Error grid analysis evaluation of noninvasive blood glucose monitoring system of diabetic Covid-19 patients

Document Type : Research Paper


Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq


Due to the life-threatening dangers of diabetic disease and the rapid spread of the Corona pandemic, the demand for continuous glucose monitoring systems increases, especially that complemented with telemedicine technologies. During and after the corona pandemic, the number of diabetes patients is anticipated to rise rapidly. This study aims to learn the interaction between diabetes and COVID-19 and the health complication, owing to control blood glucose levels to decrease these complications. Careful blood sugar control is essential because the poorer health results are strongly linked with greater blood sugar levels in COVID-19 infection patients. The non-invasive glucose detection system is vital to control diabetic COVID-19 patients'  health cases. The non-invasive blood glucose monitoring system is based on acousto-optic Raman-Nath interaction using 2MHz ultrasound and 980nm IR laser.  Clark's Error Chart and Parkes Error Grid are used for evaluating the non-invasive blood glucose monitoring system and show a promising evaluation result.


[1] Y. Adu-Agyeiwaah, M.B. Grant and A.G. Obukhov, The potential role of osteopontin and furin in worsening
disease outcomes in COVID-19 patients with pre-existing diabetes, Cells 9(11) (2020) 2528.
[2] C. Akarsu, M. Karabulut, H. Aydin, N.A. Sahbaz, A.C. Dural, D. Yegul and G.T. Adas, Association between
acute pancreatitis and COVID-19: could pancreatitis be the missing piece of the puzzle about increased mortality
rates?, J. Invest. Surgery 35 (2020) 1–7.
[3] L.N. Bachache, J.A. Hasan and A.Q. Al-Neami, Acousto-optic Design to Measure Glucose Level for Diabetic
Patients Non-invasively, In J. Phys. Conf. Ser. 1818(1) (2021) 012147.[4] Z.T. Bloomgarden, Diabetes and COVID-19, J. Diabetes 12(4) (2020) 347–348.
[5] M.M. Burlew, E.L. Madsen, J.A. Zagzebski, R.A. Banjavic and S.W. Sum, A new ultrasound tissue-equivalent
material, Radiol. Phys. J. 134(2) (1980) 517–520.
[6] E.N. Carlsen, Ultrasound physics for the physician a brief review, J. Clinic. Ultrasound 3 (1975) 69–75.
[7] Y. Cheng, L. Yue, Z. Wang, J. Zhang and G. Xiang, Hyperglycemia associated with lymphopenia and disease
severity of COVID-19 in type 2 diabetes mellitus, J. Diabetes . Compl. 35(2) (2021) 107809.
[8] W.L. Clarke, The original Clarke error grid analysis (EGA), Diabetes Technol. Therap. 7(5) (2005) 776–779.
[9] D.J. Cox, L.A. Gonder-Frederick, B.P. Kovatchev, D.M. Julian and W.L. Clarke, Understanding error grid
analysis, Diabetes Care 20(6) )1997( 911.
[10] G. Jin, X. Zhang, W. Fan, Y. Liu and P. He, Design of non-contact infrared thermometer based on the sensor of
MLX, Open Autom. Control Syst. J. 7(1) (2015) 8–20.
[11] S. Gl¨aser, S. Kr¨uger, M. Merkel, P. Bramlage and F.J. Herth, Chronic obstructive pulmonary disease and diabetes
mellitus: a systematic review of the literature, Respiration 89(3) (2015) 253–264.
[12] I. Kapoor, H. Prabhakar and C. Mahajan, Introduction: History of Coronavirus Disease Pandemic, Clinical
Synopsis of COVID-19, Springer, Singapore, (2020) 1–4.
[13] S.A. Lee, M.C. Jobe, A.A. Mathis, J.A. Gibbons, Incremental validity of coronaphobia: Coronavirus anxiety
explains depression, J. Anxiety Disord 74 (2020) 102268.
[14] J.A. McGrath, R.A.J. Eady and F.M. Pope, Anatomy and organization of human skin, Rook’s Textbook of
Dermatology 1 (2016) 2–3.
[15] Melexis inspired engineering, Datasheet Single and Dual Zone Infra Red Thermometer in TO-39, MLX90614
family datasheet, 2019.
[16] E. Merzon, I. Green, M. Shpigelman, S. Vinker, I. Raz, A. Golan-Cohen and R. Eldor, Haemoglobin A1c is a
predictor of COVID-19 severity in patients with diabetes, Diabetes/metabol. Res. Rev. 37(5) (2021) e3398.
[17] S. Nazar Haddad and R. Istepanian, A feasibility study of mobile phone text messaging to support education and
management of type 2 diabetes in Iraq, Diabetes Technol. Therap. 16(7) (2014) 454–459.
[18] F.J. Pasquel and G.E. Umpierrez, Individualizing inpatient diabetes management during the Coronavirus disease
2019 pandemic, J. Diabetes Sci. Technol. 14(4) (2020) 705–707.
[19] M.C. Petersen G.I. Shulman, Mechanisms of insulin action and insulin resistance, Physiol. Rev. 98(4) (2018)
[20] A. Pf¨utzner, D.C. Klonoff, S. Pardo and J.L. Parkes, Technical aspects of the Parkes error grid, J. Diabetes Sci.
Technol. 7(5) (2013) 1275–1281.
[21] B. Shirin, Diabetes mellitus and gestational diabetes mellitus, J. Paediatric Surg. Bangl. 5(1) (2015) 30–35.
[22] A.K. Singh, R. Gupta, A. Ghosh and A. Misra, Diabetes in COVID-19: Prevalence, pathophysiology, prognosis
and practical considerations, Diabetes Metab Syndr. 14(4) (2020) 303–310.
[23] A.K. Singh, R. Gupta, A. Ghosh and A. Misra, Diabetes in COVID-19: Prevalence, pathophysiology, prognosis
and practical considerations, Diabetes Metabol. Syndr. Clinical Res. Rev. 14(4) (2020) 303–310.
[24] G. Wilcox, Insulin and insulin resistance, Clinical Bioch. Rev. 26(2) (2005) 19.
Volume 13, Issue 1
March 2022
Pages 3697-3706
  • Receive Date: 07 November 2021
  • Revise Date: 16 December 2021
  • Accept Date: 04 January 2022
  • First Publish Date: 02 February 2022