2018;Janssen et al, 2016), babies (Al-Naji and Chahl, 2016;Regev and Wulich, 2017) and adults (Bartula et al, 2013;Nakajima et al, 2001). Thermal cameras allow respiratory rate estimation by monitoring dynamic thermal activity emitted from specific areas (usually nostrils), as the temperature of exhaled air is higher than the inhaled air (Hochhausen et al, 2018;Pereira et al, 2017;Lewis et al, 2011;Abbas et al, 2011). In addition, some authors have combined both methods in order to increase the accuracy (Bennett et al, 2017;Procházka et al, 2017;Pereira et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Monitoring breathing rate by fusing the physiological impact of respiration on video-photoplethysmogram with head movements

Iozza

Lázaro²,

Cerina³

et al. 2019

Physiol. Meas.

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Objective: The simple observation of breathing rate (BR) remains the first and often the most sensitive marker of acute respiratory dysfunction. In fact, there are evidences that drastic changes in BR are a predictive indicator of adverse events (i.e. cardiac arrest). The aim of this study is to develop a camera-based technology that may provide near-continuously estimation of BR considering the effect of respiration on videoPPG (vPPG). Approach: The technology has been tested in two different experimental settings, including controlled BR and more challenging scenarios with spontaneous breathing pattern. Video data were processed offline to derive the vPPG signal. The method derives respiration from beat-to-beat PPG rate and morphology changes in amplitude and width driven by the physiological relationships between vPPG and respiration. Moreover, respiratory-induced head movements were used as additional source of information for the vPPG system. A combination of these methods has been exploited to estimate the respiratory rate every 10 second. Main Results: According to the results, respiratory frequencies in the central range (0.2-0.4 Hz) may be estimated using vPPG system with low relative error, R < 2 % and interquartile range of order IQR < 5 %. However, the vPPG system showed a drop in performance at respiratory range boundaries, around 0.1 Hz and 0.5 Hz. Significance: This camera-based technology can be used as ubiquitous BR monitoring system. However, vPPG-based systems should consider the effect of the BR in the estimation, mainly in applications where respiratory rate is out of the range 0.2-0.4 Hz.

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Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study

Cited by 33 publications

References 34 publications

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

Non-contact thermography-based respiratory rate monitoring in a post-anesthetic care unit

Monitoring breathing rate by fusing the physiological impact of respiration on video-photoplethysmogram with head movements

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