NEW SERIES. SERIES: PHYSICS

Izvestiya of Saratov University.

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


Cite this article as:

Sagaidachnyi A. A., Mayskov D. I., Zaletov I. S., Fomin A. V., Skripal A. 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. New series. Series: Physics. , 2020, vol. 20, iss. 2, pp. 103-115. DOI: https://doi.org/10.18500/1817-3020-2020-20-2-103-115

Published online: 
01.06.2020
Language: 
Russian
UDC: 
621.384.3:61

Detection of the Single Sweat Glands Activity Via the Macro Thermography Techniques and Its Relation with Skin Temperature and Peripheral Hemodynamics

Autors: 
Sagaidachnyi Andrey Aleksandrovich, Saratov State University
Mayskov Dmitry Igorevich, Saratov State University
Zaletov Ivan Sergeevich, Saratov State University
Fomin Andrey Vladimirovich, Saratov State University
Skripal Anatoly Vladimirovich, Saratov State University
Abstract: 

Background and Objectives: Interest in the study of the human’s sweat glands activity is due to the close relationship of their activity with human body’s peripheral regions sympathetic innervation. The increased activity of sweat glands and secretion occurs not only due to thermoregulation, but also is a response to the psychoemotional load and the physiological and drug tests. Sweat gland activity’s disruption manifests itself in pathologies such as diabetes mellitus, chronic heart failure, hyperhidrosis, thyrotoxicosis, and Parkinson’s disease. Thus, the search for a method for quantifying the sweat glands activity on the surface of a human body based on physical methods is relevant for biomedical diagnosis. Materials and Methods: The primary thermographic data were recorded using a ThermaCam SC 3000 thermal imaging camera, FLIR Systems (Sweden) with a 34/100 macro lens, in the spectral range of 8–9 μm with a temperature sensitivity of 0.02°C and a resolution of 320  240 pixels with a frequency 5 frames per second. The measurements were performed in laboratory conditions with a stable ambient temperature of 23 ± 0.2°C and the absence of forced convection. Simultaneously with temperature measurements, the volumetric blood flow was monitored using a KL-72001 photoplethysmographic sensor (Taiwan) at a wavelength of 800 nm. Results: The both hands reaction to a breathing test was observed. The dynamics of changes in the active sweat pore count, temperature and blood flow was analyzed. Using an algorithm for processing thermograms that implements the suppression of low-frequency and high-frequency spatial components of temperature fluctuations, and the subsequent detection of local minima, makes it possible using the infrared thermography method to study not only the temperature dynamics, but also the sweat glands activity. Conclusion: Thus, the thermographic recording of the limb’s reaction to a respiratory test using the algorithm for detecting the sweat pore count can be positioned as a tool for studying the body’s sympathetic response to a functional load. The method is interest for the prophylactic detection of small vessel’s neuropathy’s various forms in diabetes mellitus, thyroid disorders and Raynaud’s syndrome.

DOI: 
10.18500/1817-3020-2020-20-2-103-115
References: 
  1. Krishnamurthy N., Mubarak A. S., Sri V. G., Balakumarr B., Srinivasan V. Infl uence of respiration on human sympathetic skin response. Indian Journal of Physiology and Pharmacology, 1996, vol. 40, pp. 350–354.
  2. Familoni B. O., Gregor K. L., Dodson T. S., Krzywicki A. T., Lowery Jr B. N., Orr S. P., Rasmusson A. M. Sweat pore reactivity as a surrogate measure of sympathetic nervous system activity in trauma-exposed individuals with and without posttraumatic stress disorder. Psychophysiology, 2016, vol. 53, no. 9, pp. 1417–1428. DOI: https://doi.org/10.1111/psyp.12681
  3. Shibasaki M., Kondo N., Crandall C. G. Non-thermoregulatory modulation of sweating in humans. Exercise and Sport Sciences Reviews, 2003, vol. 31, no. 1, pp. 34–39.
  4. 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. DOI: https://doi.org/10.1111/j.1469-8986.1994.tb01040.x
  5. 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. DOI: https://doi.org/10.1111/j.1469-8986.1967.tb02762.x
  6. 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.
  7. Znamenskaya I., Koroteeva E., Isaychev A., Chernorizov A. Thermography-based remote detection of psycho-emotional states. QIRT 2018 Proceedings, vol. 1, pp. 51–56. DOI: https://doi.org/10.21611/qirt.2018.p13
  8. Znamenskaya I. A., Koroteeva E. Yu., Shishakov V. V., Khakhalin A. V., Kuzmicheva E. A., Isaichev S. A., Chernorizov A. M. Analysis of video sequences and thermal images of human faces for remote detection of psychoemotional states. Materials of the 27th International Conference on Computer Graphics and Machine Vision, GraphiCon 2017 (Perm, 24–28 September, 2017). Perm, 2017, pp. 121–124 (in Russian).
  9. Znamenskaya I. A., Koroteeva E. Yu., 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).
  10. 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. DOI: https://doi.org/10.1016/S0190-9622(89)70081-5
  11. Vainer B. G. Matrichnoe teplovidenie v fi ziologii [Matrix Thermal Imaging in Physiology]. Novosibirsk, Siberian Branch of the Russian Academy of Sciences, 2004. 95 p. (in Russian).
  12. Vainer B. G. Short-wavelength matrix thermal imagers are an optimal tool for medical diagnostics and control. Bol’nichnyi list [Sick Leave], 2002, no. 9, pp. 14–21 (in Russian).
  13. 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. 63–94.
  14. Vetrugno R., Liguori R., Cortelli P., Montagna P. Sympathetic skin response. Clinical Autonomic Research, 2003, vol. 13, no. 4, pp. 256–270. DOI: https://doi.org/10.1007/s10286-003-0107-5
  15. Ohmi M., Tanigawa M., Yamada A., Ueda Y., Haruna M. Dynamic analysis of internal and external mental sweating by optical coherence tomography. J. Biomed. Opt., 2009, vol. 14, no. 1, 014026. DOI: https://doi.org/10.1117/1.3079808
  16. Lee J., Pyo M., Lee S. H., Kim J., Ra M., Kim W. Y., Park B. J., Lee Ch. W., Kim J. M. Hydrochromic conjugated polymers for human sweat pore mapping. Nature Communications, 2014, vol. 5, no. 1, pp. 1–10, 3736. DOI: https://doi.org/10.1038/ncomms4736
  17. Ring E. F. J. The historical development of temperature measurement in medicine. Infrared Physics & Technology, 2007, vol. 49, no. 3, pp. 297–301 DOI: https://doi.org/10.1016/j.infrared.2006.06.029
  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. DOI: https://doi.org/10.1109/TBME.2008.2003265
  19. Cardone D., Pinti P., Merla A. Thermal infrared imagingbased computational psychophysiology for psychometrics. Computational and Mathematical Methods in Medicine, 2015, ID 984353. DOI: https://doi.org/10.1155/2015/984353
  20. Ivanitsky G. R. Modern matrix thermovision in biomedicine. Advances in Physical Sciences, 2006, vol. 49, no. 12, pp. 1263–1288. DOI: https://doi.org/10.1070/PU2006v049n12ABEH006163
  21. 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. DOI: https://doi.org/10.1016/j.ijpsycho.2014.06.011
  22. 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. 365–373.
  23. Allen J., Di Maria C., Mizeva I., Podtaev S. Finger microvascular responses to deep inspiratory gasp assessed and quantifi ed using wavelet analysis. Physiological Measurement, 2013, vol. 34, no. 7, pp. 769–779.
  24. Illigens B. M. W., Gibbons C. H. Sweat testing to evaluate autonomic function. Clinical Autonomic Research, 2009, vol. 19, no. 2, pp. 79–87, DOI: https://doi.org/10.1007/s10286-008-0506-8
  25. Low V. A., Sandroni P., Fealey R. D., Low P. A. Detection of small-fi ber neuropathy by sudomotor testing. Muscle & Nerve, 2006, vol. 34, no. 1, pp. 57–61. DOI: https://doi.org/10.1002/mus.20551
  26. Gibbons C. H., Illigens B. M. W., Wang N., Freeman R. Quantifi cation of sweat gland innervation: a clinicalpathologic correlation. Neurology, 2009, vol. 72, no. 17, pp. 1479–1486. DOI: https://doi.org/10.1212/WNL.0b013e3181a2e8b8
  27. Podtaev S., Morozov M., Frick P. Wavelet-based correlations of skin temperature and blood fl ow oscillations. Cardiovas. Eng., 2008, vol. 8, pp. 185–189. DOI: https://doi.org/10.1007/s10558-008-9055-y
  28. Frick P., Mizeva I., Podtaev S. Skin temperature variations as a tracer of microvessel tone. Biomedical Signal Processing and Control, 2015, vol. 21, pp. 1–7. DOI: https://doi.org/10.1016/j.bspc.2015.04.014
  29. Sagaidachnyi A. A., Fomin A. V., Usanov D. A., Skripal A. V. Real-time technique for conversion of skin temperature into skin blood fl ow: human skin as a lowpass fi lter for thermal waves. Computer Methods in Biomechanics and Biomedical Engineering, 2019, vol. 22, no. 12, pp. 1009–1019. DOI: https://doi.org/10.1080/10255842.2019.1615058
  30. Sagaidachnyi A. A., Fomin A. V., Volkov I. Yu. Limit capabilities of modern thermal imaging cameras as a tool for investigation of peripheral blood fl ow oscillations within different frequency ranges. Medical Physics, 2016, no. 4. pp. 84–93 (in Russian).
  31. Sagaidachnyi A. A., Skripal A. 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, 2014, vol. 35, no. 2, pp. 153–166. DOI: https://doi.org/10.1088/0967-3334/35/2/153
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