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Optics and Spectroscopy, 2020, Volume 128, Issue 6, Pages 805–810
DOI: https://doi.org/10.21883/OS.2020.06.49414.46-20 (Mi os403) 		 
This article is cited in 12 scientific papers (total in 12 papers)

Saratov Fall Meeting 19: 7th International Symposium ''Optics and Biophotonics'', 23d International School for Junior Scientists and Students on Optics, Laser Physics & Biophotonics and 4th School on Advanced Fluorescence Imaging Methods
Biophotonics

Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning

Yu. V. Kistenevab, A. V. Tetenevab, T. V. Sorokinab, A. I. Knyazkovaac, O. A. Zakharovaac, A. Cuissetd, V. L. Vakse, E. G. Domrachevae, M. B. Chernyaevae, V. A. Anferteve, E. S. Simab, I. Yu. Yaninaaf, V. V. Tuchinafg, A. V. Borisovab

a Tomsk State University
b Siberian State Medical University, Tomsk, Russia
c Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, Tomsk, Russia
d Université du Littoral Côte d'Opale, Dunkerque, France
e Institute for Physics of Microstructures, Russian Academy of Sciences, Nizhnii Novgorod
f Saratov State University
g St. Petersburg National Research University of Information Technologies, Mechanics and Optics
Full-text PDF (991 kB) Citations (12)
DOI: https://doi.org/10.21883/OS.2020.06.49414.46-20
Abstract: Results of studying the exhaled air of patients with diabetes mellitus in comparison with healthy volunteers with the use of broadband terahertz time-domain spectroscopy are presented. Typical spectral subranges in which absorption spectrum profiles of breath tests of the target and control group differ most significantly are revealed: 0.560, 0.738, 0.970, 1.070, 1.140, 1.180, and 1.400 THz. Using the principal component analysis, it is shown that the set of absorption coefficients in these regions allows one to reliably separate the target and control groups. The obtained results are compared with measurements of acetone vapors in the exhaled air of patients with diabetes mellitus and healthy volunteers.
Keywords: diabetes, expired air, terahertz spectroscopy, machine learning.
Funding agency Grant number
Russian Foundation for Basic Research 18-52-16025
17-00-00275
17-00-00272
17-00-00184
17-00-00186
This work was performed within the framework of the Basic Research Program for State Academies of Sciences for 2013–2020, direction III.23. The study was supported by the Russian Foundation for Basic Research, project nos. 18-52-16025 and 17-00-00275 (17-00-00272, 17-00-00184, and 17-00-00186).
Received: 10.12.2019
Revised: 07.02.2020
Accepted: 28.02.2020
English version:
Optics and Spectroscopy, 2020, Volume 128, Issue 6, Pages 809–814
DOI: https://doi.org/10.1134/S0030400X20060090
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: Yu. V. Kistenev, A. V. Teteneva, T. V. Sorokina, A. I. Knyazkova, O. A. Zakharova, A. Cuisset, V. L. Vaks, E. G. Domracheva, M. B. Chernyaeva, V. A. Anfertev, E. S. Sim, I. Yu. Yanina, V. V. Tuchin, A. V. Borisov, “Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning”, Optics and Spectroscopy, 128:6 (2020), 805–810; Optics and Spectroscopy, 128:6 (2020), 809–814
Citation in format AMSBIB
\Bibitem{KisTetSor20}
\by Yu.~V.~Kistenev, A.~V.~Teteneva, T.~V.~Sorokina, A.~I.~Knyazkova, O.~A.~Zakharova, A.~Cuisset, V.~L.~Vaks, E.~G.~Domracheva, M.~B.~Chernyaeva, V.~A.~Anfertev, E.~S.~Sim, I.~Yu.~Yanina, V.~V.~Tuchin, A.~V.~Borisov
\paper Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning
\jour Optics and Spectroscopy
\yr 2020
\vol 128
\issue 6
\pages 805--810
\mathnet{http://mi.mathnet.ru/os403}
\crossref{https://doi.org/10.21883/OS.2020.06.49414.46-20}
\elib{https://elibrary.ru/item.asp?id=43808065}
\transl
\jour Optics and Spectroscopy
\yr 2020
\vol 128
\issue 6
\pages 809--814
\crossref{https://doi.org/10.1134/S0030400X20060090}
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