Izvestiya of Saratov University.


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

For citation:

Sagaidachnyi A. A., Volkov I. Y., Tsoy M. O., Fomin A. V., Mayskov D. I., Antonov A. В., Zaletov I. S., Skripal A. V. Assessment of spatiotemporal heterogeneity of two-dimensional images on the example of photoplethysmograpic imaging of hemodynamics. Izvestiya of Saratov University. Physics , 2023, vol. 23, iss. 2, pp. 128-140. DOI: 10.18500/1817-3020-2023-23-2-128-140, EDN: WTBLCR

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
Full text:
(downloads: 105)
Article type: 

Assessment of spatiotemporal heterogeneity of two-dimensional images on the example of photoplethysmograpic imaging of hemodynamics

Sagaidachnyi Andrey Aleksandrovich, Saratov State University
Volkov Ivan Yu., Saratov State University
Tsoy Maria Olegovna, Saratov State University
Fomin Andrey Vladimirovich, Saratov State University
Mayskov Dmitriy Igorevich, Saratov State University
Antonov Andrey Валерьевич, Saratov State University
Zaletov Ivan Sergeevich, Saratov State University
Skripal Anatoly Vladimirovich, Saratov State University

Background and Objectives: The problem of representation of multidimensional data on a two-dimensional plane arises during the processing of a series of two-dimensional images in the spatiotemporal and time-frequency domains. When implementing time-frequency analysis, each point of the object is characterized by a function of two arguments, therefore, to visualize the results on a two-dimensional plane, it is necessary to reduce the data dimension. Materials and Methods: This paper describes a method for color coding the correlation of spectral features at each point of a two-dimensional dynamic image. The novelty of the proposed method in the using of the wavelet correlation function of the reference area with all other regions of interest of the object. In this case, the correlation value is color-coded and forms a correlation map in each of the analyzed spectral ranges. Results: This allows to select areas that have similar time-frequency spectra of investigated characteristics of the object. The application of the method is considered on the example of the analysis of the microhemodynamics of the human hand using photoplethysmographic imaging. The analysis was carried out in the spectral range (0.005–2 Hz), covering both cardiac and low-frequency hemodynamic oscillations of the respiratory, myogenic, neurogenic, and endothelial ranges. In general, there is a tendency to a decrease of correlation of the spectrum with distance from the reference area and with a decrease in the analyzed signal frequency. It is shown that photoplethysmographic signals recorded in the area of the distal phalanx of the finger are predominantly representative of cardiac oscillations in microhemodynamics of other areas of the hand (correlation of about 0.7) and less representative with respect to endothelial, neurogenic, myogenic and respiratory oscillations (correlation of about 0.4). Due to the established high spatial inhomogeneity of the spectral features, it is recommended to use several reference areas when using contact photoplethysmographic measurements. Conclusion: The considered method of visualizing the spatial correlation of spectral features can find practical application also in the field of hemodynamic analysis using laser Doppler, laser speckle contrast, thermographic or hyperspectral imaging.

This work was funded by the Grant Council of the President of the Russian Federation for the state support of young Russian scientists – candidates of sciences (MK-140.2021.4).
  1.  Allen J., Howell K. Microvascular imaging: Techniques and opportunities for clinical physiological measurements. Physiological Measurement, 2014, vol. 35, iss. 7, pp. R91–R141. https://doi.org/10.1088/0967-3334/35/7/R91
  2. Dremin V. V., Zherebtsov E. A., Popov A. P., Meglinski I. V., Bykov A. V. Hyperspectral imaging of diabetes mellitus skin complications. In: Biomedical Photonics for Diabetes Research. CRC Press, 2022, pp. 177–195.
  3. Zherebtsov E., Dremin V., Popov A., Doronin A., Kurakina D., Kirillin M., Bykov A. Hyperspectral imaging of human skin aided by artificial neural networks. Biomedical Optics Express, 2019, vol. 10, iss. 7, pp. 3545–3559. https://doi.org/10.1364/BOE.10.003545
  4. Sagaidachnyi A., Mayskov D., Fomin A., Zaletov I., Skripal A. Separate extraction of human eccrine sweat gland activity and peripheral hemodynamics from high-and low-quality thermal imaging data. Journal of Thermal Biology, 2022, vol. 110, article no. 103351. https://doi.org/10.1016/j.jtherbio.2022.103351
  5. Mayskov D. I., Sagaidachnyi A. A., Zaletov I. S., Fomin A. V., Skripal An. V. Integral mapping of the sweat-gland activity using differential thermography technique. Izvestiya of Saratov University. Physics, 2021, vol. 21, iss. 3, pp. 222–232 (in Russian). https://doi.org/10.18500/1817-3020-2021-21-3-222-232
  6. Sagaidachnyi A. A., Mayskov D. I., Zaletov I. S., Fomin A. V., Skripal An. V. Detection of the Single Sweat Glands Activity Via the Macro Thermography Techniques and Its Relation with Skin Temperature and Peripheral Hemodynamics. Izvestiya of Saratov University. Physics, 2020, vol. 20, iss. 2, pp. 103–115 (in Russian). https://doi.org/10.18500/1817-3020-2020-20-2-103-115
  7. Cardone D., Pinti P., Merla A. Thermal infrared imaging-based computational psychophysiology for psychometrics. Computational and Mathematical Methods in Medicine, 2015, vol. 2015, article no. 984353. https://doi.org/10.1155/2015/984353
  8. Taratorin A. M., Godik E. E., Guljaev Y. V. Functional mapping of dynamic biomedical images. Measurement, 1990, vol. 8, no. 3, pp. 137–140. https://doi.org/10.1016/0263-2241(90)90055-B
  9. Frick P. G., Sokoloff D. D., Stepanov R. A. Wavelets for the space-time structure analysis of physical fields. Phys. Usp., 2022, vol. 65, pp. 62–89. https://doi.org/10.3367/UFNe.2020.10.038859
  10. Borik S., Lyra S., Perlitz V., Keller M., Leonhardt S., Blazek V. On the spatial phase distribution of cutaneous low-frequency perfusion oscillations. Scientific Reports, 2022, vol. 12, no. 1, pp. 1–18. https://doi.org/10.1038/s41598-022-09762-0
  11. 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, pp. 103091. https://doi.org/10.1016/j.bspc.2021.103091
  12. Mizeva I., Potapova E., Dremin V., Kozlov I., Dunaev A. Spatial heterogeneity of cutaneous blood flow respiratory-related oscillations quantified via laser speckle contrast imaging. PLoS ONE, 2021, vol. 16, no. 5, article no. e0252296. https://doi.org/10.1371/journal.pone.0252296
  13. Mizeva I., Dremin V., Potapova E., Zherebtsov E., Kozlov I., Dunaev A. Wavelet analysis of the temporal dynamics of the laser speckle contrast in human skin. IEEE Transactions on Biomedical Engineering, 2019. vol. 67, no. 7, pp. 1882–1889. https://doi.org/10.1109/TBME.2019.2950323
  14. Hultman M., Larsson M., Strömberg T., Henricson J., Iredahl F., Fredriksson I. Flowmotion imaging analysis of spatiotemporal variations in skin microcirculatory perfusion. Microvascular Research, 2022, vol. 146, article no. 104456. https://doi.org/10.1016/j.mvr.2022.104456
  15. Volkov I. Yu., Sagaidachnyi A. A., Fomin A. V. Photoplethysmographic imaging of hemodynamics and two-dimensional oximetry. Izvestiya of Saratov University. Physics, 2022, vol. 22, iss. 1, рр. 15–45 (in Russian). https://doi.org/10.18500/1817-3020-2022-22-1-15-45
  16. Procka P., Celovska D., Smondrk M., Borik S. Correlation Mapping of Perfusion Patterns in Cutaneous Tissue. Applied Sciences, 2022, vol. 12, iss. 15, article no. 7658. https://doi.org/10.3390/app12157658
  17. Kublanov V.S., Purtov K.S. Heart rate variability study by remote photoplethysmography. Biomeditsinskaia radioelektronika [Biomedical Radio Electronics], 2015, no. 8, pp. 3–9 (in Russian).
  18. 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://doi.org/10.18500/1817-3020-2021-21-1-58-68
  19. 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. The Russian Journal of Preventive Medicine, 2021, vol. 24, no. 8, pp. 73–79 (in Russian). https://doi.org/10.17116/profmed20212408173
  20. 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://doi.org/10.1016/j.bpj.2021.05.020
  21. 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://doi.org/10.1038/s41598-020-58196-z
  22. 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://doi.org/10.1134/s0006350920010194
  23. 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, article no. 103993. https://doi.org/10.1016/j.mvr.2020.103993
  24. Tankanag A. V., Krasnikov G. V., Chemeris N. K. Phase Coherence of Finger Skin Blood Flow Oscillations Induced by Controlled Breathing in Humans. In: Aneta Stefanovska, Peter V. E. McClintock, eds. Physics of Biological Oscillators: New Insights into Non-Equilibrium and Non-Autonomous Systems. Springer, Cham, Switzerland, 2021, pp. 281–289. https://doi.org/10.1007/978-3-030-59805-1_18
  25. Koronovskii A. A., Hramov A. E. Nepreryvnyi veivletnyi analiz i ego prilozheniia [Continuous wavelet analysis and its applications]. Moscow, Fizmatlit, 2003. 176 p. (in Russian).
  26. Nesme-Ribes E., Frick P., Sokoloff D., Zakharov V., Ribes J. C., Vigouroux A., Laclare F. Wavelet analysis of Maunder minimum as recorded in Solar diameter data. Comptes Rendus de Academie des Sciences, Paris, Serie II, 1995, vol. 321, no. 2 B, pp. 525–532.
  27. Mizeva I. A., Stepanov R. A., Frik P. G. Veivletnye krosskorreliatsii dvumernykh polei [Wavelet cross-correlations of two-dimensional fields]. Num. Meth. Prog., 2006, vol. 7, no. 2, pp. 172–179 (in Russian).
  28. Fedorovich A. A. Functional state of regulatory mechanisms of microcirculatory blood flow in normal and arterial hypertension according to laser Doppler flowmetry. Regional Blood Circulation and Microcirculation, 2010, vol. 9, no. 1, pp. 49–60 (in Russian).
  29. Krupatkin A. I. Pulse and respiratory oscillations of blood flow in the microvasculature of the human skin. Human Physiology, 2008, vol. 34, no. 3, pp. 70–76 (in Russian).
  30. Kvandal P., Landsverk S. A., Bernjak A., Stefanovska A., Kvernmo H. D., Kirkebøen K. A. Low-frequency oscillations of the laser Doppler perfusion signal in human skin. Microvascular Research, 2006, vol. 72, no. 3, pp. 120–127. https://doi.org/10.1016/j.mvr.2006.05.006
  31. Bernjak A., Stefanovska A., McClintock P. V., OwenLynch P. J., Clarkson P. B. Coherence between fluctuations in blood flow and oxygen saturation. Fluctuation and Noise Letters, 2012, vol. 11, no. 1, article no. 1240013. https://doi.org/10.1142/S0219477512400135