Internationalof Computational
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Systems
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Robotics,
Networking andArtificial Life
Vol. XX(Z);Month(Year),xx–yy6(4); March(2020),
pp.pp.
246–249
DOI: 10.1080/XXXXXXXXXXXXXX; ISSN
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DOI: https://doi.org/10.2991/jrnal.k.200221.001;
ISSNXXXX–XXXX
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https://www.atlantis-press.com/journals/ijndc
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Research Article

Non-invasive Blood Glucose Measurement based on
Impedance Spectrum
Tong Yin, Xiaoyan Chen*, Meng Du, Hongyi Yin
College of Electronic Information and Automation, Tianjin University of Science and Technology, China

ARTICLE INFOABSTRACT

Article History

Different from the traditional measurement approach and instruments of blood glucose concentration, we propose a novel
measuring method related to electrical impedance spectrum without invasion, and we have researched the correspondence
between blood glucose values and impedance parameters. Considering the skin contacting impedance and cytomembrane
conductivity feature, an equivalent circuit of human model was built in the MATLAB Simulink environment. We obtained
the impedance parameters of the model at 1 Hz–20 MHz by simulation. The measured data were analyzed by a Cole–Colemodel to derive the relationship between the impedance value and the blood glucose concentration. The results show thatthe electrical impedance is strong negative correlation to blood glucose concentration, as the blood glucose concentration
increases, the impedance value decreases. The conclusion of this research provides a theoretical basis for non-invasive blood
glucose surface-measuring.

Received 25 October 2018
Accepted 19 November 2018
Keywords
Blood glucose concentration
impedance spectroscopy
non-invasive glucose measurement© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
The blood glucose concentration is one of the key indicatorsreflecting the condition of diabetic patients. At present, the mostcommon testing method is acupuncture measurement before andafter meal invasively, which bring great pain to the patients withpotential dangers of infection [1]. Compared with the common
invasive method, non-invasive blood glucose concentration measuring has the advantages of no acupuncture and no pains withsimple and quick operation. Non-invasive blood glucose concentration measuring has an important application value of monitoring
the blood glucose change in time and of visualizing the abnormallyglucose metabolism rapidly [2].
This aim of this research is to explore a new way that detecting
the blood glucose concentration non-invasively by electrical
impedance scanning (EIS) method on the surface of body. The
concentration of electrolytes in the blood and tissues changes
with glucose concentration, which in turn causes an imbalancein electrolyte concentration between blood and tissue fluid. This
causes the ions to move in a directional manner, and the electricalproperties of the cytomembrane change with the concentration ofions passing through the cytomembrane. From a macro perspective, the impedance of the human body will also change accordingly. On the biological principle, it indicates a hopeful direction
for non-invasive blood glucose measuring.

Corresponding author. Email: cxywxr@tust.edu.cn*

2. THE RELATIONSHIP DEDUCTION OF
GLUCOSE AND CYTOMEMBRANECAPACITANCE
An increasing concentration of glucose in blood induces significant
reactions in blood and human tissues. It is known that the concentration of sodium decreases and potassium increases due to theactive transport of ions and water movement from the tissue andblood cells, such as erythrocytes, into the vascular system. It can beunderstood on the cellular level that glucose uptake through membrane transporters requires controlling the electrolyte balance tomaintain osmolality and cell sizes [3].
Shap et al. [4] proposed and verified the relationship between
cytomembrane capacitance (Cmem) and blood glucose concentration (Cglu) by Michaelis–Menten equation asCmem
0.53= 1+
8.57 		  
Cmem,01+Cglu(1)

According to the experimental data in Beving et al. [5], thecytomembrane capacitance value is 1 mF/cm2 when the blood glucose concentration is zero. In this paper, a rectangular electrodeof 3 cm2 is selected for simulation test, so Cmem,0 = 3 mF. We bringCmem,0 into the Equation (1), and obtain the relationship between
the cytomembrane capacitance values corresponding to the blood
glucose levels of different concentrations, as shown in Equation (2),and as shown in Figure 1.

T. Yin et al. / Journal of Robotics, Networking and Artificial Life 6(4) 246–249		   Cmem = 3 +

1.59

8.57
1+
Cglu

(2)

From Figure 1, at the change stage of 3–10 mmol/L in human blood
glucose concentration, the cytomembrane capacitance changes very
rapidly, and this change is more obvious than that caused by othersubstances to the cytomembrane capacitance. When the cytomembrane capacitance changes, the influence of other substances canbe ignored. Therefore, the impedance index can be used to indicate
the blood glucose reasonably.

3. EQUIVALENT CIRCUIT MODEL
To simulate the effects of different blood glucose concentrations on
impedance spectroscopy measurements, we established a humanequivalent circuit model. In an alternating electric field, the biological tissue has greater impedance at the lower frequency. The capacitive impedance characteristic of biological tissues was discoveredby scholar Philippson in 1920, who established a corresponding
bio-impedance equivalent circuit model.
Due to the detecting electrodes and skin contacting, it is also
necessary to consider the effect of contact impedance. Therefore,
we established a novel human equivalent circuit model as shownin Figure 2.

Rd represents the contact impedance, Ri is the internal liquid
resistance of the human body, Re is the liquid resistance, andCmem is the cytomembrane capacitance. According to the experimental data in Chen et al. [6], we set Rd = 1 kΩ, Ri = 500 Ω,
Re = 500 Ω.

4. DATA PROCESSING AND
RESULT ANALYSISIn order to accurate describe the equivalent circuit model of biological tissues, Cole and Cole [7,8] have conducted in-depth researchin this regard. They summed up a set of actual biological tissue
electrical impedance theory, the Cole–Cole theory, and used it todescribe the dielectric model of biological tissue. The basic contentof the Cole–Cole theory is that the trajectory of the electrical tissue’s electrical impedance in the complex plane is a circular arc in
the fourth quadrant, and the center of the circle is not on the real
axis but in the first quadrant.
The measurement of bio-impedance is essentially to obtain thecharacteristic parameters in the impedance trajectory map. It
can be seen from the above analysis that as long as an excitationelectric signal of a certain frequency is applied to the biologicaltissue and the output signal flowing through the biological tissue
is simultaneously measured, the impedance amplitude at thefrequency can be calculated by the relevant demodulation algorithm. If the excitation power signal is operated in a sweep mode,the entire impedance circle can be obtained by curve fitting. On
the impedance plot, the impedance value at any frequency point
can be obtained. Based on this theory, the Cole–Cole chart and its
electrical characteristic parameters of the human equivalent circuit model are obtained from the real and imaginary parts of theimpedance and the phase and impedance values. Corresponding
Cole–Cole biological tissue electrical impedance characteristic
equation is:
Z=R +		  ∞Figure 1 | The relationship between Cglu and Cmem. As the Cglu increases,Cmem shows an upward trend.

Figure 2 | Human equivalent circuit model.

247

R0 − R∞1 
1 + jw
2p f c(3)The Im axis represents the imaginary part of the impedance, andthe Re axis represents the real part value. fc represents the frequency value when the imaginary part is maximum. R0 represents
the electrical impedance of biological tissue at a frequency of zero.
R∞ represents the electrical impedance of biological tissue at
infinite frequency. These scatter points in the figure are calculated at frequencies from 10 Hz to 20 MHz, in which the impedance changes from 1500 to 1250 Ω. According to the measured
real and imaginary parts of the impedance, the Cole–Cole circle
is fitted through Python using the weighted average method,
and the following Figure 3 shows the Cole–Cole circle fitted atCglu = 6 mmol/L. Fitted into a circle by these scatter plots and it canbe seen that it is normative.
The change trend of the characteristic parameter fc has a great correlation with the change of the blood glucose concentration. From
the data in Table 1, the magnitude of fc has an inverse correlation
with the magnitude of the blood glucose concentration. The higher
blood glucose concentration, the smaller value of fc corresponding
to the impedance spectrum.

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248

5. CONCLUSION

Figure 3 | The Cole–Cole impedance circle.
Table 1 | Impedance spectrum characteristic parameters andcorresponding blood glucose concentration
Diagnosisfc(Hz)

Cglu(mmol/L)

Hypoglycemia
Hypoglycemia
Normal glucose
Abnormal glucose metabolism
Abnormal glucose metabolism
Diabetes

48.22
45.40
43.55
42.24
41.27
40.52

24
68
1012

As the glucose concentration changes, the binding of glucose tothe transporter changes the capacitance of the cytomembrane. The
transport of glucose into cells by transporters is a macroscopicchange in the impedance of the human body. Considering the skincontacting impedance and cytomembrane conductivity feature,an equivalent circuit of human model was built in the MATLAB
Simulink environment. We obtained the impedance parameters ofthe model at 1 Hz–20 MHz by simulation. The measured data wereanalyzed by a Cole–Cole model and the blood glucose concentration, and a power function fitting was performed by MATLAB toobtain a correspondence between Cglu and fc as shown in Equation (4).
The results show that the electrical impedance is strong negative
correlation to blood glucose concentration, especially the characteristic frequency fc, as the blood glucose concentration increases,
the impedance value decreases. The conclusion of this research
provides a theoretical basis for non-invasive blood glucose surface-measuring.

CONFLICTS OF INTEREST
The authors declare they have no conflicts interest.

ACKNOWLEDGMENTS
This paper was supported by a grant from the National Natural
Science Foundation of China (61301246, 41704131) and Tianjin
Science and Technology Key Project (18YFZCGX00360).

REFERENCES
[1]
[2]
[3]
Figure 4 | Power function fitting.

[4]

A power function fitting was performed by MATLAB to obtain acorrespondence between Cglu and fc:[5]		    Cglu = 3 ´ 1017 f c-10.2 (4)Power function fitting results are shown in Figure 4.
The coefficient of determination R2 = 0.9981, which means thatthe fitting effect is excellent. The characteristic frequency fc in theimpedance spectrum can reflect the change of human blood glucose concentration to a certain extent, and it is possible to realize non-invasive blood glucose monitoring based on impedance
spectrum method.

[6]
[7]
[8]

A.G.A. Aggidis, J.D. Newman, G.A. Aggidis, Investigating pipeline and state of the art blood glucose biosensors to formulate
next steps, Biosens. Bioelectron. 74 (2015), 243–262.
D.C. Klonoff, Noninvasive blood glucose monitoring, Diabetes
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P. Shao, X. Zhang, C. Zouxue, Biological tissue impedance modelfor blood glucose monitoring, J. Laser Biol. 25 (2016), 250–256.
H. Beving, L.E.G. Eriksson, C.L. Davey, D.B. Kell, Dielectric
properties of human blood and erythrocytes at radio frequencies(0.2–10 MHz); dependence on cell volume fraction and medium
composition, Eur. Biophys. J. 23 (1994), 207–215.
Y. Chen, Y. Cao, Y. Gao, S.H. Pun, P.U. Mak, M.I. Vai, et al.,Experimental study on contact impedance of intra-body communication transmitter, J. Biomed. Eng. Res. 29 (2010), 13–17.
K.S. Cole, R.H. Cole, Dispersion and absorption in dielectrics I,
Alternating current characteristics, J. Chem. Phys. 9 (1941),
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Direct current characteristics, J. Chem. Phys. 10 (1942), 98–105.

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249

AUTHORS INTRODUCTION
Ms. Tong Yin

Dr. Meng Du
She received the B.S. degree in YantaiUniversity, China, in 2017. Now she is
the master course student in Tianjin
University of Science & Technology. Her
research field is control engineering.

Dr. Xiaoyan Chen
She received the M.S. degree fromthe Tianjin University of Science andTechnology, in 1999, and the PhD degree
in measurement technology and automation devices from Tianjin University,in 2009. From 2009 to 2015, she held
a post-doctoral position at Tianjin
University. She was invited by RPI, USA,as a Visiting Scholar, from 2009 to 2010,and Kent, U.K., in 2012. She is currently a Professor and anAdvisor of Postgraduate and Doctorate students with theTianjin University of Science and Technology.

He received the B.S. degree in computerscience and technology from the Hebei
University of Science and Technology,
China, in 2005, the M.S. degree in computer application from Yanshan University,China, in 2009, and the Ph.D. degree in control science and engineering from Tianjin
University, China, in 2013. He is currently
with the College of Electronic Information and Automation,Tianjin University of Science and Technology. His current interests include multi-phase flow parameter measurement, flow pattern identification, time series analysis, and machine learning.

Mr. Hongyi Yin

He received the B.S. degree in communication engineering from Qiqihar University,
China, in 2017. He is currently pursuing
the M.S. degree in control science and engineering with the Department of Electronic
Information and Automation, Tianjin
University of Science and Technology,
China, under the supervision of Dr. M. Du.
His current research interests include
multi-phase flow parameter measurement, flow pattern identification, and machine learning.