Vol. 102
Latest Volume
All Volumes
PIERL 108 [2023] PIERL 107 [2022] PIERL 106 [2022] PIERL 105 [2022] PIERL 104 [2022] PIERL 103 [2022] PIERL 102 [2022] PIERL 101 [2021] PIERL 100 [2021] PIERL 99 [2021] PIERL 98 [2021] PIERL 97 [2021] PIERL 96 [2021] PIERL 95 [2021] PIERL 94 [2020] PIERL 93 [2020] PIERL 92 [2020] PIERL 91 [2020] PIERL 90 [2020] PIERL 89 [2020] PIERL 88 [2020] PIERL 87 [2019] PIERL 86 [2019] PIERL 85 [2019] PIERL 84 [2019] PIERL 83 [2019] PIERL 82 [2019] PIERL 81 [2019] PIERL 80 [2018] PIERL 79 [2018] PIERL 78 [2018] PIERL 77 [2018] PIERL 76 [2018] PIERL 75 [2018] PIERL 74 [2018] PIERL 73 [2018] PIERL 72 [2018] PIERL 71 [2017] PIERL 70 [2017] PIERL 69 [2017] PIERL 68 [2017] PIERL 67 [2017] PIERL 66 [2017] PIERL 65 [2017] PIERL 64 [2016] PIERL 63 [2016] PIERL 62 [2016] PIERL 61 [2016] PIERL 60 [2016] PIERL 59 [2016] PIERL 58 [2016] PIERL 57 [2015] PIERL 56 [2015] PIERL 55 [2015] PIERL 54 [2015] PIERL 53 [2015] PIERL 52 [2015] PIERL 51 [2015] PIERL 50 [2014] PIERL 49 [2014] PIERL 48 [2014] PIERL 47 [2014] PIERL 46 [2014] PIERL 45 [2014] PIERL 44 [2014] PIERL 43 [2013] PIERL 42 [2013] PIERL 41 [2013] PIERL 40 [2013] PIERL 39 [2013] PIERL 38 [2013] PIERL 37 [2013] PIERL 36 [2013] PIERL 35 [2012] PIERL 34 [2012] PIERL 33 [2012] PIERL 32 [2012] PIERL 31 [2012] PIERL 30 [2012] PIERL 29 [2012] PIERL 28 [2012] PIERL 27 [2011] PIERL 26 [2011] PIERL 25 [2011] PIERL 24 [2011] PIERL 23 [2011] PIERL 22 [2011] PIERL 21 [2011] PIERL 20 [2011] PIERL 19 [2010] PIERL 18 [2010] PIERL 17 [2010] PIERL 16 [2010] PIERL 15 [2010] PIERL 14 [2010] PIERL 13 [2010] PIERL 12 [2009] PIERL 11 [2009] PIERL 10 [2009] PIERL 9 [2009] PIERL 8 [2009] PIERL 7 [2009] PIERL 6 [2009] PIERL 5 [2008] PIERL 4 [2008] PIERL 3 [2008] PIERL 2 [2008] PIERL 1 [2008]
2022-02-04
Noninvasive Continuous Glucose Monitoring on Aqueous Solutions Using Microwave Sensor with Machine Learning
By
Progress In Electromagnetics Research Letters, Vol. 102, 127-134, 2022
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.
Citation
Saeed M. Bamatraf Maged A. Aldhaeebi Omar M. Ramahi , "Noninvasive Continuous Glucose Monitoring on Aqueous Solutions Using Microwave Sensor with Machine Learning," Progress In Electromagnetics Research Letters, Vol. 102, 127-134, 2022.
doi:10.2528/PIERL21110905
http://www.jpier.org/PIERL/pier.php?paper=21110905
References

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., 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, MA, 2006.

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