Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes

Evgeny A. Zherebtsov, Elena V. Zharkikh, Yulia I. Loktionova, Angelina I. Zherebtsova, Victor V. Sidorov, Edik U. Rafailov, Andrey V. Dunaev

Research output: Contribution to journalArticlepeer-review


This paper presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10 s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.
Original languageEnglish
Pages (from-to)3073-3081
Number of pages9
Journal IEEE Transactions on Biomedical Engineering
Issue number11
Early online date12 May 2023
Publication statusPublished - 1 Nov 2023

Bibliographical note

Copyright © 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Funding & Acknowledgements: Authors acknowledge the patients and doctors (G.I. Masalygina, E.A. Alimicheva) of the Endocrinology Department of the Orel Regional Clinical Hospital for their help and participation. This work was supported in part by the Academy of Finland under Grants 318281 and 326204, in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 863214-NEUROPA Project. EVZ and YIL kindly acknowledge the Russian Science Foundation for its financial support under Project No. 23-25-00522
(data collection and analysis).


  • Biomedical monitoring
  • Blood
  • Diabetes
  • Doppler effect
  • Laser Doppler flowmetry
  • Monitoring
  • Sensors
  • Skin
  • autocorrelation
  • blood perfusion
  • diabetes mellitus
  • machine learning
  • wavelet transform
  • wireless sensor


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