Acta Med. 2023, 66: 39-46

https://doi.org/10.14712/18059694.2023.14

Non-contact Vital Signs Monitoring in Paediatric Anaesthesia – Current Challenges and Future Direction

Nicole Grecha, Jean Calleja Agiusb, Stephen Sciberrasa, Neil Micallefc, Kenneth Camilleric, Owen Falzonc

aDepartment of Anaesthesia and Intensive Care Medicine, Mater Dei Hospital, Malta
bDepartment of Anatomy, Faculty of Medicine and Surgery, University of Malta
cCentre for Biomedical Cybernetics, Faculty of Engineering, University of Malta

Received January 21, 2023
Accepted May 8, 2023

References

1. Malasinghe LP, Ramzan N, Dahal, K. Remote patient monitoring: a comprehensive study. J Ambient Intell Humaniz Comput 2019; 10: 57–76. <https://doi.org/10.1007/s12652-017-0598-x>
2. WHO. Evidence of hand hygiene to reduce transmission and infections by multi- drug resistant organisms in health-care settings. http://whqlibdoc.who.int/publications, 2009.
3. Boric-Lubecke O, Lubecke VM, Droitcour AD, Park BK, Singh A. Doppler Radar Physiological Sensing. Wiley 2016.
4. Bella A, Latif R, Saddik A, Zahra Guerrouj F. Monitoring of Physiological Signs and Their Impact on The Covid-19 Pandemic: Review. E3S Web Conf 2021; 229: 01030. <https://doi.org/10.1051/e3sconf/202122901030>
5. Tsai C-Y, Chang N-C, Fang H-C, Chen Y-C, Lee S-S. A Novel Non-contact Self-Injection-Locked Radar for Vital Sign Sensing and Body Movement Monitoring in COVID-19 Isolation Ward. J Med Syst 2020; 44(10): 177. <https://doi.org/10.1007/s10916-020-01637-z> <PubMed>
6. Phua J, Weng L, Ling L, Egi M, Lim CM, Divatia JV, et al. Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir Med 2020; 8(5): 506–17. <https://doi.org/10.1016/S2213-2600(20)30161-2> <PubMed>
7. Chorney JML, Kain ZN. Behavioral analysis of children’s response to induction of anesthesia. Anesth Analg 2009; 109(5): 1434–40. <https://doi.org/10.1213/ane.0b013e3181b412cf>
8. Fortier MA, Del Rosario AM, Martin SR, Kain ZN. Perioperative anxiety in children. Paediatr Anaesth 2010; 20(4): 318–22. <https://doi.org/10.1111/j.1460-9592.2010.03263.x>
9. Watson AT, Visram A. Children’s preoperative anxiety and postoperative behaviour. Paediatr Anaesth 2003; 13(3): 188–204. <https://doi.org/10.1046/j.1460-9592.2003.00848.x>
10. Tan L, Frca BM, Meakin GH, Frca MD. Anaesthesia for the uncooperative child. Contin Educ Anaesth Crit Care Pain 2010; 10(2): 48–52. <https://doi.org/10.1093/bjaceaccp/mkq003>
11. Checketts MR, Alladi R, Ferguson K, Gemmell L, Handy JM, Klein AA, et al. Recommendations for standards of monitoring during anaesthesia and recovery 2015 : Association of Anaesthetists of Great Britain and Ireland. Anaesthesia 2016; 71(1): 85–93. <https://doi.org/10.1111/anae.13316> <PubMed>
12. Al-Khalidi FQ, Saatchi R, Burke D, Elphick H, Tan S. Respiration rate monitoring methods: A review. Pediatr Pulmonol 2011; 46(6): 523–9. <https://doi.org/10.1002/ppul.21416>
13. Mond HG, Haqqani HM. The Footprints of Electrocardiographic Interference: Fact or Artefact. Heart Lung Circ 2019; 28(10): 1472–83. <https://doi.org/10.1016/j.hlc.2019.03.006>
14. Takats Z, Strittmatter N, McKenzie JS. Ambient Mass Spectrometry in Cancer Research. Adv Cancer Res 2017; 134: 231–56. <https://doi.org/10.1016/bs.acr.2016.11.011>
15. Hewson DW, Hardman JG. Physical injuries during anaesthesia. BJA Educ 2018; 18(10): 310. <https://doi.org/10.1016/j.bjae.2018.06.003> <PubMed>
16. McDevitt WM, Farley M, Martin-Lamb D, Jones TJ, Morris KP, Seri S, Scholefield BR. Feasibility of non-invasive neuro-monitoring during extracorporeal membrane oxygenation in children. Perfusion 2023; 38(3): 547–56. <https://doi.org/10.1177/02676591211066804>
17. Vedrenne-Cloquet M, Lévy R, Chareyre J, Kossorotoff M, Oualha M, Renolleau S, Grimaud M. Association of Cerebral Oxymetry with Short-Term Outcome in Critically ill Children Undergoing Extracorporeal Membrane Oxygenation. Neurocrit Care 2021; 35(2): 409–17. <https://doi.org/10.1007/s12028-020-01179-9>
18. Allen, J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007; 28(3): R1-39. <https://doi.org/10.1088/0967-3334/28/3/R01>
19. Přibil J, Přibilová A, Frollo I. Comparative Measurement of the PPG Signal on Different Human Body Positions by Sensors Working in Reflection and Transmission Modes. Eng Proc 2020; 2(1): 69.
20. Wadhwa N, Wu HY, Davis A, Rubinstein M, Shih E, Mysore GJ, et al. Eulerian video magnification and analysis. Commun ACM 2017; 60(1): 87–95. <https://doi.org/10.1145/3015573>
21. Yang F, He S, Sadanand S, Yusuf A, Bolic M. Contactless Measurement of Vital Signs Using Thermal and RGB Cameras: A Study of COVID 19-Related Health Monitoring. Sensors (Basel) 2022; 22(2): 627. <https://doi.org/10.3390/s22020627> <PubMed>
22. Lauridsen H, Gonzales S, Hedwig D, Perrin KL, Williams CJA, Wrege PH, et al. Extracting physiological information in experimental biology via Eulerian video magnification. BMC Biol 2019; 17(1): 1–26. <https://doi.org/10.1186/s12915-019-0716-7> <PubMed>
23. A Step-by-Step Explanation of Principal Component Analysis (PCA). Built In [Internet]. Available from: https://builtin.com/data-science/step-step-explanation-principal-component-analysis
24. Independent Component Analysis (ICA) by Shawhin Talebi. Towards Data Science [Internet]. Available from: https://towardsdatascience.com/independent-component-analysis-ica-a3eba0ccec35
25. A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way by Sumit Saha. Towards Data Science [Internet]. Available from: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
26. Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. Proc 2017 Int Conf Eng Technol ICET 2017. 2018 Mar 8.
27. How Do Convolutional Layers Work in Deep Learning Neural Networks? [Internet]. Available from: https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/
28. Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci 2019; 50(4): 477–87. <https://doi.org/10.1016/j.jmir.2019.09.005>
29. Romano C, Schena E, Silvestri S, Massaroni C. Non-contact respiratory monitoring using an RGB camera for real-world applications. Sensors 2021; 21(15): 5126. <https://doi.org/10.3390/s21155126> <PubMed>
30. van Gastel M, Stuijk S, de Haan G. Robust respiration detection from remote photoplethysmography. Biomed Opt Express 2016; 7(12): 4941. <https://doi.org/10.1364/BOE.7.004941> <PubMed>
31. Valenzuela A, Sibuet N, Hornero G, Casas O. Non-contact video-based assessment of the respiratory function using a rgb-d camera. Sensors 2021; 21(16): 5605. <https://doi.org/10.3390/s21165605> <PubMed>
32. Turaga P, Chellappa R, Veeraraghavan A. Advances in Video-Based Human Activity Analysis: Challenges and Approaches. Adv Comput 2010; 80(C): 237–90. <https://doi.org/10.1016/S0065-2458(10)80007-5>
33. Brüser C, Antink CH, Wartzek T, Walter M, Leonhardt S. Ambient and unobtrusive cardiorespiratory monitoring techniques. IEEE Rev Biomed Eng 2015; 8: 30–43. <https://doi.org/10.1109/RBME.2015.2414661>
34. Li P, Benezeth Y, Nakamura K, Gomez R, Yang F. Model-based Region of Interest Segmentation for Remote Photoplethysmography. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019); 2019: 383–8. <https://doi.org/10.5220/0007389800002108>
35. Kossack B, Wisotzky E, Hilsmann A, Eisert P. Automatic region-based heart rate measurement using remote photoplethysmography. Proc IEEE Int Conf Comput Vis 2021: 2755–9.
36. Coppetti T, Brauchlin A, Mu S, Attinger-toller A, Templin C, Scho F, et al. Accuracy of smartphone apps for heart rate measurement. Eur J Prev Cardiol 2017; 24(12): 1287–93. <https://doi.org/10.1177/2047487317702044>
37. Shao D, Liu C, Tsow F, Yang Y, Du Z, Iriya R, et al. Noncontact Monitoring of Blood Oxygen Saturation Using Camera and Dual-Wavelength Imaging System. IEEE Trans Biomed Eng 2016; 63(6): 1091–8. <https://doi.org/10.1109/TBME.2015.2481896>
38. Mcduff D. Camera Measurement of Physiological Vital Signs. 2018.
39. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021; 372: n71 <https://doi.org/10.1136/bmj.n71> <PubMed>
40. Harford M, Catherall J, Gerry S, Young JD, Watkinson P. Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review. Physiol Meas 2019; 40(6): 06TR01. <https://doi.org/10.1088/1361-6579/ab1f1d>
41. Maurya L, Kaur P, Chawla D, Mahapatra P Non-contact breathing rate monitoring in newborns: A review. Comput Biol Med 2021; 132: 104321. <https://doi.org/10.1016/j.compbiomed.2021.104321>
42. Cobos-Torres J-C, Abderrahim M, Martínez-Orgado J. Non-Contact, Simple Neonatal Monitoring by Photoplethysmography. Sensors 2018; 18(12): 4362. <https://doi.org/10.3390/s18124362> <PubMed>
43. Chaichulee S, Villarroel M, Arteta C, Mccormick K. Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning. Physiol Meas 2019; 40(11): 115001. <https://doi.org/10.1088/1361-6579/ab525c> <PubMed>
44. Wieler ME, Murphy TG, Blecherman M, Mehta H, Bender GJ. Infant heart-rate measurement and oxygen desaturation detection with a digital video camera using imaging photoplethysmography. J Perinatol 2021; 41(7): 1725–31. <https://doi.org/10.1038/s41372-021-00967-1>
45. Lorato I, Stuijk S, Meftah M, Kommers D, Andriessen P, van Pul C, et al. Towards continuous camera-based respiration monitoring in infants. Sensors 2021; 21(7): 1–18.
46. Lucy FK, Suha KT, Dipty ST, Wadud SI, Kadir MA. Video based non-contact monitoring of respiratory rate and chest indrawing in children with pneumonia. Physiol Meas 2021; 42(10). <https://doi.org/10.1088/1361-6579/ac34eb>
47. Nagy Á, Földesy P, Jánoki I, Terbe D, Siket M, Szabó M, et al. Continuous camera-based premature-infant monitoring algorithms for nicu. Appl Sci 2021; 11(16): 7215. <https://doi.org/10.3390/app11167215>
48. Sun Y, Wang W, Long X, Meftah M, Tan T, Shan C, et al. applied sciences Respiration Monitoring for Premature Neonates in NICU. Appl Sci 2019; 9(23): 5246. <https://doi.org/10.3390/app9235246>
49. Gibson K, Al-Naji, A, Fleet, J, Steen, M, Esterman, A, Chahl, J, Huynh, J, Morris, S. Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study. Pediatr Res 2019; 86: 738–41. <https://doi.org/10.1038/s41390-019-0506-5>
50. Villarroel M, Chaichulee S, Jorge J, Davis S, Green G, Arteta C, et al. Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit. npj Digit Med 2019; 128: 1–18.
51. Paul M, Karthik S, Joseph J, Sivaprakasam M, Kumutha J, Leonhardt S, et al. Non-contact sensing of neonatal pulse rate using camera-based imaging: A clinical feasibility study. Physiol Meas 2020; 41(2): 024001. <https://doi.org/10.1088/1361-6579/ab755c>
52. Paul M, Behr SC, Weiss C, Heimann K, Orlikowsky T, Leonhardt S. Spatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermograph. Biomed Eng Online 2021; 20(1): 1–54. <https://doi.org/10.1186/s12938-020-00841-9> <PubMed>
53. Rossol SL, Yang JK, Toney-Noland C, Bergin J, Basavaraju C, Kumar P, et al. Non-Contact Video-Based Neonatal Respiratory Monitoring. Children 2020; 7(10): 171. <https://doi.org/10.3390/children7100171> <PubMed>
54. Chen Q, Jiang X, Liu X, Lu C, Wang L, Chen W. Non-Contact Heart Rate Monitoring in Neonatal Intensive Care Unit using RGB Camera. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers Inc.; 2020: 5822–5.
55. Khanam FTZ, Perera AG, Al-Naji A, Gibson K, Chahl J. Non-contact automatic vital signs monitoring of infants in a neonatal intensive care unit based on neural networks. J Imaging 2021; 7(8): 122. <https://doi.org/10.3390/jimaging7080122> <PubMed>
56. Infants (0–1 years). CDC [Internet]. Available from: https://www.cdc.gov/ncbddd/childdevelopment/positiveparenting/infants.html
57. Buhre W, Rossaint R. Perioperative management and monitoring in anaesthesia. Lancet 2003; 362(9398): 1839–46. <https://doi.org/10.1016/S0140-6736(03)14905-7>
58. Eddahchouri Y, Peelen R V., Koeneman M, Touw HRW, van Goor H, Bredie SJH. Effect of continuous wireless vital sign monitoring on unplanned ICU admissions and rapid response team calls: a before- and-after study. Br J Anaesth 2022; 128(5): 857–63. <https://doi.org/10.1016/j.bja.2022.01.036>
59. Lin YC, Lin YH. A study of color illumination effect on the SNR of rPPG signals. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2017 Sep 13: 4301–4.
60. Nowara EM, Mcduff D, Veeraraghavan A. A Meta-Analysis of the Impact of Skin Type and Gender on Non-contact Photoplethysmography Measurements. Available from: http://data.un.org/
61. Hall T, Lie DYC, Nguyen TQ, Mayeda JC, Lie PE, Lopez J, et al. Non-contact sensor for long-term continuous vital signs monitoring: A review on intelligent phased-array doppler sensor design. Sensors (Basel) 2017; 17(1): 2632. <https://doi.org/10.3390/s17112632> <PubMed>
62. Barbosa Pereira C, Yu X, Czaplik M, Blazek V, Venema B, Leonhardt S. Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects. J Clin Monit Comput 2017; 31(6): 1241–54. <https://doi.org/10.1007/s10877-016-9949-y>
63. Choi P, Walker R. Remote Patient Management: Balancing Patient Privacy, Data Security, and Clinical Needs. Contrib Nephrol 2019; 197: 35–43. <https://doi.org/10.1159/000496312>
64. Mondschein CF, Monda C. The EU’s General Data Protection Regulation (GDPR) in a Research Context. Fundam Clin Data Sci 2018: 55–71. Available from: https://www.ncbi.nlm.nih.gov/books/NBK 543521/
65. Lepola P, Needham A, Mendum J, Sallabank P, Neubauer D, De Wildt S. Informed consent for paediatric clinical trials in Europe. Arch Dis Child 2016; 101(11): 1017–25. <https://doi.org/10.1136/archdischild-2015-310001> <PubMed>
66. Gillick competence and Fraser guidelines. NSPCC Learning [Internet]. Available from: https://learning.nspcc.org.uk/child-protection-system/gillick-competence-fraser-guidelines
front cover

ISSN 1211-4286 (Print) ISSN 1805-9694 (Online)

Open access journal

Archive