volume | PIER Journals

Abstract

In this paper, an electrically-small microwave dipole sensor is used with machine learning algorithms to build a noninvasive continuous glucose monitoring (CGM) system. As a proof of concept, the sensor is used on aqueous (water-glucose) solutions with different glucose concentrations to check the sensitivity of the sensor. Knowledge-driven and data-driven approaches are used to extract features from the sensor's signals reflected from the aqueous glucose solution. Machine learning is used to build the regression model in order to predict the actual glucose levels. Using more than 19 regression models, the results show a good accuracy with Root Mean Square Error of 1.6 and 1.7 by Matern 5/2 Gaussian Process Regression (GPR) algorithm using the reflection coefficient's magnitude and phase.

1. DeFronzo, R. A., E. Ferrannini, P. Zimmet, and G. Alberti, International Textbook of Diabetes Mellitus, John Wiley & Sons, 2015.
doi:10.1002/9781118387658

2. Saeedi, P., I. Petersohn, P. Salpea, B. Malanda, S. Karuranga, N. Unwin, S. Colagiuri, L. Guariguata, A. A. Motala, K. Ogurtsova, et al. "Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas," Diabetes Research and Clinical Practice, Vol. 157, 107843, 2019.
doi:10.1016/j.diabres.2019.107843

3. Hanna, J., M. Bteich, Y. Tawk, A. H. Ramadan, B. Dia, F. A. Asadallah, A. Eid, R. Kanj, J. Costantine, and A. A. Eid, "Noninvasive, wearable, and tunable electromagnetic multisensing system for continuous glucose monitoring, mimicking vasculature anatomy," Science Advances, Vol. 6, No. 24, eaba5320, 2020.
doi:10.1126/sciadv.aba5320

4. Zhang, W., Y. Du, and M. L. Wang, "Noninvasive glucose monitoring using saliva nano-biosensor," Sensing and Bio-Sensing Research, Vol. 4, 23-29, 2015.
doi:10.1016/j.sbsr.2015.02.002

5. Olarte, O., J. Chilo, J. Pelegri-Sebastia, K. Barbe, and W. Van Moer, "Glucose detection in human sweat using an electronic nose," 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1462-1465, IEEE, 2013.
doi:10.1109/EMBC.2013.6609787

6. Heikenfeld, J., "Non-invasive analyte access and sensing through eccrine sweat: Challenges and outlook circa 2016," Electroanalysis, Vol. 28, No. 6, 1242-1249, 2016.
doi:10.1002/elan.201600018

7. Mun, P. S., H. N. Ting, Y. B. Chong, and T. A. Ong, "Dielectric properties of glycosuria at 0.2-50 GHz using microwave spectroscopy," Journal of Electromagnetic Waves and Applications, Vol. 29, No. 17, 2278-2292, 2015.
doi:10.1080/09205071.2015.1072480

8. Yan, Q., B. Peng, G. Su, B. E. Cohan, T. C. Major, and M. E. Meyerhoff, "Measurement of tear glucose levels with amperometric glucose biosensor/capillary tube configuration," Analytical Chemistry, Vol. 83, No. 21, 8341-8346, 2011.
doi:10.1021/ac201700c

9. Yao, H., A. J. Shum, M. Cowan, I. Lahdesmaki, and B. A. Parviz, "A contact lens with embedded sensor for monitoring tear glucose level," Biosensors and Bioelectronics, Vol. 26, No. 7, 3290-3296, 2011.
doi:10.1016/j.bios.2010.12.042

10. Gu, D., D. Zhang, L. Zhang, and G. Lu, "Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis," Sensors and Actuators B: Chemical, Vol. 173, 106-113, 2012.
doi:10.1016/j.snb.2012.06.025

11. Wei, T.-T., H.-Y. Tsai, C.-C. Yang, W.-T. Hsiao, and K.-C. Huang, "Noninvasive glucose evaluation by human skin oxygen saturation level," 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings (I2MTC), 1-5, IEEE, 2016.

12. Aldhaeebi, M. A., T. S. Almoneef, A. Ali, Z. Ren, and O. M. Ramahi, "Near field breast tumor detection using ultra-narrow band probe with machine learning techniques," Scientific Reports, Vol. 8, No. 1, 1-16, 2018.
doi:10.1038/s41598-018-31046-9

13. S. Matlab, , Matlab, The MathWorks, Natick, MA, 2012.

14. Jolliffe, I. T., "Principal components in regression analysis," Principal Component Analysis, 167-198, 2002.

15. Rasmussen, C. E., "Gaussian processes in machine learning," Summer School on Machine Learning, 63-71, Springer, 2003.

16. Williams, C. K. and C. E. Rasmussen, Gaussian Processes for Machine Learning, Vol. 2, No. 3, MIT Press Cambridge, 2006.

17. Cortes, C. and V. Vapnik, "Support-vector networks," Machine Learning, Vol. 20, No. 3, 273-297, 1995.

Dr. Saeed M. Bamatraf

Dr. Maged A. Aldhaeebi

Professor Omar M. Ramahi