Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Goldhill, D.R.; McNarry, A.F.; Mandersloot, G.; McGinley, A. A Physiologically-Based Early Warning Score for Ward Patients: The Association between Score and Outcome. Anaesthesia 2005, 60, 547–553. [Google Scholar] [CrossRef] [PubMed]
- Subbe, C.P.; Davies, R.G.; Williams, E.; Rutherford, P.; Gemmell, L. Effect of Introducing the Modified Early Warning Score on Clinical Outcomes, Cardio-Pulmonary Arrests and Intensive Care Utilisation in Acute Medical Admissions. Anaesthesia 2003, 58, 797–802. [Google Scholar] [CrossRef] [PubMed]
- Howell, M.D.; Donnino, M.W.; Talmor, D.; Clardy, P.; Ngo, L.; Shapiro, N.I. Performance of Severity of Illness Scoring Systems in Emergency Department Patients with Infection. Acad. Emerg. Med. 2007, 14, 709–714. [Google Scholar] [CrossRef] [PubMed]
- Chatterjee, N.A.; Jensen, P.N.; Harris, A.W.; Nguyen, D.D.; Huang, H.D.; Cheng, R.K.; Savla, J.J.; Larsen, T.R.; Gomez, J.M.D.; Du-Fay-de-Lavallaz, J.M.; et al. Admission Respiratory Status Predicts Mortality in COVID-19. Influenza Other Respir. Viruses 2021, 15, 569–572. [Google Scholar] [CrossRef]
- Wang, J.; Yu, H.; Hua, Q.; Jing, S.; Liu, Z.; Peng, X.; Cao, C.; Luo, Y. A Descriptive Study of Random Forest Algorithm for Predicting COVID-19 Patients Outcome. PeerJ 2020, 8, e9945. [Google Scholar] [CrossRef]
- Chourpiliadis, C.; Bhardwaj, A. Physiology, Respiratory Rate. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2022. [Google Scholar]
- Cretikos, M.A.; Bellomo, R.; Hillman, K.; Chen, J.; Finfer, S.; Flabouris, A. Respiratory Rate: The Neglected Vital Sign. Med. J. Aust. 2008, 188, 657–659. [Google Scholar] [CrossRef]
- Tarassenko, L.; Clifton, D.A.; Pinsky, M.R.; Hravnak, M.T.; Woods, J.R.; Watkinson, P.J. Centile-Based Early Warning Scores Derived from Statistical Distributions of Vital Signs. Resuscitation 2011, 82, 1013–1018. [Google Scholar] [CrossRef]
- Flenady, T.; Dwyer, T.; Applegarth, J. Explaining Transgression in Respiratory Rate Observation Methods in the Emergency Department: A Classic Grounded Theory Analysis. Int. J. Nurs. Stud. 2017, 74, 67–75. [Google Scholar] [CrossRef]
- Drummond, G.B.; Fischer, D.; Arvind, D.K. Current Clinical Methods of Measurement of Respiratory Rate Give Imprecise Values. ERJ Open Res. 2020, 6, 00023–02020. [Google Scholar] [CrossRef]
- Latten, G.H.P.; Spek, M.; Muris, J.W.M.; Cals, J.W.L.; Stassen, P.M. Accuracy and Interobserver-Agreement of Respiratory Rate Measurements by Healthcare Professionals, and Its Effect on the Outcomes of Clinical Prediction/Diagnostic Rules. PLoS ONE 2019, 14, e0223155. [Google Scholar] [CrossRef] [Green Version]
- Massaroni, C.; Nicolò, A.; Lo Presti, D.; Sacchetti, M.; Silvestri, S.; Schena, E. Contact-Based Methods for Measuring Respiratory Rate. Sensors 2019, 19, 908. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Prigent, G.; Aminian, K.; Rodrigues, T.; Vesin, J.-M.; Millet, G.P.; Falbriard, M.; Meyer, F.; Paraschiv-Ionescu, A. Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running. Sensors 2021, 21, 5651. [Google Scholar] [CrossRef] [PubMed]
- Witt, J.D.; Fisher, J.R.K.O.; Guenette, J.A.; Cheong, K.A.; Wilson, B.J.; Sheel, A.W. Measurement of Exercise Ventilation by a Portable Respiratory Inductive Plethysmograph. Respir. Physiol. Neurobiol. 2006, 154, 389–395. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.-H.; Roberge, R.; Powell, J.B.; Shafer, A.B.; Jon Williams, W. Measurement Accuracy of Heart Rate and Respiratory Rate during Graded Exercise and Sustained Exercise in the Heat Using the Zephyr BioHarness. Int. J. Sports Med. 2013, 34, 497–501. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, Y.; Zhu, S.H.; Wang, G.H.; Ye, F.; Li, P.Z. Validity and Reliability of Multiparameter Physiological Measurements Recorded by the Equivital LifeMonitor during Activities of Various Intensities. J. Occup. Environ. Hyg. 2013, 10, 78–85. [Google Scholar] [CrossRef]
- Lombard, W.P. Proceedings of the American Physiological Society. Am. J. Physiol. Leg. Content. 1937, 119, 257–427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wu, T.; Blazek, V.; Schmitt, H.J. Photoplethysmography Imaging: A New Noninvasive and Noncontact Method for Mapping of the Dermal Perfusion Changes; Priezzhev, A.V., Oberg, P.A., Eds.; SPIE: Amsterdam, The Netherlands, 2000; p. 62. [Google Scholar]
- Takano, C.; Ohta, Y. Heart Rate Measurement Based on a Time-Lapse Image. Med. Eng. Phys. 2007, 29, 853–857. [Google Scholar] [CrossRef]
- Verkruysse, W.; Svaasand, L.O.; Nelson, J.S. Remote Plethysmographic Imaging Using Ambient Light. Opt. Express 2008, 16, 21434. [Google Scholar] [CrossRef] [Green Version]
- Allen, J. Photoplethysmography and Its Application in Clinical Physiological Measurement. Physiol. Meas. 2007, 28, R1-39. [Google Scholar] [CrossRef] [Green Version]
- Tohma, A.; Nishikawa, M.; Hashimoto, T.; Yamazaki, Y.; Sun, G. Evaluation of Remote Photoplethysmography Measurement Conditions toward Telemedicine Applications. Sensors 2021, 21, 8357. [Google Scholar] [CrossRef]
- Sun, Y.; Papin, C.; Azorin-Peris, V.; Kalawsky, R.; Greenwald, S.; Hu, S. Use of Ambient Light in Remote Photoplethysmographic Systems: Comparison between a High-Performance Camera and a Low-Cost Webcam. J. Biomed. Opt. 2012, 17, 037005. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Luo, H.; Yang, D.; Barszczyk, A.; Vempala, N.; Wei, J.; Wu, S.J.; Zheng, P.P.; Fu, G.; Lee, K.; Feng, Z.-P. Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. Circ. Cardiovasc. Imaging 2019, 12, e008857. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Coppetti, T.; Brauchlin, A.; Müggler, S.; Attinger-Toller, A.; Templin, C.; Schönrath, F.; Hellermann, J.; Lüscher, T.F.; Biaggi, P.; Wyss, C.A. Accuracy of Smartphone Apps for Heart Rate Measurement. Eur. J. Prev. Cardiolog. 2017, 24, 1287–1293. [Google Scholar] [CrossRef]
- Pham, C.; Poorzargar, K.; Nagappa, M.; Saripella, A.; Parotto, M.; Englesakis, M.; Lee, K.; Chung, F. Effectiveness of Consumer-Grade Contactless Vital Signs Monitors: A Systematic Review and Meta-Analysis. J. Clin. Monit. Comput. 2022, 36, 41–54. [Google Scholar] [CrossRef] [PubMed]
- Jorge, J.; Villarroel, M.; Chaichulee, S.; Guazzi, A.; Davis, S.; Green, G.; McCormick, K.; Tarassenko, L. Non-Contact Monitoring of Respiration in the Neonatal Intensive Care Unit. In Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 30 May–3 June 2017; pp. 286–293. [Google Scholar]
- Couderc, J.-P.; Kyal, S.; Mestha, L.K.; Xu, B.; Peterson, D.R.; Xia, X.; Hall, B. Detection of Atrial Fibrillation Using Contactless Facial Video Monitoring. Heart Rhythm 2015, 12, 195–201. [Google Scholar] [CrossRef]
- Villarroel, M.; Jorge, J.; Pugh, C.; Tarassenko, L. Non-Contact Vital Sign Monitoring in the Clinic. In Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 30 May–3 June 2017; pp. 278–285. [Google Scholar]
- Allado, E.; Poussel, M.; Moussu, A.; Saunier, V.; Bernard, Y.; Albuisson, E.; Chenuel, B. Innovative Measurement of Routine Physiological Variables (Heart Rate, Respiratory Rate and Oxygen Saturation) Using a Remote Photoplethysmography Imaging System: A Prospective Comparative Trial Protocol. BMJ Open 2021, 11, e047896. [Google Scholar] [CrossRef]
- Fitzpatrick, T.B. The Validity and Practicality of Sun-Reactive Skin Types I through VI. Arch. Dermatol. 1988, 124, 869–871. [Google Scholar] [CrossRef]
- Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med. 2016, 15, 155–163. [Google Scholar] [CrossRef] [Green Version]
- Pai, A.; Veeraraghavan, A.; Sabharwal, A. HRVCam: Robust Camera-Based Measurement of Heart Rate Variability. J. Biomed. Opt. 2021, 26, 022707. [Google Scholar] [CrossRef]
- Yue, H.; Li, X.; Cai, K.; Chen, H.; Liang, S.; Wang, T.; Huang, W. Non-Contact Heart Rate Detection by Combining Empirical Mode Decomposition and Permutation Entropy under Non-Cooperative Face Shake. Neurocomputing 2020, 392, 142–152. [Google Scholar] [CrossRef]
- Al-Naji, A.; Chahl, J.; Lee, S.-H. Cardiopulmonary Signal Acquisition from Different Regions Using Video Imaging Analysis. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 2019, 7, 117–131. [Google Scholar] [CrossRef]
- Wei, B.; He, X.; Zhang, C.; Wu, X. Non-Contact, Synchronous Dynamic Measurement of Respiratory Rate and Heart Rate Based on Dual Sensitive Regions. BioMed Eng. OnLine 2017, 16, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sanyal, S.; Nundy, K.K. Algorithms for Monitoring Heart Rate and Respiratory Rate from the Video of a User’s Face. IEEE J. Transl. Eng. Health Med. 2018, 6, 2700111. [Google Scholar] [CrossRef] [PubMed]
Female, n (%) | 471 (48.9%) |
---|---|
Age, mean (SD), years | 56.6 (±16.0) |
Body mass index, mean (SD), kg/m2 | 28.1 (±7.3) |
BMI < 30, n (%) | 650 (67.5%) |
Class 1 obesity, n (%) | 172 (17.9%) |
Class 2 obesity, n (%) | 67 (7.0%) |
Class 3 obesity, n (%) | 74 (7.7%) |
Fitzpatrick skin Color scale, n (%) | |
1 | 20 (2.1%) |
2 | 512 (3.2%) |
3 | 360 (37.4%) |
4 | 58 (6.0%) |
5 | 8 (0.8%) |
6 | 5 (0.5%) |
Standard Patients (n = 924) | Outlier Patients (n = 21) | p-Value * | |
---|---|---|---|
Female, n (%) | 450 (48.7%) | 11 (52.4%) | 0.739 |
Age, mean (SD), years | 56.5 (±15.9) | 60.3 (±15.5) | 0.278 |
18–29 years | 72 (7.8%) | 2 (9.5%) | 0.221 |
30–39 years | 85 (9.2%) | 1 (4.8%) | |
40–49 years | 126 (13.6%) | 0 (0.0%) | |
50–59 years | 193 (20.9%) | 4 (19.0%) | |
60–69 years | 245 (26.5%) | 7 (33.3%) | |
70–79 years | 145 (16.7%) | 7 (33.3%) | |
>80 years | 49 (5.3%) | 0 (0.0%) | |
Body mass index, mean (SD), kg/m2 | 28.1 (±7.1) | 28.5 (±7.4) | 0.805 |
BMI < 30 | 628 (68.0%) | 12 (57.1%) | 0.444 |
Class 1 obesity | 165 (17.9%) | 5 (23.8%) | |
Class 2 obesity | 62 (6.7%) | 3 (14.3%) | |
Class 3 obesity | 69 (7.5%) | 1 (4.8%) | |
Fitzpatrick skin color scale | |||
1 | 18 (1.9%) | 0 (0.0%) | 0.975 |
2 | 492 (53.2%) | 12 (57.1%) | |
3 | 344 (37.2%) | 8 (38.1%) | |
4 | 57 (6.2%) | 1 (4.8%) | |
5 | 8 (0.9%) | 0 (0.0%) | |
6 | 5 (0.5%) | 0 (0.0%) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Allado, E.; Poussel, M.; Renno, J.; Moussu, A.; Hily, O.; Temperelli, M.; Albuisson, E.; Chenuel, B. Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. J. Clin. Med. 2022, 11, 3647. https://doi.org/10.3390/jcm11133647
Allado E, Poussel M, Renno J, Moussu A, Hily O, Temperelli M, Albuisson E, Chenuel B. Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. Journal of Clinical Medicine. 2022; 11(13):3647. https://doi.org/10.3390/jcm11133647
Chicago/Turabian StyleAllado, Edem, Mathias Poussel, Justine Renno, Anthony Moussu, Oriane Hily, Margaux Temperelli, Eliane Albuisson, and Bruno Chenuel. 2022. "Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial" Journal of Clinical Medicine 11, no. 13: 3647. https://doi.org/10.3390/jcm11133647
APA StyleAllado, E., Poussel, M., Renno, J., Moussu, A., Hily, O., Temperelli, M., Albuisson, E., & Chenuel, B. (2022). Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. Journal of Clinical Medicine, 11(13), 3647. https://doi.org/10.3390/jcm11133647