Noninvasive Blood Glucose Monitoring System Based on Distributed Multi-Sensors Information Fusion of Multi-Wavelength NIR

doi:10.4236/eng.2013.510B114

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Engineering, 2013, 5, 553-560

http://dx.doi.org/10.4236/eng.2013.510B114 Published Online October 2013 (http://www.scirp.org/journal/eng)

Noninvasive Blood Glucose Monitoring System Based on

Distributed Multi-Sensors Information Fusion of

Multi-Wavelength NIR*

Bo Zeng, Wei Wang#, Na Wang, Funing Li, Fulong Zhai, Lintao Hu

School of Information Science and Engineering, Lanzhou Universit y, Lanzhou, China

Email: [email protected], #[email protected]

Received 2013

ABSTRACT

In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser

multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a

multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root-

Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coef-

ficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion

of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri-

buted multi-sensors, shows better prediction accuracy.

Keywords: Noninvasive Glucose Monitoring; NIR Arrays Signals Fusion; BP-Artificial Neural Network

1. Introduction

Diabetes has become a modern disease and more than

150 million people are suffering from it all around the

world [1]. In order to prevent complication, the tight

blood glucose level control is very essential. Currently,

patients are recommended to monitor their blood glucose

level via an invasive finger-tip stick method, this way

will inevitably bring patients pain and infection [2]. In

order to avoid the weakness of invasive method, a num-

ber of noninvasive measurements occur r ed, like middle-

infrared emission spectroscopy [3,4], Near Infrared (NIR)

spectroscopy [5,6]. The main drawback of infrared mea-

surement is its accuracy and stability. In our past work,

an infrared noninvasive blood glucose measurement sys-

tem based on multi-sensors and Mixture of Experts (ME)

[7] was designed. ME algorithm greatly improved the

precision of noninvasive blood glucose measurement. In

the noninvasive blood glucose monitoring system based

on distributed multi-sensors information fusion of mul-

ti-wavelength NIR, this system was designed on the pur-

pose of continuous blood glucose monitoring for patients

at home and hospital [2]. Another advantage of this sys-

tem was the introduction of distributed multi-sensors id ea,

this change has been proved to be better improvement for

blood glucose prediction accuracy. In this distributed

multi-sensors system, a multi-sensors information fusion

model, Back Prop agation Artificial N eural Network ( BP-

ANN), was appli e d.

2. Noninvasive Blood Glucose Monitoring

Model

The experimental data for this research was obtained via

NIR based on laser arrays as shown in Figure 1. The

NIR light source consists of 3*3 laser diodes arrays op-

erating at output powers of 5 mW [8].

2.1. Hardware Design

Wavelengths selection: According to the specialty of

glucose molecular structure and absorption specialty,

second order times frequency absorption exists between

1100 - 1300 nm, and first order times frequency absorp-

tion exists between 1500 - 1800 nm. Other components

in blood, such as hemoglobin and water contain groups

of hydrogen which can trigger NIR absorption as well.

Water absorption peaks are mainly distributed between

1440 - 1460 nm and between 1940 - 1960 nm, those

regions should be avoided in wavelength selection. Be-

tween 1400 - 1800 nm, water absorption only exists at

1787 nm, fat and protein exist no absorption peak in this

region. Therefore, 1400 - 1800 nm region is suitable for

measurement wavelength selection.

Measurement sites selection: In noninvasive blood

The work is supported b y National Nature Science Fund of China (No

30970876, 81141076).

*Corresponding a uthor.

B. ZENG ET AL.

554

Figure 1. Framework of the multiwavelength arrays monitoring system.

glucose measurement, the selection of an ideal measure-

ment site is essential. Four factors should be considered :

First, for the convenience of measurement, the measure-

ment site should be exposed outside; Second, in order to

decrease the influence from outside factors, individual

difference like gender, age and physical state should be

low; Third, the measurement site should contain rich

blood and the interference from other components is low;

Last, NIR light can transmit measurement site easily. In

the noninvasive blood glucose monitoring system based

on distributed multi-sensors, left ear lobe, right ear lobe

and the right hand part between thumb and index finger

are selected as distributed measurement sites.

Circuit design: six channels of laser-driving and photo-

electronic amplification circuit are designed in this

system. Figure 2 shows the laser-driving circuit, LD-

driving and the part of photodiode feedback circuit help

to maintain laser operate with output power of 5 mW and

forward current of 30 mA. By adjusting POT1, forward

current can be adjusted between 0 - 120 mA. Figure 3

shows the photoelectrical signal amplification c ircuit. By

applying an operational amplifier POT1 and some af-

filiated resisters and capacities, the obtained spectral

signal can be amplified by 1000 to 3V, the amplified

signal is transferred to plug seat JP2-1 which connected

to AD converter.

2.2. Software Design

1) Data acquisition and save: Amplified spectral sig-

nal is first conveyed to AD converter, which supports

Labview drive. So the converted data can be displayed

on the screen programmed by Labview language. Figu r e

4 shows the interface of acquired spectral data from six

channels. At each channel, the average value of 10

successive acquired data is calculated as displaying infor-

mation.

2) Distributed multi-sensors information fusion based

on BP-ANN: BP-ANN is one kind of supervised learning

network. There are two phases of positive transmitting

processing and error reverse transmitting processing in

the study processing of BP-ANN.

A three layers BP neural network (Figure 5) is applied

to model fuse multi-sensors information and predict the

blood glucose concentration in experiments. Least Square

function is adopted as error function in the training of BP

network, which writes as:

( )

rk rk

= =

−

∑∑

(2)

is the output of note k; yk is the corresponding de-

sired output; q is the number of output notes, and m is the

number of training samples. For the output weight, the

revise value

w∆

is:

jkk k

ηδ

∆=

(3)

w is the weight connecting the number k output

note and the number j hidden note:

is the study speed,

is output value at the number k output note;

the gradient factor. For the hidden layer, revise value

writes as:

ijj j

∆=

(4)

is the weight connecting the number j hidden

note and the number i input note;

is the output at the

number j hi dden note .

Figure 6 shows the training curve in the concentration

prediction of blood glucose, after training of 10 times,

the goal is reached and the performance can reach 0.088

mmol/L.

3) Prediction result display based on Labview: The

acquired data displaying and predicted blood glucose

concentration value interface is shown in Figure 4. On

the interface, 8 measurement methods options are given.

Corresponding blood glucose prediction program will be

launched by clicking any switch, and the predicted value

will be displayed as well.

B. ZENG ET AL.

555

Figure 2. Laser-driving circuit.

Figure 3. Photoelectrical signal amplification circuit.

Figure 4. System interface of calculated blood glucose concentration.

B. ZENG ET AL.

556

Figure 5. BP framework for multisensor information fusion.

Figure 6. Training curve in human blood glucose concent ration predict i on.

3. Experiments

In both glucose samples experiment and noninvasive

blood glucose measurement experiment, 7 measurement

methods with single, two and three wavelengths were

applied, which were named A, B, C, A + B, A + C, B +

C and A + B + C method respectively.

3.1. Glucose Samples Experiment

Process of glucose experiment: All samples were mea-

sured in a 5 mm quartz cell and the temperature in the

sample cell was controlled at about 37˚C. Two sets of 2 0

samples were prepared with varying concentrations,

ranging from 10 to 200 mg/dL in 10 mg/dL steps. More

than 500 original data sets were obtained, after eliminat-

ing some outlier samples, 200 samples with different

concentrations were left for building calibration model.

In the experiment, the spectrum of empty cell was mea-

sured firstly as a background [9].

Data preparation of calibration model: It is essential

to figure out the significant difference betwe en each

group of spectral data sample. Statistic method of T-test

was used to prove the validity of samples and to check

out the outliers data. In Table 1, a total of 200 glucose

Six spectra input

Hidden layer

Output (y)

Spectra absorption of

wavelength A

Spectra absorption of

wavelength B

Spectra absorption of

wavelength C

B. ZENG ET AL.

557

Table 1. Comparison of 20 glucose absorption spectra data.

Sample 10 20 30 40 50 60 70 80 90 100

xs±

312.0 ±

0.005

311.8 ±

0.008

311.6 ±

0.003

311.4 ±

0.032

311.3 ±

0.020

311.1 ±

0.023

318.8 ±

0.003

310.6 ±

0.003

310.4 ±

0.003

310.1 ±

0.006

p 0.0001 0.0001 0.0001 0.0007 0.0001 0.0002 0.0001 0.0001 0.00005 0.00005

Sample 110 120 130 140 150 160 170 180 190 200

xs±

309.9 ±

0.019

309.8 ±

0.020

309.5 ±

0.002

309.4 ±

0.005

309.2 ±

0.002

309.0 ±

0.019

308.7 ±

0.007

308.6 ±

0.018

308.3 ±

0.003

308.1 ±

0.019

p 0.0044 0.0001 0.00004 0.00003 0.0011 0.0004 0.0007 0.0001 0.0001 0.0001

absorption spectra were given out, the average and

standard deviation of 10 measurement values for each

glucose sample are calculated as well. Comparing with

the differences between 20 groups of glucose NIR ab-

sorption spectra, their differences achieve significant

level (T-test, p < 0.05).

3.2. Human Noninvasive Measurement

Experiment

Process of Human noninvasive measurement experiment:

In order to get training data covering wide variety scope

of human blood glucose concentration, the blood glucose

tests were made on volunteers at certain time before or

after their meal with the interval time of half an hour.

After measurement of blood glucose, multi-sensors were

attached to measurement sites to get responding spectra

information [10]. A total of 142 samples for each type of

measurements were got in this experiment and the blood

glucose concentration ranges from 3.2 mmol/L to 10.8

mmol/L.

Data preparation for BP-ANN training and testing:

Similarly to the data preparation of calibration model,

experimental data in noninvasive blood glucose measure-

ment were given in Table 2. T-test is applied to test the

spectra difference of different groups of blood glucose

concentration, p of T-test is smaller than 0.05, which

demonstrates that their differences reach significant

levels.

4. Results

4.1. Building Calibration Model

In both the glucose samples experiment and noninvasive

blood glucose measurement experiment, 7 kinds of cali-

bration model were built to realize the function of conti-

nuous monitoring. In A + B + C method of glucose expe-

riment, 20 spectra data samples were used to build the

calibration model. Figure 7 shows the relation between

glucose concentration and glucose absorption spectra,

from the figure we can see this validation owns a fine

prediction ability for unknown received spectra informa-

tion. In Table 3, the performances of different measure-

ment methods were compared, in the A + B + C method,

the RMSEP is 4.412 mg/dL and CC is 0.90, which shows

a great improvement in prediction performance to other

single or two wavelength methods. Compare with statis-

tic values of three single wavelength and combination of

two wavelength methods, single wavelength A shows

best sensitivity

4.2. Results of Human Blood Glucose

Noninvasive Measurement

4.2.1. BP-ANN Measurement Model

In the human noninvasive blood glucose measurement

experiment, a total of 142 samples were obtained, 122

couples of blood glucose absorption spectra data and

corresponding blood glucose reference values were used

for the BP network training. As shown in Figure 6, the

network can reach goal after 10 training, RMSEP of this

model is 0.088 mmol/L and CC is 0.9315 in the method

of A + B + C. Comparing with the other six methods, the

BP-ANN greatly decreased the prediction error, which

can be seen in Table 4.

4.2.2. Testing of Measurement Model

The left 20 samples were used as validation data sets to

test performance of the measurement model. 20 spectra

information data were inputted to the BP-ANN multi-

sensors information fusion model, 20 output values of

predicted blood glucose concentration were received at

the output layer, the result was given in Table 5. The

RMSEP between NIR method and standard method is

0.3143 mg/dL. T-test is applied and p is 0.1728, bigger

than 0.05, which demonstrate that the relation between

those two values is well related.

5. Conclusion

In this noninvasive blood glucose monitoring system,

two new measurement ideas were employed, which are

continuous monitoring and distributed multi-wavelength

measurements. The experimental result has proved that

this system has an improved advantage in blood glucose

prediction, and the multi-wavelength information fusion

model has also contributed greatly to the monitoring

B. ZENG ET AL.

558

Table 2. Difference comparison of 28 groups of blood glucose absorption spectra data.

Sample 10.8 10.5 10.1 10.0 9.8 9.6

xs±

567.95 ± 0.03 568.42 ± 0.15 568.93 ± 0.49 569.01 ± 0.33 568.98 ± 0.47 569.91 ± 0. 22

p 0.0125 0.0332 0.0318 0.0412 0.0215 0.0358

Sample 9.4 9.1 8.9 8.6 8.3 8.2

xs±

570.24 ± 0.10 570.67 ± 0.32 571.29 ± 0.41 571.44 ± 0.6071 572.06 ± 0.22 572.87 ± 0.49

p 0.0215 0.3025 0.0217 0.0160 0.0321 0.0245

Sample 7.8 7.6 7.4 6.9 6.7 6.5

xs±

573.52 ± 0.25 573.63 ± 0.21 574.03 ± 0.72 575.18 ± 0.43 575.36 ± 0.37 575.57 ± 0.39

p 0.0418 0.044 0.0360 0.0214 0.0124 0.0173

Sample 6.3 5.9 5.7 5.3 4.9 4.7

xs±

576.745 ± 0.36 577.15 ± 0.63 577.89 ± 0.26 578.13 ± 0.31 579.16 ± 0.11 579.58 ± 0.34

p 0.0368 0.0123 0.0451 0.0358 0.0201 0.0426

sa mpl e 4.4 4.2 3.9 3.7 3.5 3.1

xs±

579.80 ± 0.25 580.71 ± 0.23 581.07 ± 0.32 581.13 ± 0.21 581.21 ± 0.03 572.15 ± 0.16

p 0.0103 0.29 0.0412 0.0328 0.01 0.0257

Table 3. Performance comparison with different measurement methods in calibration model.

Statistics value A B C A + B A + C B + C A + B + C

RMSEP (mg/dL) 4.92 8.36 11.41 6.25 8.02 9.75 4.41

CC 0.89 0. 79 0.67 0.82 0.77 0.74 0.90

Figure 7. Relation between glucose concentration and spectra absorption.

020406080100 120 140160 180200

332

333

334

335

336

337

338

339

glucose concent rat i on

glucose abs orpti on spectra

RMSEP=4.41

CC=0 .90

mg/dL

B. ZENG ET AL.

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Figure 8. Rel ation of blood glucose concentration between measurement method and NIR arrays information fusion method.

Table 4. Performance comparison of different measurement methods in measurement model.

Statistics value A B C A + B

RMSEP(mmol/L) 0.175 0.362 0.423 0.2651

CC 0.8522 0.7325 0.6355 0.79342

Statistics value A + C B + C A + B + C A + B + C based on BP

RMSEP(mmol/L) 0.31288 0.39528 0.115 0.088

CC 0.76392 0.6821 0.8938 0.94713

Table 5. Analysis result of blood glucose concentration between comparison method and NIR method.

Numerical order NIR method (mmol/L) Standard method (mmol/L) difference

1 3.3888 3 −0.3888

2 3.9451 3.5 −0.4451

3 3.8019 3.8 −0.0019

4 4.0913 4.0 −0.0913

5 4.7504 4.5 −0.2504

6 4.2858 4.8 0.5142

7 4.7266 5.0 0.2734

8 5.3335 5.5 0.1665

9 6.0472 65.8 −0.2472

10 6.6496 6.0 −0.6496

11 6.9600 6.5 −0.4600

12 7.2690 6.8 −0.4690

13 7.0227 7.0 −0.0227

14 7.5781 7.5 −0.0781

15 8.0006 7.8 −0.2006

16 8.1452 8.0 −0.1452

17 8.1090 8.5 0.3910

18 8.8642 9.0 0.1358

19 9.4608 9.5 0.0392

20 10.1804 10 −0.1804

RMSEP 0.3143

p 0.1728 (T-test, p > 0.05)

3 4 5678910

A ct ual value(mmol /L)

Calabtat i on val ue(mm ol /L)

RMSEP =0. 088

CC=0.9315

V al ue of c al abrat i on

x=y

B. ZENG ET AL.

560

property. In the later work, we hope that new multi-wa-

velength stra tegy can be used to further improve th e pre-

diction accuracy.

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