Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave

Y. N.R. Reddy; K. T. Chandrasekaran; M. F. Karim; A. Alphones; M. Y. Siyal; A. Q. Liu

doi:10.1109/ICACCE.2018.8441729

Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave

Y. N.R. Reddy, K. T. Chandrasekaran, M. F. Karim, A. Alphones, M. Y. Siyal, A. Q. Liu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

A non-invasive blood glucose measurement method based on microwave transmission and applying machine learning technique to the data obtained, is proposed for monitoring the patients' blood glucose level. Using this technique, non-invasive measurement of the blood glucose concentration at the earlobe portion can be realized and put into use by analysing the reflected microwave signals. A third order Cole-Cole equation is derived to model the dielectric properties of human tissues. Particle swarm optimisation technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, human earlobe portion is modelled and tested at a wide range of frequencies to analyse for the region of linearity. It was observed that frequency range from 6-8 GHz shows the linearity and calculating the complex permittivity values with the help of transmission parameters. Applying Machine-learning technique to the above process can facilitate a real-time processing which in turn is able to alert patients during hyperglycemia conditions, and can suggest a precise dose of insulin to intake.

Original language	English
Title of host publication	Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018
Editors	Vishal Kumar, S. D. Sudarsan, Ravi Tomar
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	89-91
Number of pages	3
ISBN (Print)	9781538644850
DOIs	https://doi.org/10.1109/ICACCE.2018.8441729
Publication status	Published - 20 Aug 2018
Externally published	Yes
Event	2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018 - Paris, France Duration: 22 Jun 2018 → 23 Jun 2018

Publication series

Name	Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018

Conference

Conference	2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018
Country/Territory	France
City	Paris
Period	22/06/18 → 23/06/18

Keywords

Blood Glucose
Cole-cole model
diabetes
hyperglycemia
machine learning
microwave
particle swarm optimisation

ASJC Scopus subject areas

Computer Science (miscellaneous)
Computer Networks and Communications
Hardware and Architecture

More information

10.1109/ICACCE.2018.8441729

Cite this

Reddy, Y. N. R., Chandrasekaran, K. T., Karim, M. F., Alphones, A., Siyal, M. Y., & Liu, A. Q. (2018). Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave. In V. Kumar, S. D. Sudarsan, & R. Tomar (Eds.), Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018 (pp. 89-91). Article 8441729 (Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCE.2018.8441729

Reddy, Y. N.R. ; Chandrasekaran, K. T. ; Karim, M. F. et al. / Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave. Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018. editor / Vishal Kumar ; S. D. Sudarsan ; Ravi Tomar. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 89-91 (Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018).

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title = "Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave",

abstract = "A non-invasive blood glucose measurement method based on microwave transmission and applying machine learning technique to the data obtained, is proposed for monitoring the patients' blood glucose level. Using this technique, non-invasive measurement of the blood glucose concentration at the earlobe portion can be realized and put into use by analysing the reflected microwave signals. A third order Cole-Cole equation is derived to model the dielectric properties of human tissues. Particle swarm optimisation technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, human earlobe portion is modelled and tested at a wide range of frequencies to analyse for the region of linearity. It was observed that frequency range from 6-8 GHz shows the linearity and calculating the complex permittivity values with the help of transmission parameters. Applying Machine-learning technique to the above process can facilitate a real-time processing which in turn is able to alert patients during hyperglycemia conditions, and can suggest a precise dose of insulin to intake.",

keywords = "Blood Glucose, Cole-cole model, diabetes, hyperglycemia, machine learning, microwave, particle swarm optimisation",

author = "Reddy, {Y. N.R.} and Chandrasekaran, {K. T.} and Karim, {M. F.} and A. Alphones and Siyal, {M. Y.} and Liu, {A. Q.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018 ; Conference date: 22-06-2018 Through 23-06-2018",

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Reddy, YNR, Chandrasekaran, KT, Karim, MF, Alphones, A, Siyal, MY & Liu, AQ 2018, Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave. in V Kumar, SD Sudarsan & R Tomar (eds), Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018., 8441729, Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 89-91, 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018, Paris, France, 22/06/18. https://doi.org/10.1109/ICACCE.2018.8441729

Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave. / Reddy, Y. N.R.; Chandrasekaran, K. T.; Karim, M. F. et al.
Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018. ed. / Vishal Kumar; S. D. Sudarsan; Ravi Tomar. Institute of Electrical and Electronics Engineers Inc., 2018. p. 89-91 8441729 (Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Reddy, Y. N.R.

AU - Chandrasekaran, K. T.

AU - Karim, M. F.

AU - Alphones, A.

AU - Siyal, M. Y.

AU - Liu, A. Q.

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AB - A non-invasive blood glucose measurement method based on microwave transmission and applying machine learning technique to the data obtained, is proposed for monitoring the patients' blood glucose level. Using this technique, non-invasive measurement of the blood glucose concentration at the earlobe portion can be realized and put into use by analysing the reflected microwave signals. A third order Cole-Cole equation is derived to model the dielectric properties of human tissues. Particle swarm optimisation technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, human earlobe portion is modelled and tested at a wide range of frequencies to analyse for the region of linearity. It was observed that frequency range from 6-8 GHz shows the linearity and calculating the complex permittivity values with the help of transmission parameters. Applying Machine-learning technique to the above process can facilitate a real-time processing which in turn is able to alert patients during hyperglycemia conditions, and can suggest a precise dose of insulin to intake.

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Reddy YNR, Chandrasekaran KT, Karim MF, Alphones A, Siyal MY, Liu AQ. Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave. In Kumar V, Sudarsan SD, Tomar R, editors, Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 89-91. 8441729. (Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018). doi: 10.1109/ICACCE.2018.8441729

Machine Learning Approach for Non-Invasive Detection of Blood Glucose Concentration using Microwave

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