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Medical & Biological Engineering & Computing

, Volume 57, Issue 11, pp 2389–2405 | Cite as

Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept

  • Carlos G. Juan
  • Héctor García
  • Ernesto Ávila-Navarro
  • Enrique Bronchalo
  • Vicente Galiano
  • Óscar Moreno
  • Domingo Orozco
  • José María Sabater-NavarroEmail author
Original Article
  • 215 Downloads

Abstract

Self-management of blood glucose level is part and parcel of diabetes treatment, which involves invasive, painful, and uncomfortable methods. A proper non-invasive blood glucose monitor (NIBGM) is therefore desirable to deal better with it. Microwave resonators can potentially be used for such a purpose. Following the positive results from an in vitro previous work, a portable device based upon a microwave resonator was developed and assessed in a multicenter proof of concept. Its electrical response was analyzed when an individual’s tongue was placed onto it. The study was performed with 352 individuals during their oral glucose tolerance tests, having four measurements per individual. The findings revealed that the accuracy must be improved before the diabetes community can make real use of the device. However, the relationship between the measuring parameter and the individual’s blood glucose level is coherent with that from previous works, although with higher data dispersion. This is reflected in correlation coefficients between glycemia and the measuring magnitude consistently negative, although small, for the different datasets analyzed. Further research is proposed, focused on system improvements, individual calibration, and multitechnology approach. The study of the influence of other blood components different to glucose is also advised.

Graphical abstract

Keywords

Blood glucose Microwaves Portable device Proof of concept Quality factor 

Notes

Acknowledgments

The authors would like to sincerely thank the nursing work carried out by María de los Ángeles Vicedo García and Ana Laura Morote Castellanos throughout the measurements and data acquisition process.

Funding information

Carlos G. Juan’s work was funded by the Spanish Ministry of Education, Culture, and Sport through the Research and Doctorate Supporting Program FPU under Grant FPU14/00401. This work was partially funded by the Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO) through Project UGP-15-202, and by Spanish Research State Agency and European Regional Development Fund through “Craneeal” Project (DPI2106-80391-C3-2-R).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee of Hospital General Universitario de Alicante and Ethics Committee of Hospital Universitatio San Juan de Alicante, as well as with the 1964 Declaration of Helsinki and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    World Health Organization (2016) Global report on diabetes. Switzerland, GenevaGoogle Scholar
  2. 2.
    Heinemann L (2008) Finger pricking and pain: a never ending story. J Diabetes Sci Technol 2(5):919–921.  https://doi.org/10.1177/193229680800200526 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    van Beers CAJ, DeVries JH (2016) Continuous glucose monitoring: impact on hypoglycemia. J Diabetes Sci Technol 10(6):251–1258.  https://doi.org/10.1177/1932296816653411 CrossRefGoogle Scholar
  4. 4.
    Fortwaengler K, Campos-Náñez E, Parkin CG, Breton MD (2018) The financial impact of inaccurate blood glucose monitoring systems. J Diabetes Sci Technol 12(2):318–324.  https://doi.org/10.1177/1932296817731423 CrossRefPubMedGoogle Scholar
  5. 5.
    Gill M, Zhu C, Shah M, Chhabra H (2018) Health care costs, hospital admissions, and glycemic control using a standalone, real-time, continuous glucose monitoring system in commercially insured patients with type 1 diabetes. J Diabetes Sci Technol 12(4):800–807.  https://doi.org/10.1177/1932296818777265 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Zarkogianni K, Mitsis K, Litsa E, Arredondo M-T, Fico G, Fioravanti A, Nikita KS (2015) Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring. Med Biol Eng Comput 53(12):1333–1343.  https://doi.org/10.1007/s11517-015-1320-9 CrossRefPubMedGoogle Scholar
  7. 7.
    Zhao C, Yu C (2015) Rapid model identification for online subcutaneous glucose concentration prediction for new subjects with type 1 diabetes. IEEE Trans Biomed Eng 57(8):1333–1344.  https://doi.org/10.1109/TBME.2014.2387293 CrossRefGoogle Scholar
  8. 8.
    Georga EI, Protopappas VC, Polyzos D, Fotiadis DI (2015) Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. Med Biol Eng Comput 53(12):1305–1318.  https://doi.org/10.1007/s11517-015-1263-1 CrossRefPubMedGoogle Scholar
  9. 9.
    Lee JB, Dassau E, Gondhalekar R, Seborg DE, Pinsker JE, Doyle FJ III (2016) Enhanced model predictive control (empc) strategy for automated glucose control. Ind Eng Chem Res 55:11857–11868.  https://doi.org/10.1021/acs.iecr.6b02718 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Abraham MB, Nicholas JA, Smith GJ, Fairchild JM, King BR, Ambler GR, Cameron FJ, Davis EA, Jones TW (2018) Reduction in hypoglycemia with the predictive low-glucose management system: a long-term randomized controlled trial in adolescents with type 1 diabetes. Diabetes Care 41:303–310.  https://doi.org/10.2337/dc17-1604 CrossRefPubMedGoogle Scholar
  11. 11.
    Georga EI, Príncipe JC, Fotiadis DI (2019) Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters. Med Biol Eng Comput 57(1):27–46.  https://doi.org/10.1007/s11517-018-1859-3 CrossRefPubMedGoogle Scholar
  12. 12.
    Abu-Rmileh A, Garcia-Gabin W (2010) Feedforward–feedback multiple predictive controllers for glucose regulation in type 1 diabetes. Comput Methods Prog Biomed 99(1):113–123.  https://doi.org/10.1016/j.cmpb.2010.02.010 CrossRefGoogle Scholar
  13. 13.
    Lunze K, Singh T, Walter M, Brendel MD, Leonhardt S (2013) Blood glucose control algorithms for type 1 diabetic patients: a methodological review. Biomed Signal Process Control 8(2):107–119.  https://doi.org/10.1016/j.bspc.2012.09.003 CrossRefGoogle Scholar
  14. 14.
    Nichols SP, Koh A, Storm WL, Shin JH, Schoenfisch H (2013) Biocompatible materials for continuous glucose monitoring devices. Chem Rev 113(4):2528–2549.  https://doi.org/10.1021/cr300387j CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Facchinetti A, Del Favero S, Sparacino G, Castle JR, Ward WK, Cobelli C (2014) Modeling the glucose sensor error. IEEE Trans Biomed Eng 61(3):620–629.  https://doi.org/10.1109/TBME.2013.2284023 CrossRefPubMedGoogle Scholar
  16. 16.
    Reiterer F, Polterauer P, Freckmann G, del Re L (2016) Identification of CGM time delays and implications for BG control in T1DM. In IFMBE Proc. XIV Mediterranean Conf on Med and Biol Eng Comp 2016, Paphos, Cyprus, pp 190–195.  https://doi.org/10.1007/978-3-319-32703-7_39 CrossRefGoogle Scholar
  17. 17.
    Du Y, Zhang W, Wang ML (2016) An on-chip disposable salivary glucose sensor for diabetes control. J Diabetes Sci Technol 10(6):1344–1352.  https://doi.org/10.1177/1932296816642251 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Laione F, Marques JLB (2005) Methodology for hypoglycaemia detection based on the processing, analysis and classification of the electroencephalogram. Med Biol Eng Comput 43(4):501–507.  https://doi.org/10.1007/BF02344732 CrossRefGoogle Scholar
  19. 19.
    Yamaguchi M, Kawabata Y, Kambe S, Wårdell K, Nystrom FH, Naitoh K, Yoshida H (2004) Non-invasive monitoring of gingival crevicular fluid for estimation of blood glucose level. Med Biol Eng Comput 42(3):322–327.  https://doi.org/10.1007/BF02344706 CrossRefPubMedGoogle Scholar
  20. 20.
    Yan K, Zhang D, Wu D, Wei H, Lu G (2014) Design of a breath analysis system for diabetes screening and blood glucose level prediction. IEEE Trans Biomed Eng 61(11):2787–2795.  https://doi.org/10.1109/TBME.2014.2329753 CrossRefPubMedGoogle Scholar
  21. 21.
    Liao Y-T, Yao H, Lingley A, Parviz B, Otis BP (2012) A 3-μW CMOS glucose sensor for wireless contact-lens tear glucose monitoring. IEEE J Solid State Circuits 47(1):335–344.  https://doi.org/10.1109/JSSC.2011.2170633 CrossRefGoogle Scholar
  22. 22.
    Chen L, Tse WH, Chen Y, McDonald MW, Melling J, Zhang J (2017) Nanostructured biosensor for detecting glucose in tear by applying fluorescence resonance energy transfer quenching mechanism. Biosens Bioelectron 91:393–399.  https://doi.org/10.1016/j.bios.2016.12.044 CrossRefPubMedGoogle Scholar
  23. 23.
    Novais S, Ferreira CIA, Ferreira MS, Pinto JL (2018) Optical fiber tip sensor for the measurement of glucose aqueous solutions. IEEE Photonics J 10(5):6803609–6803609.  https://doi.org/10.1109/JPHOT.2018.2869944 CrossRefGoogle Scholar
  24. 24.
    Pleitez MA, Lieblein T, Bauer A, Hertzberg O, Lilienfeld-Toal H, Mäntele W (2013) In vivo noninvasive monitoring of glucose concentration in human epidermis by mid-infrared pulsed photoacoustic spectroscopy. Anal Chem 85:1013–1020.  https://doi.org/10.1021/ac302841f CrossRefPubMedGoogle Scholar
  25. 25.
    Ghazaryan A, Ovsepian SV, Ntziachristos V (2018) Extended near-infrared optoacoustic spectrometry for sensing physiological concentrations of glucose. Front Endocrinol 9(112).  https://doi.org/10.3389/fendo.2018.00112
  26. 26.
    Vahlsing T, Delbeck S, Leonhardt S, Michael Heise H (2018) Noninvasive monitoring of blood glucose using color-coded photoplethysmographic images of the illuminated fingertip within the visible and near-infrared range: opportunities and questions. J Diabetes Sci Technol 12(6):1169–1177.  https://doi.org/10.1177/1932296818798347 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Yadav J, Rani A, Singh V, Murari BM (2015) Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy. Biomed Signal Process Control 18:214–227.  https://doi.org/10.1016/j.bspc.2015.01.005 CrossRefGoogle Scholar
  28. 28.
    Yilmaz T, Foster R, Hao Y (2019) Radio-frequency and microwave techniques for non-invasive measurement of blood glucose levels. Diagnostics 9(6):1–34.  https://doi.org/10.3390/diagnostics9010006 CrossRefGoogle Scholar
  29. 29.
    Greene J, Abdullah B, Cullen J, Korostynska O, Louis J, Mason A (2019) Non-invasive monitoring of glycogen in real-time using an electromagnetic sensor. In: Mukhopadhyay S, Jayasundera K, Postolache O (eds) Modern sensing technologies. smart sensors, measurement and instrumentation, vol 29, Springer, pp. 1–15. ISBN: 978-3-319-99539-7.  https://doi.org/10.1007/978-3-319-99540-3_1 Google Scholar
  30. 30.
    Amin B, Elahi MA, Shahzad A, Porter E, McDermott B, O’Halloran M (2019) Dielectric properties of bones for the monitoring of osteoporosis. Med Biol Eng Comput 57(1):1–13.  https://doi.org/10.1007/s11517-018-1887-z CrossRefPubMedGoogle Scholar
  31. 31.
    Potelon B, Quendo C, Carré J-L, Chevalier A, Person C, Queffelec P (2014) Electromagnetic signature of glucose in aqueous solutions and human blood. In Proc MEMSWAVE Conf 2014, La Rochelle, France, pp. 4–7Google Scholar
  32. 32.
    Juan CG, Bronchalo E, Torregrosa G, Ávila E, García N, Sabater-Navarro JM (2017) Dielectric characterization of water glucose solutions using a transmission/reflection line method. Biomed Signal Process Control 31(1):139–147.  https://doi.org/10.1016/j.bspc.2016.07.011 CrossRefGoogle Scholar
  33. 33.
    Lin T, Gu S, Lasri T (2017) Highly sensitive characterization of glucose aqueous solution with low concentration: application to broadband dielectric spectroscopy. Sensors Actuators A Phys 267:318–326.  https://doi.org/10.1016/j.sna.2017.10.029 CrossRefGoogle Scholar
  34. 34.
    Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Phys Med Biol 41(11):2271–2293CrossRefGoogle Scholar
  35. 35.
    Costanzo S, Cioffi V, Raffo A (2018) Complex permittivity effect on the performances of non-invasive microwave blood glucose sensing: enhanced model and preliminary results. In Proc WorldCIST'18 2018: Trends and advances in information systems and technologies, Naples, Italy, pp 1505–1511.  https://doi.org/10.1007/978-3-319-77712-2_146,Google Scholar
  36. 36.
    Parkhomenko MP, Savel’ev SV, von Gratovski SV (2017) Analysis of dielectric properties of blood and development of a resonator method for noninvasive measuring of glucose content in blood. J Commun Technol Electron 62(3):267–281.  https://doi.org/10.1134/S1064226917030159 CrossRefGoogle Scholar
  37. 37.
    Juan CG, Bronchalo E, Potelon B, Quendo C, Ávila-Navarro E, Sabater-Navarro JM (2019) Concentration measurement of microliter-volume water–glucose solutions using Q factor of microwave sensors. IEEE Trans Instrum Meas 68(7):2621–2634.  https://doi.org/10.1109/TIM.2018.2866743 CrossRefGoogle Scholar
  38. 38.
    Juan CG, Bronchalo E, Torregrosa G, Garcia A, Sabater-Navarro JM (2015) Microwave microstrip resonator for developing a non-invasive glucose sensor. Int J Comput Assist Radiol Surg (CARS) 10(1):172–173.  https://doi.org/10.1007/s11548-015-1213-2
  39. 39.
    Jean BR, Green EC, McClung MJ (2008) A microwave frequency sensor for non-invasive blood-glucose measurement. In Proc IEEE Sensors Appl Symp (SAS) 2008, Atlanta, GA, USA  https://doi.org/10.1109/SAS.2008.4472932
  40. 40.
    Yilmaz T, Foster R, Hao Y (2014) Towards accurate dielectric property retrieval of biological tissues for blood glucose monitoring. IEEE Trans Microw Theory Tech 62(12):3193–3204.  https://doi.org/10.1109/TMTT.2014.2365019 CrossRefGoogle Scholar
  41. 41.
    Choi H, Naylon J, Luzio S, Beutler J, Birchall J, Martin C, Porch A (2015) Design and in vitro interference test of microwave noninvasive blood glucose monitoring sensor. IEEE Trans Microw Theory Tech 63(10):3016–3025.  https://doi.org/10.1109/TMTT.2015.2472019 CrossRefGoogle Scholar
  42. 42.
    Raicu V, Feldman Y (2015) Dielectric relaxation in biological systems: physical principles, methods and applications. Oxford Univ. Press. ISBN: 9780199686513, Oxford.  https://doi.org/10.1093/acprof:oso/9780199686513.001.0001 CrossRefGoogle Scholar
  43. 43.
    nBio Research Group (2019) File with_without_plastic.zip. In: Glucolate nBio. Available via http://nbio.umh.es/glucolate/. Accessed 11 July 2019
  44. 44.
    Pozar D (1998) Microwave filters. In Pozar D (ed.) Microwave engineering, 2nd edn. John Wiley & Sons, pp. 422–498. ISBN: 0–471–17096-8Google Scholar
  45. 45.
    García H, Juan CG, Ávila-Navarro E, Bronchalo E, Sabater-Navarro JM (2019) Portable device based on microwave resonator for noninvasive blood glucose monitoring. In 2019 41st Annual Int Conf of the IEEE Eng Med Biol Society (EMBC), Berlin, GermayGoogle Scholar
  46. 46.
    Bray JR, Roy L (2004) Measuring the unloaded, loaded, and external quality factors of one- and two-port resonators using scattering-parameter magnitudes at fractional power levels. IEE Proc-Microw Antennas Propag 151(4):345–350.  https://doi.org/10.1049/ip-map:20040521 CrossRefGoogle Scholar
  47. 47.
    Kajfez D (2011) Q factor measurements using Matlab. Norwood: Artech House. ISBN: 9781608071616Google Scholar
  48. 48.
    nBio Research Group (2019) File all_data.zip. In: Glucolate nBio. Available via http://nbio.umh.es/glucolate/. Accessed 11 July 2019
  49. 49.
    Turgul V, Kale I (2016) A novel pressure sensing circuit for non-invasive RF/microwave blood glucose sensors. In 16th Mediterranean Microwave Symposium (MMS), Abu Dhabi, United Arab Emirates.  https://doi.org/10.1109/MMS.2016.7803818

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Systems Engineering and AutomationMiguel Hernández UniversityElcheSpain
  2. 2.Department of Materials Science, Optics and Electronic TechnologyMiguel Hernández UniversityElcheSpain
  3. 3.Department of Communications EngineeringMiguel Hernández UniversityElcheSpain
  4. 4.Department of Computer EngineeringMiguel Hernández UniversityElcheSpain
  5. 5.Department of Clinical MedicineMiguel Hernández UniversityElcheSpain

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