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

doi:10.1109/tbme.2023.3275654

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 journal › Article › peer-review

Abstract

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 language	English
Pages (from-to)	3073-3081
Number of pages	9
Journal	IEEE Transactions on Biomedical Engineering
Volume	70
Issue number	11
Early online date	12 May 2023
DOIs	https://doi.org/10.1109/tbme.2023.3275654
Publication status	Published - 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).

Keywords

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

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10.1109/tbme.2023.3275654Licence: CC BY 4.0

Zherebtsovetal_2023
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/
Final published version, 5.54 MBLicence: CC BY 4.0

Cite this

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title = "Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes",

abstract = "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.",

keywords = "Biomedical monitoring, Blood, Diabetes, Doppler effect, Laser Doppler flowmetry, Monitoring, Sensors, Skin, VCSEL, autocorrelation, blood perfusion, diabetes mellitus, machine learning, wavelet transform, wireless sensor",

author = "Zherebtsov, {Evgeny A.} and Zharkikh, {Elena V.} and Loktionova, {Yulia I.} and Zherebtsova, {Angelina I.} and Sidorov, {Victor V.} and Rafailov, {Edik U.} and Dunaev, {Andrey V.}",

note = "Copyright {\textcopyright} 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{\textquoteright}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). ",

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doi = "10.1109/tbme.2023.3275654",

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AU - Zherebtsov, Evgeny A.

AU - Zharkikh, Elena V.

AU - Loktionova, Yulia I.

AU - Zherebtsova, Angelina I.

AU - Sidorov, Victor V.

AU - Rafailov, Edik U.

AU - Dunaev, Andrey V.

N1 - 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).

PY - 2023/11/1

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N2 - 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.

AB - 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.

KW - Biomedical monitoring

KW - Blood

KW - Diabetes

KW - Doppler effect

KW - Laser Doppler flowmetry

KW - Monitoring

KW - Sensors

KW - Skin

KW - VCSEL

KW - autocorrelation

KW - blood perfusion

KW - diabetes mellitus

KW - machine learning

KW - wavelet transform

KW - wireless sensor

UR - https://ieeexplore.ieee.org/document/10123701

UR - http://www.scopus.com/inward/record.url?scp=85159815502&partnerID=8YFLogxK

U2 - 10.1109/tbme.2023.3275654

DO - 10.1109/tbme.2023.3275654

M3 - Article

SN - 0018-9294

VL - 70

SP - 3073

EP - 3081

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

IS - 11

ER -

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

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