Skip to main content

Advertisement

SpringerLink
Log in

Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring

  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

The aim of this study is to establish a real-time self-adjusting calibration algorithm to compensate for signal drift and sensitivity attenuation of subcutaneous implantable glucose sensors. A real-time self-adjusting in vivo calibration method was designed based on the one-point calibration model. The current signal was compensated in real-time and the sensitivity was calibrated regularly. The least squares method was used to fit the initial parameters of the model, and then, the in vivo monitored current data was calibrated. Comparing with the mean absolute relative difference (MARD) of the blood glucose concentration by the traditional one-point calibration model (22.85 ± 5.76%), the MARD of the blood glucose concentration calibrated by the real-time self-adjusting in vivo calibration method was 6.28 ± 2.31%. The accuracy of the dynamic blood glucose monitoring was effectively improved. This calibration algorithm could compensate the signal drift in real time and correct sensitivity regularly to improve the accuracy of dynamic glucose monitoring, thus significantly enhancing diabetic self-management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Hooijdonk, R. T. V., Leopold, J. H., Winters, T., Binnekade, J. M., Juffermans, N. P., Horn, J., Fischer, J. C., Dongenlases, E. C. V., & Schultz, M. J. (2015). Point accuracy and reliability of an interstitial continuous glucose-monitoring device in critically ill patients: a prospective study. Critical Care,19, 1–201.

    Article  Google Scholar 

  2. Thomas, F., Signal, M., & Chase, J. G. (2015). Using continuous glucose monitoring data and detrended fluctuation analysis to determine patient condition: a review. J Diabetes Sci Technol,9(6), 1327–1335.

    Article  Google Scholar 

  3. Beers, C. A. J. V., Kleijer, S. J., Serné, E. H., Snoek, F. J., Kramer, M. H. H., & Diamant, M. (2015). Design and rationale of the IN CONTROL trial: the effects of real-time continuous glucose monitoring on glycemia and quality of life in patients with type 1 diabetes mellitus and impaired awareness of hypoglycemia. Bmc Endocrine Disorders,15, 42.

    Article  Google Scholar 

  4. Mutyala, S., & Mathiyarasu, J. (2014). Direct electron transfer at a glucose oxidase-chitosan-modified vulcan carbon paste electrode for electrochemical biosensing of glucose. Applied Biochemistry And Biotechnology,172(3), 1517–1529.

    Article  CAS  Google Scholar 

  5. Srinivasan, G., Chen, J., Parisi, J., Brueckner, C., Yao, X., & Lei, Y. (2015). An injectable PEG-BSA-coumarin-GOx hydrogel for fluorescence turn-on glucose detection. Applied Biochemistry And Biotechnology,177(5), 1115–1126.

    Article  CAS  Google Scholar 

  6. Aust, H., Dinges, G., Nardi-Hiebl, S., Koch, T., Lattermann, R., Schricker, T., & Eberhart, L. H. (2014). Feasibility and precision of subcutaneous continuous glucose monitoring in patients undergoing CABG surgery. Journal of Cardiothoracic & Vascular Anesthesia,28, 1264–1272.

    Article  Google Scholar 

  7. Leal, Y., Garcia-Gabin, W., Bondia, J., Esteve, E., Ricart, W., & Vehí, F. R. (2010). Real-time glucose estimation algorithm for continuous glucose monitoring using autoregressive models. Journal of Diabetes Science & Technology,4(2), 391–403.

    Article  Google Scholar 

  8. Bindra, D. S., Zhang, Y., Wilson, G. S., Sternberg, R., Thévenot, D. R., Moatti, D., & Reach, G. (1991). Design and in vitro studies of a needle-type glucose sensor for subcutaneous monitoring. Analytical Chemistry,63, 1692–1696.

    Article  CAS  Google Scholar 

  9. Bailey, T. S., Chang, A., & Christiansen, M. (2015). Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. J Diabetes Sci Technol,9(2), 209–214.

    Article  CAS  Google Scholar 

  10. Bailey, T. S., Ahmann, A., Brazg, R., Christiansen, M., Garg, S., Watkins, E., Welsh, J. B., & Lee, S. W. (2014). Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor. Diabetes Technol Ther,16(5), 277–283.

    Article  CAS  Google Scholar 

  11. Crane, B. C. and Paterson, W. (2014) Glucose sensor calibration, EP2432894.

  12. Hoss, U., & Budiman, E. S. (2017). Factory-calibrated continuous glucose sensors: the science behind the technology. Diabetes Technology & Therapeutics,19, S-44–S-50.

    Article  Google Scholar 

  13. Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., Mader, J. K., Place, J., Boscari, F., & Pieber, T. R. (2017). Accuracy of two continuous glucose monitoring systems: a head-to-head comparison under clinical research centre and daily life conditions. Earth & Planetary Science Letters,471, 42–53.

    Article  Google Scholar 

  14. Facchinetti, A., Del, F. S., Sparacino, G., Castle, J. R., Ward, W. K., & Cobelli, C. (2014). Modeling the glucose sensor error. IEEE transactions on bio-medical engineering,61, 620–629.

    Article  Google Scholar 

  15. Shin, J. J., Holtzclaw, K. R., Dangui, N. D., Kanderian, S., Mastrototaro, J. J., & Hong, P. I. (2011). Real time self-adjusting calibration algorithm. US.

  16. Hoss, U., Budiman, E. S., Liu, H., & Christiansen, M. P. (2014). Feasibility of factory calibration for subcutaneous glucose sensors in subjects with diabetes. Journal of Diabetes Science & Technology,8, 89–94.

    Article  Google Scholar 

  17. Bequette, B. W. (2010). Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms. Journal of Diabetes Science & Technology,4(2), 404–418.

    Article  Google Scholar 

  18. Mahmoudi, Z., Johansen, M. D., Christiansen, J. S., & Hejlesen, O. (2014). Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm. Journal of Diabetes Science & Technology,8, 709–712.

    Article  CAS  Google Scholar 

  19. Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2018). Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives. Biosensors-Basel,8(1), 24.

  20. Akintola, A. A., Noordam, R., Jansen, S. W., de Craen, A. J., Ballieux, B. E., Cobbaert, C. M., Mooijaart, S. P., Pijl, H., Westendorp, R. G., & Van, H. D. (2015). Accuracy of continuous glucose monitoring measurements in normo-glycemic individuals. Plos One,10, e0139973.

    Article  Google Scholar 

  21. Yue, X. Y., Zheng, Y., Cai, Y. H., Yin, N. N., & Zhou, J. X. (2013). Real-time continuous glucose monitoring shows high accuracy within 6 hours after sensor calibration: a prospective study. Plos One,8, e60070.

    Article  CAS  Google Scholar 

  22. Asghar, N., Mustafa, G., Yasinzai, M., Al-Soud, Y. A., Lieberzeit, P. A., & Latif, U. (2019). Real-time and online monitoring of glucose contents by using molecular imprinted polymer-based IDEs sensor. Applied biochemistry and biotechnology. Available from: https://link.springer.com/article/10.1007/s12010-019-03049-3.

  23. Facchinetti, A. (2016). Continuous glucose monitoring sensors: past, present and future algorithmic challenges. Sensors,16, 2093.

  24. El, Y. J., Castle, J. R., Engle, J. M., Massoud, R. G., & Ward, W. K. (2010). Continuous glucose monitoring in subjects with type 1 diabetes: improvement in accuracy by correcting for background current. Diabetes Technology & Therapeutics,12, 921–928.

    Article  Google Scholar 

  25. Rebrin Jr., K., S. N., & Steil, G. M. (2010). Use of subcutaneous interstitial fluid glucose to estimate blood glucose: revisiting delay and sensor offset. Journal of Diabetes Science & Technology,4(5), 1087–1098.

    Article  Google Scholar 

  26. Vaddiraju, S., Burgess, D. J., Tomazos, I., Jain, F. C., & Papadimitrakopoulos, F. (2010). Technologies for continuous glucose monitoring: current problems and future promises. J Diabetes Sci Technol,4, 1540–1562.

    Article  Google Scholar 

  27. Zhang, Y. (2011) Catheter-free implantable needle biosensor, WO2006062668.

  28. Shao, J., Lin, M., Li, Y., Xue, L., Liu, J., Liang, J., & Yao, H. (2012). In vivo blood glucose quantification using Raman spectroscopy. Plos One,7, e48127.

    Article  CAS  Google Scholar 

  29. Doniger, K. J., Budiman, E. S. and Hayter, G. A. (2014) Method and device for early signal attenuation detection using blood glucose measurements, US2014200427.

  30. Van, d. S. D., Garai, E., Zavaleta, C., & Gambhir, S. S. (2011). A hybrid least squares and principal component analysis algorithm for Raman spectroscopy. Conf Proc IEEE Eng Med Biol Soc,2011, 6971–6974.

    Google Scholar 

  31. Ahn, J., Zhang, Z., & Sternad, D. (2016). Noise induces biased estimation of the correction gain. Plos One,11, e0158466.

    Article  Google Scholar 

  32. Facchinetti, A., Sparacino, G., Guerra, S., Luijf, Y. M., DeVries, J. H., Mader, J. K., Ellmerer, M., Benesch, C., Heinemann, L., Bruttomesso, D., Avogaro, A., Cobelli, C., & Consortium, A. P. H. (2013). Real-time improvement of continuous glucose monitoring accuracy. Diabetes Care,36, 793–800.

    Article  CAS  Google Scholar 

  33. Andrea, F., Giovanni, S., & Claudio, C. (2011). Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring. IEEE transactions on bio-medical engineering,58, 2664–2671.

    Article  Google Scholar 

  34. Shin, J., Park, H., Cho, S., Nam, H., & Lee, K. J. (2014). A correction method using a support vector machine to minimize hematocrit interference in blood glucose measurements. Computers in Biology & Medicine,52, 111–118.

    Article  Google Scholar 

  35. Rossetti, P., Bondia, J., Vehi, J., & Fanelli, C. G. (2010). Estimating plasma glucose from interstitial glucose: the issue of calibration algorithms in commercial continuous glucose monitoring devices. Sensors,10, 10936–10952.

    Article  CAS  Google Scholar 

  36. Mahmoudi, Z., Jensen, M. H., Johansen, M. D., Christensen, T. F., Tarnow, L., Christiansen, J. S., & Hejlesen, O. (2014). Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia. Diabetes Technology & Therapeutics,16(10), 667–678.

    Article  CAS  Google Scholar 

  37. Feldman, B. J. and Mcgarraugh, G. V. (2014). Method of calibrating an analyte-measurement device, and associated methods, devices and systems, US20050239154.

  38. Hayter, G. A., Doniger, K. J., Budiman, E. S., Zhang, S. and Mazza, J. C. (2017). Method and system for providing calibration of an analyte sensor in an analyte monitoring system, US2019254575.

  39. Lalantha, L., English, S. W., Hood, T., Karen, C., Allen, J. M., Kavita, K., Wilinska, M. E., Marianna, N., Ahmad, H., & Evans, M. L. (2014). Accuracy of subcutaneous continuous glucose monitoring in critically ill adults: improved sensor performance with enhanced calibrations. Diabetes Technology & Therapeutics,16, 97.

    Article  Google Scholar 

  40. Barcelo-Rico, F., Diez, J.-L., Rossetti, P., Vehi, J., & Bondia, J. (2013). Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring. Ieee Journal Of Biomedical And Health Informatics,17(3), 530–538.

    Article  Google Scholar 

  41. Barcelo-Rico, F., Bondia, J., Luis Diez, J., & Rossetti, P. (2012). A multiple local models approach to accuracy improvement in continuous glucose monitoring. Diabetes Technology & Therapeutics,14(1), 74–82.

    Article  CAS  Google Scholar 

  42. Choleau, C., Klein, J. C., Reach, G., Aussedat, B., Demaria-Pesce, V., Wilson, G. S., Gifford, R., & Ward, W. K. (2002). Calibration of a subcutaneous amperometric glucose sensor implanted for 7 days in diabetic patients - part 2. Superiority of the one-point calibration method. Biosensors & Bioelectronics,17, 647–654.

    Article  CAS  Google Scholar 

Download references

Funding

This study was funded by the National Key Research and Development Program of China (grant number 2018YFC2000804), National Natural Science Foundation of China (grant number 81671850), and the Fundamental Research Funds for the Central Universities (grant number 2019CDCG0014).

Author information

Authors and Affiliations

  1. Key Laboratory for National Defense Science and Technology of Innovative Micro-nano Devices and System Technology, Chongqing University, Chongqing, 400030, People’s Republic of China

    Ziru Jia, Lijuan Huang, Hongying Liu, Yonghong Huang, Wang Li, Xitian Pi & Xiaolin Zheng

  2. Chongqing Engineering Research Center of Medical Electronics, Chongqing, 400030, People’s Republic of China

    Hongying Liu

  3. Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing, 400030, People’s Republic of China

    Xitian Pi

Authors
  1. Ziru Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yonghong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xitian Pi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaolin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hongying Liu or Xitian Pi.

Ethics declarations

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 Ministry of Health, Chongqing (ECMHC), and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed Consent

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

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, Z., Huang, L., Liu, H. et al. Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring. Appl Biochem Biotechnol 190, 1163–1176 (2020). https://doi.org/10.1007/s12010-019-03142-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-019-03142-7

Keywords

Search

Navigation