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Mayskov D. I., Sagaidachnyi A. A., Zaletov I. S., Fomin A. V., Skripal A. 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. DOI: 10.18500/1817-3020-2021-21-3-222-232, EDN: QQVIHQ

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Integral mapping of the sweat-gland activity using differential thermography technique

Mayskov Dmitriy Igorevich, Saratov State University
Sagaidachnyi Andrey Aleksandrovich, Saratov State University
Zaletov Ivan Sergeevich, Saratov State University
Fomin Andrey Vladimirovich, Saratov State University
Skripal Anatoly Vladimirovich, Saratov State University

Background and Objectives: The sweat-gland activity is associated with the functional state of small sympathetic nerve fibers that are subject to destructive changes in a amount of pathologies, for example, such as diabetic peripheral neuropathy and rheumatoid arthritis. In this work, we have solved the problem of visualizing sweat pores on the skin surface using dynamic differential thermography. Materials and Methods: Based on the wavelet analysis of the fingers phalanges skin temperature fluctuations, it was found that the sweat-gland activity forms spectral components at frequencies of about 0.1 Hz and higher. As a result, it was proposed to consider the temperature signal as a twocomponent one. It is believed that the low-frequency component less than 0.1 Hz is mainly due to hemodynamics, the high-frequency component is mainly due to the functioning of the sweat glands and sweating. To implement differential thermography, the difference between the current frame and the frame delayed by 10 s relative to it was used. Results: As a result, this made it possible to isolate spatial high-frequency information corresponding to sweat pores on the dynamic thermogram. Testing with a sharp breath showed that the signal level of the differential thermogram characterizes the level of the sweat-gland activity that changes over time. Building an integral map of sweat-gland activity by averaging differential thermograms over the entire registration period makes it possible to assess the spatial distribution of sweat gland activity time. The given example of an integral map showed a decrease in the spatial density of functioning sweat glands in a patient with type 2 diabetes mellitus compared with a normal subject. Conclusion: Thus, differential thermography and integral maps of the sweat-gland activity can find application in the field of medicine and physiology for quantitative diagnosis and monitoring of therapy for sympathetic nerve fibers dysfunction, which is relevant in a number of socially significant diseases.

The study and application of a two-component model of the skin temperature dynamics for detecting sweat pores were funded by RFBR according to the research project № 19-32-90072; the study of oscillations in skin temperature caused by the sweat-gland activity, and the possibility of presenting a dynamic thermogram in the form of an integral map was supported by the Russian Science Foundation (project No. 21-75-00035).
  1. Achkasov E. E., Volovik M. G., Dolgov I. M., Kolesov S. N. Meditsinskoe teplovidenie [Medical Thermal Imaging]. Moscow, INFRA-M Academic Publishing LLC, 2019. 218 p. (in Russian).
  2. Volovik M. G., Dolgov I. M. Thermotography of the hands of a healthy person as a basis for thermal diagnosis (narrative review). Modern Functional Diagnostics, 2020, vol. 4, no. 32, pp. 62–68 (in Russian). https://doi.org/10.33667/2078-5631-2020-32-62-68
  3. Volovik M. G., Dolgov I. M. Thermosemiotics of the hands. Report 2. Thermal patterns of the hands in patients with upper limbs vascular disorders, Raynaud’s phenomenon, after thoracic sympathectomy, in ischemic heart disease and a number of other diseases. Medical Alphabet, 2021, no. 5, pp. 62–70 (in Russian). https://doi.org/10.33667/2078-5631-2021-5-62-70
  4. Ivanitsky G. R. Modern matrix thermovision in biomedicine. Physics-Uspekhi, 2006, vol. 49, no. 12, pp. 1263.
  5. Shusharin A. G., Morozov V. V., Polovinka M. P. Medical infrared imaging – modern features of the method. Modern Problems of Science and Education, 2011, no. 4, pp. 1–10 (in Russian).
  6. Kozhevnikova I. S., Pankov M. N., Gribanov A. V., Startseva L. F., Ermoshina N. A. The Use of Infrared Thermography in Modern Medicine (Literature Review). Ekologiya cheloveka [Human Ecology], 2017, no. 2, pp. 39–46 (in Russian).
  7. Morozov A. M., Mokhov E. M., Kadykov V. A., Panova A. V. Medical thermography: capabilities and perspectives. Kazan Medical Journal, 2018, vol. 99, no. 2, pp. 264–270. https://doi.org/10.17816/KMJ2018-264
  8. Vainer B. G. FPA-based infrared thermography as applied to the study of cutaneous perspiration and stimulated vascular response in humans. Physics in Medicine & Biology, 2005, vol. 50, no. 23, pp. 63.
  9. Vainer B. G. Matrichnoe teplovidenie v fiziologii [Matrix Thermal Imaging in Physiology]. Novosibirsk, Izd-vo Sibirskogo otdeleniya Ros. AN, 2004. 95 p. (in Russian).
  10. Znamenskaya I. A., Koroteeva E. Iu., Khakhalin A. V., Shishakov V. V. Thermographic visualization and remote control of dynamical processes around a facial area. Nauchnaia vizualizatsiia [Scientifi c Visualization], 2016, vol. 8, no. 5, pp. 122–131 (in Russian).
  11. Glatte P., Buchmann S. J., Hijazi M. M., Illigens B. M. W., Siepmann T. Architecture of the Cutaneous Autonomic Nervous System. Frontiers in Neurology, 2019, no. 10, pp. 970. https://doi.org/10.3389/fneur.2019.00970
  12. Znamenskaya I. A., Koroteyeva E. Y., Khakhalin A. V., Shishakov V. V., Isaichev S. A., Chernorizov A. M. Infrared Thermography and Image Analysis of Dynamic Processes around the Facial Area. Moscow University Physics Bulletin, 2017, vol. 72, no. 6, pp. 595–600.
  13. Znamenskaya I., Koroteeva E., Isaychev A., Chernorizov A. Thermography-based remote detection of psychoemotional states. Proc. QIRT 2018. 14th Quantitative InfraRed Thermography Conference, 25–29 June 2018, Berlin, Germany. https://doi.org/10.21611/qirt.2018.p13
  14. Freedman L. W., Scerbo A. S., Dawson M. E., Raine A., McClure W. O., Venables P. H. The relationship of sweat gland count to electrodermal activity. Psychophysiology, 1994, vol. 31, no. 2, pp. 196–200. https://doi.org/10.1111/j.1469-8986.1994.tb01040.x
  15. Juniper Jr K., Blanton D. E., Dykman R. A. Palmar skin resistance and sweat-gland counts in drug and non-drug states. Psychophysiology, 1967, vol. 4, no. 2, pp. 231–243. https://doi.org/10.1111/j.1469-8986.1967.tb02762.x
  16. Sato K., Kang W. H., Saga K., Sato K. T. Biology of sweat glands and their disorders. II. Disorders of sweat gland function. Journal of the American Academy of Dermatology, 1989, vol. 20, no. 5, pp. 713–726. https://doi.org/10.1016/S0190-9622(89)70081-5
  17. Krzywicki A. T., Berntson G. G., O’Kane B. L. A. Noncontact technique for measuring eccrine sweat gland activity using passive thermal imaging. International Journal of Psychophysiology, 2014, vol. 94, no. 1, pp. 25–34. https://doi.org/10.1016/j.ijpsycho.2014.06.011
  18. Shastri D., Merla A., Tsiamyrtzis P., Pavlidis I. Imaging facial signs of neurophysiological responses. IEEE Transactions on Biomedical Engineering, 2009, vol. 56, no. 2, pp. 477–484. https://doi.org/10.1109/TBME.2008.2003265
  19. Shastri D., Papadakis M., Tsiamyrtzis P., Bass B., Pavlidis I. Perinasal imaging of physiological stress and its affective potential. IEEE Transactions on Affective Computing, 2012, vol. 3, no. 3, pp. 366–378. https://doi.org/10.1109/T-AFFC.2012.13
  20. 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
  21. Allen J., Frame J. R., Murray A. Microvascular blood flow and skin temperature changes in the fingers following a deep inspiratory gasp. Physiological Measurement, 2002, vol. 23, no. 2, pp. 365.
  22. Mayrovitz H. N., Groseclose E. E. Neurovascular responses to sequential deep inspirations assessed via laser-Doppler perfusion changes in dorsal finger skin. Clinical Physiology and Functional Imaging, 2002, vol. 22, no. 1, pp. 49–54. https://doi.org/10.1046/j.1475-097x.2002.00404.x
  23. Sagaidachnyi A. A., Fomin A. V., Volkov I. Yu. Limit capabilities of modern thermal imaging cameras as a tool for investigation of peripheral blood flow oscillations within different frequency ranges. Meditsinskaia fizika [Medical Physics], 2016, no. 4, pp. 84–93.
  24. Bentham M., Stansby G., Allen J. Innovative multi-site photoplethysmography analysis for quantifying pulse amplitude and timing variability characteristics in peripheral arterial disease. Diseases, 2018, vol. 6, no. 3, pp. 81–95. https://doi.org/10.3390/diseases6030081
  25. Karavaev A. S., Borovik A. S., Borovkova E. I., Orlova E. A., Simonyan M. A., Ponomarenko V. I., Skazkina V. V., Gridnev V. I., Bezruchko B. P., Prokhorov M. D., Kiselev A. R. Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure. Biophysical Journal, 2021, vol. 120, iss 13, pp. 2657–2664. https://doi.org/10.1016/j.bpj.2021.05.020
  26. Nowakowski A., Kaczmarek M., Ruminski J., Hryciuk M., Renkielska A., Grudzinski J., Siebert J., Jagielak D., Rogowski J., Roszak K., Stojek W. Medical applications of model-based dynamic thermography. Thermosense XXIII. International Society for Optics and Photonics, 2001, vol. 4360, pp. 492–503. https://doi.org/10.1117/12.421030
  27. Estañol B., Corona M. V., Elías Y., Téllez-Zenteno J. F., Infante O., García-Ramos G. Sympathetic co-activation of skin blood vessels and sweat glands. Clinical Autonomic Research, 2004, vol. 14, no. 2, pp. 107–112. https://doi.org/10.1007/s10286-004-0170-6