Analysis ofNoninvasive Measurement ofHuman

Blood Glucose with ANN-NIR Spectroscopy

Ping ZuoInstitute of Mathematics

University of JilinChangchun, China, 130025

E-mail: rose6701@,sohu.com

Yingchun LiDepartment of Electrical Engineering

University of TsinghuaBeijing,China, 100084

E-mail: liyingchun@tsinghua.edu.cn

Jie Ma SiLiang MaInstitute ofMathematics

University of JilinChangchun, China,130025

E-mail: MaJie@mail.edu.cn

Abstract-The survey by diabetics themselves is the mainmeans to decrease the combinet mness caused by diabetes. Thecurrent measurement method is harmful or less harmful thatwill bring pains to the patients and also has manyinsufficiencies in using it. Based on the task of noninvasiveblood glucose measure, An outside body and inside bodymeasure amphibious human blood glucose measuring system isdesigned by us in china which is used to measure and modelanalyze glucose liquid of different concentration, and in thispaper we puts forward a new technology in analyzing theabsorption spectrum of infrared ray by blood. After themeasurement of the absorption spectrum of infrared by thetotal blood and normal human serum and higher glucoseblood, this paper makes an artificial neural network trainingthrough the Levenberg-Marquardt BP neural network whichdepends on the character parameter in the value of the 16special wavelength. Only by lItimes the training can match theaccuracy of requirement that would meet error demands of thenational measurement in biology and chemistry between-O.02A.03mmol/L. and the experiment is an advanced one.The research result has valuable promise to blood analysis ofthe infrared spectroscopy and diagnosing the disease. Theexperiment proves that this method, compared with thecurrent method, is characterized by rapid training speed andaccurate predication result.

I. INTRODUCTION

The absorption coefficient of human body's bones,muscles, fat, skin and body fluid at near infraredspectroscopy is small, the ray in the NIR spectrum is hardlyreflected or scattered, in addition, it is Linear polarized raythat makes it more penetrable and easily reach over 5 Cmdepth beneath the skin and tissue, then obtains plentyabsorption signals from deepness, so it is used widely in theresearch of component of human body' tissue. This paperapplies the technology of the reflection of near infraredspectroscopy to the noninvasive blood glucose. Thereflective spectrums contain a lot of the information ofvarious body's components when near-infraredspectroscopy reflects the human tissues, so measuring the

reflection of near infrared spectroscopy can measure theconcentration of the blood glucose in human's body.The application of artificial neural network in analyzing

of biology and chemistry has attracted more and moreattention from analyses, it is also one of the good ways toprevent and diagnose diseases [1]. Presently, there arereports of Madaline network, linear network, radial basicfunction network, ordinary BP network which applied inmedicine analyzing[ 1-6 ].This paper first measured theabsorption of infrared spectrum over normal and abnormalblood samples through the calculation of Levenberg-Marquardt BP, and has gained a satisfying result, and thenetwork training met the requirement only with 16 times.The measurement method is hopefully to be the sufficientmethod of noninvasive measurement to human's body. Theresearch result will bring a significant value in analyzingblood fluid and diagnosing the disease.

II.THEORY AND METHOD

The neural network can imitate the process of human'slearning and feeling and solve non-linearity matters, whichhas made it widely used in calculating and predictingcomplicated system, and achieved a non-linearity mappedeffect which the conventional calculating way could not do.The nerve cell, which is the base of neural network, has thefunction of processing information. The task selected a BPnetwork structure with a hidden layer. 21 points were madeaccording the arrangement of experiment (each onerepresented the amount of absorption of specific wavelengthin 21), the output layer has two points (represented theamount of total blood and blood fluid samples respectively),the hidden layer has six points. In order to calculate theprediction directly, the forms of transfer function betweentwo layers are different, the S function is adopted betweeninput and out put layers, the particular function here is tanone, the non-linearity function is adopted between hiddenlayer and output one, the transfer function of the wholenetwork can be expressed by the following formula:

0-7803-9422-4/05/$20.00 ©2005 IEEE1350

M 2y1 =b2l + W22jlm

=1 1+e-2[; Wigxi + by]

i=1, 2, *,m; j=1 2, M;m in the formula is the number of node points of input

layers, and "M' is the number of node points of hiddenlayers, "1" is the number ofnode points of output layers, wlijis the connection weight of No.1 node point of input layerand Noj node point of hidden layer, w2jl is the connectionweight of Noj node point of hidden layer and No.1 nodepoint of output layer, blj is the bias of the No j node point ofhidden layer, b21 is the bias of the No.1 node point ofoutput layer. This task used the Levenberg-Marquardtcalculating way considering the speed of calculation andconstringency.

mII. THE EXPERIMENT SECTION

1. The experiment devices and design ofthe projectWhen the devices' parameters are fixed, Nexus-870

interference infrared spectrum device takes a minute tocollect a sample, and 1-2 minutes to collect vein blood, sothe total collecting time for one sample is about 3minutes .Each unit needs to select three parts to collectspectrum data and in total they need 10 minutes. Theaverages of scanning spectrum in experiment are4000-10000cm 1 differentiate rate is 4cm 1, scanningtime is 32. Selection of position of measuring spectroscopyis very important for the noninvasive blood glucose, in somedocument the mouth, tongue tip and finger tip are used asthe position of collecting spectrum, but the former two canbe hardly accepted by the tested person or diabetics. Theaim of noninvasive blood glucose survey is to producecommercial device, as clinic devices it must consider theconvenience and cleaning of the patient, so the thumb tip,wrist with vein and palm are selected for the position ofselection of the spectrum, which are easy for the testedperson to accept. The process of the experiment imitated theway for testing the bearing amount of oral dextrose sugar,the consistence of body blood glucose has a change aftertaking the amount of oral dextrose generally 75g or lOOg,the blood glucose consistence increases firstly and get to themaximum value while taking oral dextrose 1 to 2 hours later,then the blood glucose consistence begins to lower till 2-3hours later it recovers to the consistence before takingglucose. The normal person's blood glucose consistence isalways below the kidney glucose level, the abnormalperson's blood glucose consistence exceeds the kidney rglucose level, and then it decreases; finally returns to itsinitial level and keep it for a period, the diabetics'consistence of blood glucose far exceeds the kidney glucoselevel and the time of keeping it is longer than that of aabnormal one.

2. The confirmation ofmeasuring wavelength and themodel ofBP network training

Select 5500'6500nm, 7500-8500nm as the average ofscanning, select the next 11 wavelengths as the testingwavelength; test the absorption of the light samples atdifferent wavelength according to the methods ofexperiment.

3. The network modelparameter and discussionSix volunteers is numbers as A, B, C , D, E, F random

and which 20 samples are collected. No.5 samples randomand keep them as prediction sample which do not participatemodeling. Considering the feature of NN, concentration ofblood sugar of prediction sample should be within the rangeof concentration of modeling samples. BP network is trainedusing integrated spectrum data of limosis and after meal andthe model is set up. Since the part of the same unit can effectthe modeling result, Collecting spectrum data from the partof the body where has little difference is important [4] [5].

The near infrared reflection spectrum data ofConcentration of human blood sugar and absorbency in7500'8500cmn1 wave band collected from wrist, thumb tipand palm is modeled using ANN. The number of samplesfrom each volunteer is 15, and 5 is kept as prediction samplewhich do not participate modeling. Awl stands for firstdifference of actual values of concentration of blooddifference of spectrum data from wrist and Aw2 stands fromsecond difference of spectrum data. ERP is standard sugarand computed values of model. Character t and P stand forspectnrm is collected from thumb tip and palm.

TABLE I

THE MODEL PARAMETER OF ARTIFICIAL NEURAL NETWORK

wlIj j=1 j=2 j=3 j=4 j=5 j=61=1 1.37814 -5.7770 -30.9693 0.1129 -0.6345 0.07151=2 -0.9956 -0.7237 0.7039 0.1046 0.5684 0.25401=3 -0.2143 0.7289 0.1490 -0.6486 -0.5268 -0.48991=4 0.9127 -0.0946 -1.0178 -0.1540 -0.8885 -0.30561=5 0.9095 -0.3261 -1.0548 1.0270 0.6098 0.90081=6 0.3529 0.2468 0.7152 0.5993 0.5582 1.18311=7 -0.2096 -0.6171 -1.9538 -0.9671 -1.2070 -0.5251=8 -0.5668 -1.0770 0.2964 -0.1354 -0.3095 -0.19971=9 -0.3114 0.3054 -0.3764 -0.8111 -0.2259 -0.4018I=10 0.7920 -1.4621 -2.0117 0.7704 -0.8842 0.64661=11 -1.5694 -0.7456 0.3009 1.4204 0.9382 -0.3024w2jl j=1 j=2 j=3 j=4 j=5 j=61=1 -1.2056 1.1121 0.6328 0.7907 -1.282 0.5365

j=1 j=2 j=3 j=4 j=5 j=6blj 0.3085 -0.7295 1.0981 0.9402 0.4733 1.0589b21 -0.064

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Input the absorption degrees of training sample, useartificial neural network to train according to the process ofcalculating, fix the present weight value and threshold valuewhen the trains satisfy the demand. The obtained neuralnetwork weight matrix and threshold values are as Table I.

The whole learning times are 11 and it can meet therequirement of less than 1.1% relative error. Theconventional calculating method times are more than 25 andsome go up to 50 or 60, obviously we can see that thiscalculation method can save much more time and workloadsthan the conventional one.

IV. THE ANALYZING AND DISCUSSION

Choose 15 volunteer models and arbitrarily keep 5samples from establishing the model and keep them aspredicating samples. According to the characteristic of BPmethod, the glucose concentration of predicating samplesshould be within the range of the samples which are to beused to establish the model. Choose 6 volunteers andestablish model by combining the lismosis, and after mealspectrum samples. Experiment data are inputted into atrained computer by group and according output valued iscomputed. The accurate rate of prediction output data andrelative standard deviation are as below Table II. Character fand k stand for after meal and lismosis.

Concentration of human blood glucose is predicted andmodeled as single unit adopting BP neural network methodblood using near infrared diffuse reflection spectroscopy ofthe vein of waist in 7500-8500cm1 wave band.Self-prediction error of single unit model0.29mmolI/L andmaximum error of computed value and actual value ofconcentration of blood glucose is -0.2-0.2 mmol/L which isless than 1.1 mmol/L and -0.8-0.8 mmol/L which is refereedin literature[6-8].

It can be seen from the Table II that the error of predictedresult of 15 other samples based on model of volunteer D inwave band of 7500'8500cm71 A 1.028mmol/L Standarddifference of error of computed value and actual value ofconcentration of blood sugar is -0.3031-0.328mmolILwhich is not more than 1.1 mmol/L and -0.8-0.8 mmol/Lmentioned in literature[6-8].

Data in table HI is predicted values of 15 samples of 5volunteers' based on volunteer D's model. The maximumprediction error of 15 other samples based on volunteer D'smodel reaches 1.106 mmol/L.

Contrastly, the maximum self-prediction error ofconcentration of glucose of 6 volunteers based onemendation model is 1.013mmol/L which is also less than1.1 mmol/L, but the maximum prediction error of 15samples which dose not belong to volunteer D basedvolunteer D's model is 1.3686 mmol/L. It can be seen thatenlargement of model region of spectrum can increasepracticability of model, mean while decrease predictionprecision of model, thus modeling region should be

confirmed according to practical situation in actualapplication, which is very important of noninvasivemeasurement of concentration ofblood glucose.

TABLE II

RESULT OF SELF PREDICTION OF A SINGLE UNIT MODEL INWAVE BAND OF I7500-8500CM-1 AND PREDICTION ERROR OF 15OTHER SAMPLES BASED ON VOLUNTEER D'S MODEL REACHES

1.106 MMOL/L

Self-prediction Error Prediction Errorssamples (mmol/L) samples (mmol/L)

AkO6 -0.016387 AfD9 0.83340AfD5 0.02925 AfO5 0.63686Af09 0.015823 AkO6 -0.20576BfOl 0.013494 BfD6 0.67826BfD6 0.010341 BfD1 0.08799BkO8 -0.018998 BkO8 0.64710Cf03 -0.010437 Cf06 0.38949Cf06 0.005888 CfO3 0.36512CkO6 -0.015273 CkO6 0.37826DfOl -0.010724 EfO6 -0.25337Df09 0.016596 EfO7 0.20358DkO5 -0.00833 EkOl 0.09031Ef06 -0.004522 Ff04 -0.41037EfD7 0.018008 FklO -0.09590EkOl 0.028911 FkOl 0.35605

TABLE IlI

SECOND DIFFERENTIAL OF SELF PREDICTED RESULT OFSINGLE UNIT MODEL IN 7500-9500CM0 WAVE BAND ANDPREDICTED RESULT OF OTHER 15 SAMPLES BASED ON

VOLUNTEER D'S MODEL IN WAVE BAND OF 7500-9500CM'_

Self-prediction Error Prediction Errorsamples (mmol/L) samples (mmol/L)

AkO6 -0.43623 A9 1.03859AfD5 0.70325 Af05 0.39413AfD9 0.01213 AkO6 -0.09705BfD1 0.19187 BfD6 0.73788BfD6 -0.21831 Bfil 1.11385BkO8 -0.1986 BkO8 0.86154Cf03 -0.80978 CfO6 1.19075CfO6 0.09636 CfD3 -0.4608CkO6 0.56297 CkO6 0.73788DfDl -0.57708 EfD6 0.70840Df09 0.44826 EfD7 0.82805DkO5 -0.44826 EkOl 1.18882EfO6 -0.40446 FfO4 -0.23508EfD7 0.18008 FklO 0.97578EkOl 0.72233 FkOl 1.04182

Conclusion can be drawn that ERP value of 6 volunteersvaries rapidly and result of first difference modeling ofspectrum of volunteers' thumb tip is better than which ofpalm and second difference modeling of spectrum of wristcan have good effect and standard difference of neutralemendation model is less than 0.305 which indicatesstability ofmodel is good. Errors range from -0.8 mmol/L to0.8 mmol/L, which indicates application scope of model isabroad and can be applied abroad to diabetic patients [3].

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V.CONCLUSIONS

In this paper, a measure technique of concentration ofblood sugar in the human blood using near infraredspectrum is systematic researched. To start with analyzingnear infrared spectrum of glucose the main ingredient ofblood sugar, combining common outside body measure ofblood sugar and research of which inside body, analyzingblood plasma and whole blood outside body of a singleperson and blood serum and blood outside of body of multiperson, a mass of experiment data can be achieved based onwhich optimal analysis wave band and pretreatment methodof spectrum data and chemical metric method and posture ofhuman body when spectrum is gathered is confirmed. Rangeof wave band of near infrared analysis of blood sugar andthe thickness of sample pool of blood sugar abruptionexperiment is basic confirmed. Artificial neutral network BParithmetic is confirmed as the optimal method by measuringnear infrared spectrum of glucose liquid of differentconcentration and model analyzing of three kind ofchemical metric methods. On the base of upper researchmodeling analyzing of manual confected whole blood andblood plasma of different concentration of a single personwhich proved that is feasible to measure concentration ofblood sugar using NIS technique. Part of samples which didnot participate in modeling predicted and analyzed base onemendation model and the result of self prediction of singleunit reaches 0.835mmolIL which is much better than theresult mentioned in literature of China and of abroad.Meanwhile, a broad wave band model is made to enhanceapplicability of model. All above has proved feasibility ofmeasuring human blood sugar using near infrared spectrumtechnique.An outside body and inside body measure amphibious

human blood sugar measuring system is designed in chinawhich is used to measure and model analyze glucose liquidof different concentration. Result gained is compared withthe result gained by Fourier spectrum analyzing instrumentwhich proved the maneuverability ofthe system.

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[6] Mark A Arnold, Jason J B, Gary W. Small, "Phantom glucosecalibration models from simulated noninvasive human near-infraredspectral", Analytical Chemistry, vol. 70(9), pp. 1773-1781, 1998.

[7] Airat K. Amerov, Yu Sun, Gary W. Samll, et al, "Kromoscopicmeasurement of glucose in the first overt- one region of the nearinfrared spectrum", SPIE, vol. 4624, pp.1 1-19, 2002.

[8] Airat K. Amerov and Mark A. Arnold, "In Vitro Kromoscopicanalysis of glucose in blood", SPIE, vol. 4965, pp. 7-15, 2003.

[9] Kevin H H, Mark A Arnold, Gary W S, "Measurement of glucose andother analytes I in undiluted human serum with near-infiaredtransmission spectroscopy", Analytical Chemical Acta, vol. 371,pp.255-267, 1998.

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ACKNOWLEDGMENT

The authors would like to thank Mr. Yuguo Tang andZhongbo Zhang for their help. This work was supported by15 Brainstorm Projects of National Science and Technologyunder grant no. 2004BA210A09.

REFERENCE

[1] T. B.Blank, T. L.Ruchti, A. D. Lorenz, et al, "Clinical results from anon-invasive blood glucose monitor", SPIE, vol. 4624, pp. 1-10, 2002.

[2] Sun-jen Yeh, Charles F. Hanna and Omark S. Khalil, "Monitoringblood glucose changes in cutaneous tissue by temperature-modulatedlocalized reflectance measurements", Clinical Chemistry, vol. 49(6) pp.924-934,2003.

[3] HaiTao Ma, Dong Ding, JiCheng Cui, et al, "Application of nearinfrared spectrum in Non-invasive glucose measurement", Chinese

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