Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)


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Volkov I. Y., Sagaidachnyi A. A., Fomin A. V. Photoplethysmographic imaging of hemodynamics and two-dimensional oximetry. Izvestiya of Sarat. Univ. Physics. , 2022, vol. 22, iss. 1, pp. 15-45. DOI: 10.18500/1817-3020-2022-22-1-15-45

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.03.2022
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(downloads: 70)
Language: 
Russian
Article type: 
Article
UDC: 
57.087.3:612.1

Photoplethysmographic imaging of hemodynamics and two-dimensional oximetry

Autors: 
Volkov Ivan Yu., Saratov State University
Sagaidachnyi Andrey Aleksandrovich, Saratov State University
Fomin Andrey Vladimirovich, Saratov State University
Abstract: 

Background and Objectives: A review of recent papers devoted to actively developing methods of photoplethysmographic imaging (PPGI) of blood volume pulsations in vessels and non-contact two-dimensional oximetry on the surface of the human body is carried out. Results: The physical fundamentals and technical aspects of PPGI and oximetry have been considered. The diversity of physiological parameters available for analysis by PPGI has been shown. The prospects of PPGI technology have been discussed. The possibilities of non-contact determination of blood oxygen saturation SpO2 (saturation of pulse O2) have been described. The relevance of remote determination of the level of oxygenation due to the spread of a new coronavirus infection SARS-CoV-2 (COVID-19) has been emphasized. Most of the works under consideration cover the period of 2010–2021 years. 

Acknowledgments: 
A review of the possibilities of determining oxygenation based on photoplethysmographic imaging technology was funded by the Grant Council of the President of the Russian Federation for the state support of young Russian scientists – candidates of sciences (project No. MK-140.2021.4); a review ofthe possibilitiesfor verifyingthermal imaging data by photoplethysmographic imaging wasfunded bythe Russian Science Foundation (project No. 21-75-00035).
Reference: 
  1. Strokanev K. S. Review and Classification of Current Methods for Remote Photoplethysmography of the Face. Intellektual’nye sistemy v proizvodstve [Intelligent Systems in Manufacturing], 2021, vol. 19, no. 2, pp. 129–138 (in Russian). https://www.doi.org/10.22213/2410-9304-2021-2-129-138
  2. Hertzman A. B. The blood supply of various skin areas as estimated by the photoelectric plethysmograph. Am. J. Physiol., 1938, vol. 124, no. 2, pp. 328–340. https://www.doi.org/10.1152/ajplegacy.1938.124.2.328
  3. De Trafford J., Lafferty K. What does photoplethysmography measure? Medical & Biological Engineering & Computing, 1984, vol. 22, no. 5, pp. 479–480. https://www.doi.org/10.1007/BF02447713
  4. Sun Y. Photoplethysmography revisited : From contact to noncontact, from point to imaging. IEEE Transactions on Biomedical Engineering, 2015, vol. 63, no. 3, pp. 463–477. https://www.doi.org/10.1109/TBME.2015.2476337
  5. Aoyagi T., Kishi M., Yamaguchi K., Watanabe S. Improvement of the earpiece oximeter. Japanese Society of Medical Electronics and Biological Engineering, 1974, vol. 974, pp. 90–91.
  6. Tremper K. K., Barker S. J. Pulse oximetry. Anesthesiology, 1989, vol. 70, no. 1, pp. 98–108. https://www.doi.org/10.1097/00000542-198901000-00019 
  7. Blazek V., Rutten W., Such O. A method for spaceresolved, noncontacting and functional visualization of dermal perfusion. German patent, no. P196 38 873.2, 1996.
  8. Wu T., Blazek V., Schmitt H. J. Photoplethysmography imaging : A new noninvasive and non-contact method for mapping of the dermal perfusion changes. Proc. SPIE, 2000, no. 4163, pp. 62–70. https://www.doi.org/10.1117/12.407646
  9. Takano C., Ohta Y. Heart rate measurement based on a time-lapse image. Med. Eng. Phys., 2007, vol. 29, no. 8, pp. 853–857. https://www.doi.org/10.1016/j.medengphy.2006.09.006
  10. Verkruysse W., Svaasand L., Nelson J. Remote plethysmographic imaging using ambient light. Optics Express, 2008, vol. 16, no. 26, pp. 21434–21445. https://www.doi.org/10.1364/oe.16.021434
  11. Kamshilin A. A., Miridonov S., Teplov V., Saarenheimo R., Nippolainen E. Photoplethysmographic imaging of high spatial resolution. Biomedical Optics Express, 2011, vol. 2, no. 4, pp. 996–1006. https://www.doi.org/10.1364/BOE.2.000996
  12. Zaproudina N., Teplov V., Nippolainen E., Lipponen J. A., Kamshilin A. A., Närhi M., Giniatullin R. Asynchronicity of facial blood perfusion in migraine. PloS ONE, 2013, vol. 8, no. 12, e80189. https://www.doi.org/10.1371/journal.pone.0080189
  13. Wieringa F., Mastik F., van der Steen A. Contactless multiple wavelength photoplethysmographic imaging : A first step toward «SpO2 camera» technology. Ann. Biomed. Eng., 2005, vol. 33, no. 8, pp. 1034–1041. https://www.doi.org/10.1007/s10439-005-5763-2
  14. Wieringa F., Mastik F. In Vitro Demonstration of an SpO2-Camera. Computers in Cardiology, 2007, no. 34, pp. 749–751.
  15. Simonyan M. A., Posnenkova O. M., Kiselev A. R. Capabilities of photoplethysmography as a method for screening of cardiovascular system pathology. Cardio-IT, 2020, vol. 7, no. 1, pp. 102 (in Russian). https://www.doi.org/10.15275/cardioit.2020.0102
  16. Semchuk I. P., Zmievskoi G. N., Muravskaia N. P., Samorodov A. V. Non-contact photoplethysmograph for the study of vital body functions. Pribory [Instruments], 2018, vol. 217, no. 7, pp. 1–6 (in Russian).
  17. Akishin A. D., Semchuk I. P., Nikolaev A. P. Development of a device for control of the state of the organism based on photopletismography using technologies of digital adaptive filtration. System Analysis and Management in Biomedical Systems, 2020, vol. 19, no. 4, pp. 100–107 (in Russian). https://www.doi.org/10.36622/VSTU.2020.19.4.0124
  18. Avanesov A. A., Kopeliovich M. V., Kalinin K. B., Shcherban I. V. Approaches to evaluating the frequency of heart reductions by video recording analysis. Trudy Severo-Kavkazskogo filiala Moskovskogo tekhnicheskogo universiteta sviazi i informatiki [Proceedings of the North Caucasian branch of the Moscow Technical University of Communications and Informatics], 2020, no. 1, pp. 27–40 (in Russian).
  19. Semchuk I. P., Zmievskoy G. N., Muravskaya N. P., Volkov A. K., Murashko M. A., Samorodov A. V. An experimental study of contactless photoplethysmography techniques. Meditsinskaya tekhnika [Biomedical Engineering], 2019, no. 1, pp. 1–4 (in Russian).
  20. Kamshilin A. A., Sidorov I. S., Babayan L., Volynsky M. A., Giniatullin R., Mamontov O. V. Accurate measurement of the pulse wave delay with imaging photoplethysmography. Biomedical Optics Express, 2016, vol. 7, no. 12, pp. 5138–5147. https://www.doi.org/10.1364/BOE.7.005138
  21. Kamshilin A. A., Krasnikova T. V., Volynsky M. A., Miridonov S. V., Mamontov O. V. Alterations of blood pulsations parameters in carotid basin due to body position change. Scientific Reports, 2018, vol. 8, no. 1, pp. 1–9. https://www.doi.org/10.1038/s41598-018-32036-7
  22. Chatterjee S., Phillips J. P., Kyriacou P. A. Monte carlo investigation of the effect of blood volume and oxygen saturation on optical path in reflectance pulse oximetry. Biomedical Physics & Engineering Express, 2016, vol. 2, no. 6, e065018. https://www.doi.org/10.1088/2057-1976/2/6/065018
  23. Budidha K., Kyriacou P. A., Abay T. Y. Optical techniques for blood and tissue oxygenation. Ref. Modul. Biomed. Sci., 2019, vol. 3, pp. 461–472. https://www.doi.org/10.1016/B978-0-12-801238-3.10886-4
  24. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 2007, vol. 28, no. 3, pp. R1–39. https://www.doi.org/10.1088/0967-3334/28/3/R01
  25. Gastel M., Stuijk S., Haan G. Camera-based pulseoximetry – validated risks and opportunities from theoretical analysis. Biomedical Optics Express, 2018, vol. 9, no. 1, pp. 102–119. https://www.doi.org/10.1364/BOE.9.000102
  26. Gastel M., Stuijk S., Haan G. New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring. Scientific Reports, 2016, vol. 6, no. 1, e38609. https://www.doi.org/10.1038/srep38609
  27. Nitzan M., Adar Y. Comparison of systolic blood pressure values obtained by photoplethysmography and by korotkoff sounds. Sensors, 2013, vol. 13, no. 11, pp. 14797–14812. https://www.doi.org/10.3390/s131114797
  28. Aoyagi T. Pulse oximetry : Its invention, theory, and future. Journal of Anesthesia, 2003, vol. 17, no. 4, pp. 259–266. https://www.doi.org/10.1007/s00540-003-0192-6
  29. Lapitan D. G., Tarasov A. P. Analytical assessment of the modulation depth of photoplethysmographic signal based on the modified Beer-Lambert law. 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL), 2019, pp. 103–106. https://www.doi.org/10.1109/CAOL46282.2019.9019552  
  30. Moço A. V., Stuijk S., de Haan G. Skin inhomogeneity as a source of error in remote PPG-imaging. Biomedical Optics Express, 2016, vol. 7, no. 11, pp. 4718–4733. https://www.doi.org/10.1364/BOE.7.004718
  31. Fine I. The optical origin of the PPG signal. Saratov Fall Meeting 2013 : Optical Technologies in Biophysics and Medicine XV. 2014, no. 9031, e903103. https://www.doi.org/10.1117/12.2051228
  32. Kamshilin A. A., Nippolainen E., Sidorov I. S., Vasilev P. V., Erofeev N. P., Podolian N. P., Romashko R. V. A new look at the essence of the imaging photoplethysmography. Scientific Reports, 2015, vol. 5, no. 1, pp. 10494. https://www.doi.org/10.1038/srep10494
  33. Moço A. V., Stuijk S., de Haan, G. Motion robust PPG-imaging through color channel mapping. Biomedical Optics Express, 2016, vol. 7, no. 5, pp. 1737–1754. https://www.doi.org/10.1364/BOE.7.001737
  34. Sidorov I. S., Romashko R. V., Koval V. T., Giniatullin R., Kamshilin A. A. Origin of infrared light modulation in reflectance mode photoplethysmography. PLoS ONE, 2016, vol. 11, no. 10, e0165413. https://www.doi.org/10.1371/journal.pone.0165413
  35. Farrell T. J., Patterson M. S., Wilson B. A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Medical Physics, 1992, vol. 19, no. 4, pp. 879–888. https://www.doi.org/10.1118/1.596777
  36. Rogatkin D. A. Physical foundations of optical oximetry. Meditsinskaya fizika [Medical Physics], 2012, vol. 2, no. 54, pp. 97–113 (in Russian).
  37. Marcinkevics Z., Rubins U. Imaging photoplethysmography for clinical assessment of cutaneous microcirculation at two different depths. Journal of Biomedical Optics, 2016, vol. 21, no. 3, e35005. https://www.doi.org/10.1117/1.JBO.21.3.035005
  38. Verkruysse W., Bartula M. Calibration of contactless pulse oximetry. Anesthesia and Analgesia, 2017, vol. 124, no. 1, pp. 136–145. https://www.doi.org/10.1213/ANE.0000000000001381
  39. Rubins U., Erts R., Nikiforovs V. The blood perfusion mapping in the human skin by photoplethysmography imaging. IFMBE Proceedings, 2010, vol. 29, pp. 304–306. https://www.doi.org/10.1007/978-3-642-13039-7_76
  40. Sun Y., Hu S., Azorin-Peris V. Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise. Journal of Biomedical Optics, 2011, vol. 16, no. 7, e077010. https://www.doi.org/10.1117/1.3602852
  41. Zheng J., Hu S., Azorin-Peris V. Remote simultaneous dual wavelength imaging photoplethysmography : A further step towards 3-D mapping of skin blood microcirculation. Proc. of SPIE, 2008, vol. 6850, e68500S. https://www.doi.org/10.1117/12.761705
  42. Trumpp A., Bauer P. L. The value of polarization in camera-based photoplethysmography. Biomedical Optics Express, 2017, vol. 8, no. 6, pp. 2822–2834. https://www.doi.org/10.1364/BOE.8.002822
  43. Bousefsaf F., Maaoui C., Pruski A. Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate. Biomedical Signal Processing and Control, 2013, vol. 8, no. 6, pp. 568–574. https://www.doi.org/10.1016/j.bspc.~2013.05.010
  44. Taranov A. A., Spiridonov I. N. Non-contact measurement of arterial pulse rate. Biotekhnosfera [Biotechnosfera], 2014, vol. 3, no. 33, pp. 43–45 (in Russian).
  45. 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 lowcost webcam. Journal of Biomedical Optics, 2012, vol. 17, no. 3, e037005. https://www.doi.org/10.1117/1.JBO.17.3.037005
  46. Mironenko Y., Kalinin K., Kopeliovich M., Petrushan M. Remote photoplethysmography : Rarely considered factors. Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 296–297.
  47. Humphreys K., Ward T., Markham C. A CMOS Camera-Based Pulse Oximetry Imaging System. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, no. 2005, pp. 3494–3497. https://www.doi.org/10.1109/IEMBS.2005.1617232
  48. Hsu Y., Lin Y.-L., Hsu W. Learning-based heart rate detection from remote photoplethysmography features. 2014 IEEE Int. Conf. Acoust. Speech Signal Process, 2014, pp. 4433–4437. https://www.doi.org/10.1109/ICASSP.~2014.6854440
  49. Poh M.-Z., McDuff D. J., Picard R. W. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transactions on Biomedical Engineering, 2011, vol. 58, no. 1, pp. 7–11. https://www.doi.org/10.1109/TBME.2010.2086456
  50. Fallet S., Moser V. Imaging Photoplethysmography : What are the Best Locations on the Face to Estimate Heart Rate, Computing in Cardiology, 2016, vol. 43, pp. 341–344. https://www.doi.org/10.22489/Cinc.~2016.098-236
  51. Kumar M., Veeraraghavan A. Contact-free camera measurements of vital signs. SPIE, 2015, pp. 1–4. https://www.doi.org/10.1117/2.1201511.006184
  52. Shao D., Liu C., Tsow F., Yang Y., Du Z., Iriya R., Yu H., Tao N. Noncontact monitoring of blood oxygen saturation using camera and dual-wavelength imaging system. IEEE Transactions on Biomedical Engineering, 2016, vol. 63, no. 6, pp. 1091–1098. https://www.doi.org/10.1109/TBME.2015.2481896
  53. Feng L., Po L. M., Xu X., Li Y., Ma R. Motion-resistant remote imaging photoplethysmography based on the optical properties of skin. IEEE Transactions on Circuits and Systems for Video Technology, 2015, vol. 25, no. 5, pp. 879–891. https://www.doi.org/10.1109/TCSVT.2014.2364415
  54. Zou J., Chen T., Yang X. Non-Contact Real-Time Heart Rate Measurement Algorithm Based on PPG-Standard Deviation. Computers, Materials & Continua, 2019, vol. 60, no. 3, pp. 1029–1040. https://www.doi.org/10.32604/cmc.~2019.05793
  55. Trumpp A., Schell J. Vasomotor assessment by camera-based photoplethysmography. Current Directions in Biomedical Engineering, 2016, vol. 2, no. 1, pp. 199–202. https://www.doi.org/10.1515/cdbme-2016-0045
  56. Poh M.-Z., McDuff D. J., Picard R. W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express, 2010, vol. 18, no. 10, pp. 10762–10774. https://www.doi.org/10.1364/OE.18.010762
  57. Tulyakov S., Alameda-Pineda X., Ricci E., Yin L., Cohn J. F., Sebe N. Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2396–2404.
  58. Bousefsaf F., Maaoui C., Pruski A. Automatic selection of webcam photoplethysmographic pixels based on lightness criteria. J. Med. Biol. Eng., 2017, vol. 37, no. 3, pp. 374–385. https://www.doi.org/10.1007/s40846-017-0229-1
  59. Bousefsaf F., Maaoui C., Pruski A. Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate. Biomed. Signal Process. Control, 2013, vol. 8, no. 6, pp. 568–574. https://www.doi.org/10.1016/j.bspc.2013.05.010
  60. Bobbia S., Macwan R., Benezeth Y., Mansouri A., Dubois J. Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognit Lett., 2017, vol. 124, no. 9, pp. 1–9. https://www.doi.org/10.1016/j.patrec.2017.10.017
  61. Bobbia S., Luguern D., Benezeth Y., Nakamura K., Gomez R., Dubois J. Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography. Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1341–1348. https://www.doi.org/10.1109/CVPRW.2018.00182
  62. Bobbia S., Benezeth Y., Dubois J. Remote photoplethysmography based on implicit living skin tissue segmentation. Proc. of the 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 361–365.
  63. Wang W., Stuijk S., de Haan G. Living-skin classification via remote-ppg. IEEE Transactions on Biomedical Engineering, 2017, vol. 64, no. 12, pp. 2781–2792. https://www.doi.org/10.1109/TBME.2017.2676160
  64. Tarassenko L., Villarroel M., Guazzi A., Jorge J., Clifton D., Pugh C. Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiological Measurement, 2014, vol. 35, no. 5, pp. 807–831. https://www.doi.org/10.1088/0967-3334/35/5/807
  65. Chwyl B., Chung A. G., Amelard R., Deglint J., Clausi D. A., Wong A. SAPPHIRE : Stochastically ac[1]quired photoplethysmogram for heart rate inference in realistic environments. Proc. of the IEEE International Conference on Image Processing (ICIP), 2016, no. 2016, pp. 1230–1234. https://www.doi.org/10.1109/ICIP.~2016.7532554
  66. Chaichulee S., Villarroel M., Jorge J., Arteta C., Green G., McCormick K., Zisserman A., Tarassenko L. Multi-Task Convolutional Neural Network for Patient Detection and Skin Segmentation in Continuous Non-Contact Vital Sign Monitoring. Proc. of the 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 266–272. https://www.doi.org/10.1109/FG.2017.41
  67. Hu S., Peris V., Echiadis A., Zheng J., Shi P. Development of effective photoplethysmographic measurement techniques : From contact to non-contact and from point to imaging. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 6550–6553. https://www.doi.org/10.1109/IEMBS.2009.5334505
  68. Villarroel M., Guazzi A. Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthcare Technology Letters, 2014, vol. 1, no. 3, pp. 87–91. https://www.doi.org/10.1049/htl.2014.0077
  69. Wang W., Brinker A. C. D., Stuijk S., Haan G. D. Algorithmic principles of remote PPG. IEEE Trans. Biomed. Eng., 2017, vol. 64, no. 7, pp. 1479–1491. https://www.doi.org/10.1109/TBME.2016.2609282  
  70. Li X., Chen J., Zhao G., Pietikainen M. Remote heart rate measurement from face videos under realistic situations. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264–4271. https://www.doi.org/10.1109/CVPR.2014.543
  71. Strokanev K. S., Korobeynikov A. V. System of photoplethysmography by face video image with applying the euler magnification. Intellektual’nye sistemy v proizvodstve [Intelligent Systems in Manufacturing], 2016, vol. 3, no. 30, pp. 56–59 (in Russian).
  72. Wu H.-Y., Rubinstein M., Shih E., Guttag J. V., Durand F., Freeman W. T. Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph, 2012, vol. 31, no. 4, pp. 65. https://www.doi.org/10.1145/2185520.2185561
  73. Lewandowska M., Ruminski J., Kocejko T., Nowak J. Measuring pulse rate with a webcam – a non-contact method for evaluating cardiac activity. 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), 2011, pp. 405–410.
  74. De Haan G., Jeanne V. Robust pulse rate from chrominance-based rppg. IEEE Transactions on Biomedical Engineering, 2013, vol. 60, no. 10, pp. 2878–2886. https://www.doi.org/10.1109/TBME.2013.2266196
  75. De Haan G., Van Leest A. Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiological Measurement, 2014, vol. 35, no. 9, pp. 1913–1926. https://www.doi.org/10.1088/0967-3334/35/9/1913
  76. Wang W., Stuijk S., De Haan G. A Novel Algorithm for Remote Photoplethysmography : Spatial Subspace Rotation. IEEE Transactions on Biomedical Engineering, 2016, vol. 63, no. 9, pp. 1974–1984. https://www.doi.org/10.1109/TBME.2015.2508602
  77. Hsu G., Ambikapathi A., Chen M. Deep learning with time-frequency representation for pulse estimation from facial videos. IEEE International Joint Conference on Biometrics (IJCB), 2017, pp. 383–389. https://www.doi.org/10.1109/BTAS.2017.8272721
  78. Chen W., McDuff D. DeepPhys : Videobased physiological measurement using convolutional attention networks. European Conference on Computer Vision (ECCV), 2018, pp. 356–373.
  79. Niu X., Shan S., Han H., Chen X. RhythmNet : End-to-end heart rate estimation from face via spatial-temporal representation. IEEE Transactions on Image Processing, 2020, no. 29, pp. 2409–2423. https://www.doi.org/10.1109/TIP.~2019.2947204
  80. Yu Z., Li X., Zhao G. Remote photoplethysmography signal measurement from facial videos using spatio-temporal networks. Proceedings of the British Machine Vision Conference (BMVC), 2019, pp. 1–12.
  81. Heusch G., Marcel S. Pulse-based features for face presentation attack detection. IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018, pp. 1–8. https://www.doi.org/10.1109/BTAS.2018.8698579
  82. Liu S.-Q., Lan X., Yuen P. Remote photoplethysmography correspondence feature for 3d mask face presentation attack detection. European Conference on Computer Vision (ECCV), 2018, pp. 577–594. https://www.doi.org/10.1007/978-3-030-01270-0_34
  83. Speth J., Vance N., Flynn P., Bowyer K., Czajka A. Remote Pulse Estimation in the Presence of Face Masks. 2021. ArXiv preprint arXiv:2101.04096.
  84. Mannapperuma K., Holton B. D. Performance limits of ICA-based heart rate identification techniques in imaging photoplethysmography. Physiological Measurement, 2015, vol. 36, no. 1, pp. 67–83. https://www.doi.org/10.1088/0967-3334/36/1/67
  85. Forrester K. R., Tulip J., Leonard C., Stewart C., Bray R. C. A laser speckle imaging technique for measuring tissue perfusion. IEEE Transactions on Biomedical Engineering, 2004, vol. 51, no. 11, pp. 2074–2084. https://www.doi.org/10.1109/TBME.2004.834259
  86. Serov A., Steinacher B., Lasser T. Full-field laser Doppler perfusion imaging and monitoring with an intelligent CMOS camera. Optics Express, 2005, vol. 13, no. 10, pp. 3681–3689. https://www.doi.org/10.1364/opex.13.003681
  87. Kamshilin A. A., Teplov V., Nippolainen E., Miridonov S., Giniatullin R. Variability of microcirculation detected by blood pulsation imaging. PloS ONE, 2013, vol. 8, no. 2, e57117. https://www.doi.org/10.1371/journal.pone.0057117
  88. Iakovlev D., Dwyer V., Hu S., Silberschmidt V. Noncontact blood perfusion mapping in clinical applications. Biophotonics : Photonic Solutions for Better Health Care V, 2016, vol. 9887, pp. 55–56. https://www.doi.org/10.1117/12.2225216
  89. Aarts L. A., Jeanne V., Cleary J. P., Lieber C., Nelson J. S., Oetomo S. B., Verkruysse W. Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit – a pilot study. Early Hum. Dev., 2013, vol. 89, no. 12, pp. 943–948. https://www.doi.org/10.1016/j.earlhumdev.2013.09.016
  90. Kumar M., Suliburk J. PulseCam : High-resolution blood perfusion imaging using a camera and a pulse oximeter. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2016, pp. 3904–3909. https://www.doi.org/10.1109/EMBC.2016.7591581
  91. Kumar M., Suliburk J. PulseCam : A camera-based, motion-robust and highly sensitive blood perfusion imaging modality. Scientific Reports, 2020, vol. 10, no. 1, pp. 1–17 https://www.doi.org/10.1038/s41598-020-61576-0
  92. Mamontov O. V., Krasnikova T. V., Volynsky M. A., Anokhina N. A., Shlyakhto E. V., Kamshilin A. A. Novel instrumental markers of proximal scleroderma provided by imaging photoplethysmography. Physiological Measurement, 2020, vol. 41, no. 4, e044004. https://www.doi.org/10.1088/1361-6579/ab807c  
  93. Volynsky M. A., Margaryants N. B., Kamshilin A. A. Monitoring Changes in Capillary Blood Flow due to Thermal Impact Using Imaging Photoplethysmography. Imaging and Applied Optics, 2019, paper ITh3B.4. https://www.doi.org/10.1364/ISA.2019.ITh3B.4
  94. Kamshilin A. A., Lyubashina O. A. Assessment of Pain-Induced Changes in Cerebral Microcirculation by Imaging Photoplethysmography. International Work-Conference on Bioinformatics and Biomedical Engineering. Cham, Springer, 2019, pp. 479–489. https://www.doi.org/10.1007/978-3-030-17935-9_43
  95. Mamontov O. V., Shcherbinin A. V. Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light. Applied Sciences, 2020, vol. 10, no. 18, e6192. https://www.doi.org/10.3390/app10186192
  96. Kamshilin A. A., Volynsky M. A. Novel capsaicininduced parameters of microcirculation in migraine patients revealed by imaging photoplethysmography. The Journal of Headache and Pain, 2018, vol. 19, no. 1, pp. 43. https://www.doi.org/10.1186/s10194-018-0872-0
  97. Volynsky M. A., Mamontov O. V. Pulse wave transit time measured by imaging photoplethysmography in upper extremities. Journal of Physics : Conference Series, 2016, vol. 737, e012053. https://www.doi.org/10.1088/1742-6596/737/1/012053
  98. Nirala N., Periyasamy R., Kumar A. Study of skin flow motion pattern using photoplethysmogram. International Journal of Advanced Intelligence Paradigms, 2020, vol. 16, no. 3–4, pp. 241–264. https://www.doi.org/10.1504/IJAIP.~2020.10018682
  99. Blanik N., Blazek C., Pereira C., Blazek V., Leonhardt S. Frequency-selective quantification of skin perfusion behavior during allergic testing using photoplethysmography imaging. Medical Imaging 2014 : Image Processing, 2014, vol. 9034, e903429. https://www.doi.org/10.1117/12.2043567
  100. Allen J., Chen F. Low-frequency variability in photoplethysmography and autonomic function assessment. In: Photoplethysmography. Academic Press, 2022, pp. 277–318. https://www.doi.org/10.1016/B978-0-12-823374-0.00008-6
  101. Nishidate I., Hoshi A., Aoki Y., Nakano K., Niizeki K., Aizu Y. Noncontact imaging of plethysmographic pulsation and spontaneous low-frequency oscillation in skin perfusion with a digital red-green-blue camera. Dynamics and Fluctuations in Biomedical Photonics XIII, 2016, vol. 9707, e97070L. https://www.doi.org/10.1117/12.2212558
  102. Nishidate I., Tanabe C., McDuff D. J., Nakano K., Niizeki K., Aizu Y., Haneishi H. RGB camera-based noncontact imaging of plethysmogram and spontaneous low-frequency oscillation in skin perfusion before and during psychological stress. Proc. SPIE. Optical Diagnostics and Sensing XIX : Toward Point-of-Care Diagnostics, 2019, vol. 10885, pp. 9–16. https://www.doi.org/10.1117/12.2509752  
  103. McDuff D., Nishidate I., Nakano K., Haneishi H., Aoki Y., Tanabe C., Aizu Y. Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors. Scientific Reports, 2020, vol. 10, no. 1, pp. 1–13. https://www.doi.org/10.1038/s41598-020-67647-6
  104. Khanoka B., Slovik Y., Landau D., Nitzan M. Sympathetically induced spontaneous fluctuations of the photoplethysmographic signal. Medical and Biological Engineering and Computing, 2004, vol. 42, no. 1, pp. 80–85. https://www.doi.org/10.1007/BF02351014
  105. Kublanov V. S., Purtov K. S. Heart rate variability study by remote photoplethysmography. Biomedical Radioelectronics, 2015, no. 8, pp. 3–9 (in Russian).
  106. Kublanov V. S., Purtov K. S. Researching the possibilities of remote photoplethysmography application to analysis of time-frequency changes of human heart rate variability. 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON), 2015, pp. 87–92. https://www.doi.org/10.1109/SIBIRCON.2015.7361857
  107. Kulminskiy D. D., Kurbako A. V., Skazkina V. V., Prokhorov M. D., Ponomarenko V. I., Kiselev A. R., Bezruchko B. P., Karavaev A. S. Development of a digital finger photoplethysmogram sensor. Izvestiya of Saratov University. Physics, 2021, vol. 21, iss. 1, pp. 58–68 (in Russian). https://www.doi.org/10.18500/1817-3020-2021-21-1-58-68
  108. Simonyan M. A., Skazkina V. V., Posnenkova O. M., Ishbulatov Yu. M, Shvartz V. A., Borovkova E. I., Gorshkov A. Yu., Fedorovich A. A., Dzhioeva O. N., Karavaev A. S., Gridnev V. I., Drapkina O. M., Kiselev A. R. Analysis of the spectral indices of the photoplethysmographic signals and their age-related dynamics for the task of screening of cardiovascular diseases. Profilakticheskaya meditsina [The Russian Journal of Preventive Medicine], 2021, vol. 24, no. 8, pp. 73–79 (in Russian). https://www.doi.org/10.17116/profmed20212408173
  109. Karavaev A. S., Borovik A. S., Borovkova E. I., Orlova E. A., Simonyan M. A., Ponomarenko V. I., Kiselev A. R. Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure. Biophysical Journal, 2021, vol. 120, no. 13, pp. 2657–2664. https://www.doi.org/10.1016/j.bpj.2021.05.020
  110. Kiselev A. R., Borovkova E. I., Shvartz V. A., Skazkina V. V., Karavaev A. S., Prokhorov M. D., Bockeria O. L. Low-frequency variability in photoplethysmographic waveform and heart rate during on-pump cardiac surgery with or without cardioplegia. Scientific Reports, 2020, vol. 10, no. 1, pp. 1–9. https://www.doi.org/10.1038/s41598-020-58196-z  
  111. Tankanag A. V., Grinevich A. A., Tikhonova I. V., Chemeris N. K. An analysis of phase relationships between oscillatory processes in the human cardiovascular system. Biophysics, 2020, vol. 65, no. 1, pp. 159–164. https://www.doi.org/10.1134/s0006350920010194
  112.  Tikhonova I. V., Grinevich A. A., Tankanag A. V. Analysis of phase interactions between heart rate variability, respiration and peripheral microhemodynamics oscillations of upper and lower extremities in human. Biomedical Signal Processing and Control, 2022, vol. 71, e103091. https://www.doi.org/10.1016/j.bspc.2021.103091
  113. Tankanag A., Krasnikov G., Mizeva I. A pilot study : Wavelet cross-correlation of cardiovascular oscillations under controlled respiration in humans. Microvascular Research, 2020, vol. 130, e103993. https://www.doi.org/10.1016/j.mvr.2020.103993
  114. Tankanag A. V., Krasnikov G. V., Chemeris N. K. Phase Coherence of Finger Skin Blood Flow Oscillations Induced by Controlled Breathing in Humans. Physics of Biological Oscillators : New Insights into Non-Equilibrium and Non-Autonomous Systems. Cham, Springer, 2021, pp. 281. https://www.doi.org/10.1007/978-3-030-59805-1_18
  115. Sagaidachnyi A. A., Skripal An. V., Fomin A. V., Usanov D. A. Determination of the amplitude and phase relationships between oscillations in skin temperature and photoplethysmography-measured blood flow in fingertips. Physiological Measurement, vol. 35, no. 2, pp. 153–166. https://www.doi.org/10.1088/0967-3334/35/2/153
  116. Sagaidachnyi A. A., Fomin A. V., Usanov D. A., Skripal An. V. Thermography-based blood flow imaging in human skin of the hands and feet : A spectral filtering approach. Physiological Measurement, 2017, vol. 38, no. 2, pp. 272–288. https://www.doi.org/10.1088/1361-6579/aa4eaf
  117. Sagaidachnyi A. A., Fomin A. V., Usanov D. A., Skripal An. V. Real-time technique for conversion of skin temperature into skin blood flow : Human skin as a low-pass filter for thermal waves. Computer Methods in Biomechanics and Biomedical Engineering, 2019, vol. 22, no. 12, pp. 1009–1019. https://www.doi.org/10.1080/10255842.2019.1615058
  118. Allan D., Chockalingam N., Naemi R. Validation of a non-invasive imaging photoplethysmography device to assess plantar skin perfusion, a comparison with laser speckle contrast analysis. Journal of Medical Engineering & Technology, 2021, vol. 45, no. 3, pp. 170–176. https://www.doi.org/10.1080/03091902.2021.1891309
  119. Volkov I. Yu., Fomin A. V., Mayskov D. I., Zaletov I. S., Skripal An. V., Sagaidachnyi A. A. Possibilities of photoplethysmographic visualization of peripheral hemodynamics in the low-frequency range. Metody komp’iuternoi diagnostiki v biologii i meditsine – 2021 : Sbornik statei Vserossiiskoi shkoly-seminara. Pod red. An. V. Skripalia [An. V. Skripal, ed. Methods of Computer Diagnostics in Biology and Medicine – 2021 : Collection of articles of the All-Russian school-seminar]. Saratov, Izd-vo “Saratovskii istochnik”, 2021, pp. 107–110 (in Russian).
  120. Volkov I. Yu., Fomin A. V., Mayskov D. I., Skripal An. V., Sagaidachnyi A. A. Imaging photoplethysmography of hemodynamics and oximetryusing optical clearing of human skin. Yu. N. Dubnishcheva, N. M. Skornyakovoi, eds. Optical Methods of Flow Investigation : Proceedings of the XVI International Scientific and Technical Conference. Moscow, Pero Publ., 2021, pp. 107–113 (in Russian).
  121. Sun Y., Hu S., Azorin-Peris V., Kalawsky R., Greenwald S. Noncontact imaging photoplethysmography to effectively access pulse rate variability. Journal of Biomedical Optics, 2013, vol. 18, no. 6, e061205. https://www.doi.org/10.1117/1.JBO.18.6.061205
  122. Taranov A. A., Spiridonov I. N. Contactless photoplethysmography and arterial pulse rate measurements by means of a webcam. Biomeditsinskaya radioelektronika [Biomedical Radioelectronics], 2014, no. 10, pp. 71–80 (in Russian).
  123. Kublanov V., Purtov K., Belkov D. Remote Photoplethysmography for the Neuro-electrostimulation Procedures Monitoring. Science and Technology Publications, 2017, vol. 4, pp. 307–314. https://www.doi.org/10.5220/0006176003070314
  124. Kopeliovich M. V., Petrushan M. V. Optimal Facial Areas for Webcam-Based Photoplethysmography. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications), 2016, vol. 26, no. 1, pp. 150–154. https://www.doi.org/10.1134/S1054661816010120  
  125. Imms R., Hu S., Azorin-Peris V., Trico M., Summers R. A high performance biometric signal and image processing method to reveal blood perfusion towards 3D oxygen saturation mapping. The International Society for Optical Engineering, 2014, vol. 8947. https://www.doi.org/10.1117/12.2044318
  126. Blazek C. R., Merk H. F., Schmid-Schoenbein H., Huelsbusch M., Blazek V. Assessment of allergic skin reactions and their inhibition by antihistamines using photoplethysmography imaging (ppgi). J. Allergy Clin. Immun., 2006, vol. 117, no. 2, pp. S226. https://www.doi.org/10.1016/j.jaci.2005.12.894
  127. Hulsbusch M., Blazek V. Contactless mapping of rhythmical phenomena in tissue perfusion using ppgi. Proc. SPIE, 2002, vol. 4683, pp. 110–117. https://www.doi.org/10.1117/12.463573
  128. Wieringa F. P., Mastik F. Remote Non-invasive Stereoscopic Imaging of Blood Vessels : First In-vivo Results of a New Multispectral Contrast Enhancement Technology. Annals of Biomedical Engineering, 2006, vol. 34, no. 12, pp. 1870–1878. https://www.doi.org/10.1007/s10439-006-9198-1
  129. Kobayashi L., Chuck C. C., Kim C. K., Luchette K. R., Oster BS. A., Merck D. L., Kirenko I., Zon K. V., Bartula M., Rocque M., Wang H., Capraro G. A. Pilot Study of Emergency Department Patient Vital Signs Acquisition Using Experimental Video Photoplethysmography and Passive Infrared Thermography Devices. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019, pp. 0023–0032. https://www.doi.org/10.1109/UEMCON47517.2019.8993087
  130. Cho Y., Julier S. J., Bianchi-Berthouze N. Instant stress : Detection of perceived mental stress through smartphone photoplethysmography and thermal imaging. JMIR Mental Health, 2019, vol. 6, no. 4, e10140. https://www.doi.org/10.2196/10140
  131. Blanik N., Abbas A. K., Venema B., Blazek V., Leonhardt S. Hybrid optical imaging technology for long-term remote monitoring of skin perfusion and temperature behavior. Journal of Biomedical Optics, 2014, vol. 19, no. 1, e016012. https://www.doi.org/10.1117/1.JBO.19.1.016012
  132. Paul M., Behr S. C., Weiss C., Heimann K., Orlikowsky T., Leonhardt S. Spatio-temporal and-spectral feature maps in photoplethysmography imaging and infrared thermograph. BioMedical Engineering OnLine, 2021, vol. 20, no. 1, pp. 1–54. https://www.doi.org/10.1186/s12938-020-00841-9
  133. Humphreys K., Ward T., Markham C. Noncontact simultaneous dual wavelength photoplethysmography : A further step toward noncontact pulse oximetry. Review of Scientific Instruments, 2007, vol. 78, no. 4, e044304. https://www.doi.org/10.1063/1.2724789
  134. Kong L., Zhao Y. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light. Optics Express, 2013, vol. 21, no. 15, pp. 17464–17471. https://www.doi.org/10.1364/OE.21.017464
  135. Shao D., Liu C. Noncontact Monitoring of Blood Oxygen Saturation Using Camera and Dual Wavelength Imaging System. IEEE Transactions on Biomedical Engineering, 2016, vol. 63, no. 6, pp. 1091–1098. https:// www.doi.org/10.1109/TBME.2015.2481896
  136. Foroughian F., Bauder C. J. The Wavelength Selection for Calibrating Non Contact Detection of Blood Oxygen Satuartion using Imaging Photoplethysmography. 2018 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2018, pp. 1–2.
  137. Moco A., Verkruysse W. Pulse oximetry based on photoplethysmography imaging with red and green light. Journal of Clinical Monitoring and Computing, 2021, vol. 35, no. 1, pp. 123–133. https://www.doi.org/10.1007/s10877-019-00449-y
  138. Gastel M., Wang W., Verkruysse W. Reducing the effects of parallax in camera-based pulse-oximetry. Biomedical Optics Express, 2021, vol. 12, no. 5, pp. 2813–2824. https://www.doi.org/10.1364/BOE.419199
  139. Gastel M., Verkruysse W., Haan G. Data-driven calibration estimation for robust remote pulse-oximetry. Appl. Sci., 2019, vol. 9, no. 18, e3857. https://www.doi.org/10.3390/app9183857
  140. Bal U. Non-contact estimation of heart rate and oxygen saturation using ambient light. Biomedical Optics Express, 2015, vol. 6, no. 1, pp. 86–97. https://www.doi.org/10.1364/BOE.6.000086
  141. Freitas U. S. Remote Camera-based Pulse Oximetry. The Sixth International Conference on eHealth, Telemedicine, and Social Medicine. International Academy, Research and Industry Association (IARIA), 2014, pp. 59–63.
  142. Guazzi A. R., Villarroel M. Non-contact measurement of oxygen saturation with an RGB camera. Biomedical Optics Express, 2015, vol. 6, no. 9, pp. 3320–3338. https://www.doi.org/10.1364/BOE.6.003320
  143. Addison P. S. Modular continuous wavelet processing of biosignals : Extracting heart rate and oxygen saturation from a video signal. Healthcare Technology Letters, 2016, vol. 3, no. 2, pp. 1–6. https://www.doi.org/10.1049/htl.2015.0052
  144. Mathew J., Tian X., Wu M. Remote Blood Oxygen Estimation From Videos Using Neural Networks. 2021. arXiv:2107.05087.
  145. Ali A.-N., Khalid G. A. Non-Contact SpO2 Prediction System Based on a Digital Camera. Appl. Sci., 2021, vol. 11, no. 9, e4255. https://www.doi.org/10.3390/app11094255
  146. Tuchin V. V. Tissue Optics, Light Scattering Methods and Instruments for Medical Diagnosis. Society of Photo Optical, 2015. 988 p. (Russ. ed. : Tuchin V. V. Optika biologicheskikh tkanei : Metody rasseyaniya sveta v meditsinskoi diagnostike. Moscow : OOO Izdatel’skaya firma “Fiziko-matematicheskaya literatura”, 2013. 812 p.)
Received: 
29.12.2021
Accepted: 
04.02.2022
Published: 
31.03.2022