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a Weak Signal Extraction Method for Human Blood Glucose Noninvasive

a Weak Signal Extraction Method for Human Blood Glucose Noninvasive
a Weak Signal Extraction Method for Human Blood Glucose Noninvasive

A Weak Signal Extraction Method for Human

Blood Glucose Noninvasive Measurement using Near

Infrared Spectroscopy

Li-Na Li &Qing-Bo Li &Guang-Jun Zhang

Received:4April 2009/Accepted:22June 2009/

Published online:4July 2009#Springer Science +Business Media,LLC 2009

Abstract Background interference from optical absorption of matrix components,low spectral selectivity and low spectral sensitivity are the main interference factors for human blood glucose noninvasive measurement using near infrared (NIR)spectroscopy.In order to extract the weak glucose concentration information,a modified uninformative variable elimination (mUVE)method combined with successive projections algorithm (SPA)named as mUVE-SPA,is proposed.mUVE is used to eliminate matrix background and high-frequency noise by wavelet multi-resolution technology.SPA is followed to select variables with minimum colinearity by projection algorithm in a vector space.The proposed method was applied in two NIR spectra data sets (plasma samples experiment in vitro and human blood glucose noninvasive measurement experiment in vivo)respectively.The performance and adaptability of the proposed strategy were discussed.The results indicate that the proposed hybrid method can give an alternative path to extract weak glucose information and yield more parsimonious models with higher precision.

Keywords Uninformative variable elimination .Successive projections algorithm .Blood glucose noninvasive measurement .Near infrared spectroscopy

1Introduction

World wide,150million people suffer from disturbances in the endocrine metabolic regulation,called diabetes.Nowadays,there are no better treatment than monitoring blood glucose concentration frequently and adjustment of drug dosage in time.Minimally invasive measurement is often used,which puncture the skin are still standard techniques for monitoring glucose concentrations through electrochemical,colorimetric or optical disposable strips for finger blood samples.However,pricking the finger several times a day J Infrared Milli Terahz Waves (2009)30:1191–1204

DOI 10.1007/s10762-009-9544-0

L.-N.Li :Q.-B.Li :G.-J.Zhang (*)

School of Instrumentation Science and Opto-electronics Engineering,

Precision Opto-mechatronics Technology,Key Laboratory of Education Ministry,

Room B528,Xin Zhu Lou,Beihang University,37Xue Yuan Road,Hai Dian District,

Beijing 100191,China

is painful and often leads to the risks of infection,which causes difficulties to the therapy for patient[1].

Recently,the study of noninvasive method for blood glucose monitoring has become a favorite topic in the field of biomedicine[2,3].Especially,near infrared(NIR) spectroscopy appears as a very promising alternative to noninvasive blood glucose monitoring,which attracts many researcher’s interest[4–7].Unfortunately,so far there are no reports or patents which show that such noninvasive approaches have the same accuracy as invasive procedures.

Glucose signal is weak,and it is a block to improve prediction accuracy for such noninvasive approach[8–10].The main difficulty to weak signal extraction is that,in the NIR region,the spectral selectivity and sensitivity is not better enough to weak glucose information extraction.The main reasons can be described as follows:

(1)The magnitude of glucose signal changes is small.When the signal-to-noise ratio

(SNR)of instrument is low,the faint glucose signal is tend to be submerged in high-frequency noise.

(2)The interference from human body is great.It is difficult to distinguish weak blood

signals through intervening tissues(fat,skin,etc.),and in addition,extract glucose information from that of other matrix with higher concentration(proteins,urea,uric acid,hemoglobin,albumin,water,etc.).The glucose content is occupied a small part in human blood.Ordinarily,plasma and hemoglobin is occupied55%and44%in blood respectively.And water is occupied90%in plasma.Normally,the ratio of glucose and water is0.1in blood and tissue.Especially,the optical absorption of water in NIR region is great.Figure1shows the NIR absorptivity of four main components(glucose,water,albumin and hemoglobin)in blood.It is clear that the absorptivity of water is higher,which forms a great matrix background interference to human blood glucose noninvasive measurement using NIR spectroscopy[11–13].

And glucose is overlapped with other components,which also is the main reason why the weak glucose information extraction is so difficult.

In this work,the authors attempt to explore a novel weak glucose signal extraction method for human blood glucose noninvasive measurement using NIR spectroscopy.

Commonly,data processing is utilized to eliminate the matrix background and high-frequency noise.Variable selection is utilized to extraction of informative variables representing weak glucose signal.Aiming to improve prediction accuracy,a modified uninformative variable elimination(mUVE)combined with successive projections algorithm(SPA)called mUVE-SPA,is proposed in this paper.mUVE is a spectral data processing method,which based on the theory of wavelet transform.It is utilized first to eliminate matrix background and high-frequency noise simultaneously,which can improve NIR spectra quality[14–19].SPA is a variable selection method,which based on projection theory in a vector space.It is aimed to eliminate colinearity and extract informative variables[20–22].In this paper,mUVE-SPA is applied to two NIR spectra data for analysis of blood glucose concentration:plasma samples and human blood glucose noninvasive measurement.The weak glucose signals extraction performance and adaptability to complex samples were discussed in this paper.

2Theory and calculation

2.1Modified uninformative variable elimination

The mUVE method is based on wavelet multi-resolution technology for eliminating matrix background and high-frequency noise.It is well known that background and noise are always found in high-scale approximation and low-scale detail coefficients.Therefore,it is possible to distinguish the corresponding components by wavelet transform[17–19].Then mUVE can be used to determine the scales expressing the information of background and noise.This mUVE method is developed from the method called“Uninformative Variable Elimination (UVE)”[15,16].Instead of selecting the variables for multivariate calibration as the UVE do,mUVE is proposed to investigate the corresponding wavelet component’s contribution to the model.More detail description about the theory of mUVE sees Ref.[9,17].

2.2Successive projections algorithm

SPA employs simple projection operations in a vector space to obtain subsets of variables with small collinearity[20–22].SPA is a deterministic algorithm and has good repeatability, which does not employ stochastic operations such as Simulated Annealing(SA)and Genetic Algorithm(GA)do.The principle of variable selection by SPA is that the new variable selected is the one among all the remaining variables,which has the maximum projection value on the orthogonal subspace of the previous selected variable.The theory description of SPA sees Ref.[20].It is noticed that,the start vector X start(i.e.a spectral variable)and maximum number of selected variables N are important parameters need setup initially.Because N value is within the scope of sample number,it is possible to start from each variable X start(for start=1to p,p is the number of variables)with each N(for N=1 to n,n is the number of samples).Under the parameter pair of(X start,N),the optimal initial variable and the number of selected variables can be determined on the basis of the smallest root mean squares error of prediction(RMSEP)of cross-validation.

2.3mUVE-SPA method

According to the theory of mUVE,the predictive accuracy of calibration can be improved

are still employed in PLS,which provides complex models and may not have a straightforward physical interpretation.SPA has the ability to minimize redundant information by eliminating colinearity.However,variables selected by direct SPA may be with low SNR or useless for multivariate calibration.So the hybrid method,mUVE-SPA is proposed here to construct a more parsimonious calibration model with higher prediction accuracy for blood glucose noninvasive measurement using NIR spectroscopy.mUVE is employed at first to eliminate matrix background and noise.Then SPA is used to select variables with minimum redundant information from the reconstructed spectrum.Under the pretreatment of mUVE,the selected variable by SPA is more representative.And it makes the association of variables and property closer.The combine of mUVE and SPA can improve prediction accuracy for human blood glucose noninvasive measurement using NIR spectroscopy.

3Experimental

3.1Plasma samples measurement in vitro using NIR spectrometer

Instrumentation and reagent:NIR spectra data of plasma samples were got by Spectrum GX FITR instrument(Perkin-Elmer,America).The InSb detector with liquid nitrogen cooling was utilized.A1mm quartz sample pool and peristaltic pump automatic sampling system were used in this experiment.The scanning range of spectrum was from918.2nm to 3708.7nm.There were8192wavelength variables got for the NIR spectra data of plasma samples.Blood glucose reference values were got by automatic biochemistry instrument TBA30(Japan).Glucose powder was got from The Third Chemical Reagents Plant of Tianjin(China).Plasma samples were got from Tianjin Hospital of China.

Experiment procedure:Adding heparin in whole blood,and then separating plasma from whole blood by centrifugal machine.By adding glucose to plasma randomly,a variety of glucose concentrations in plasma samples were got.The reference values of glucose concentration were got by TBA30.

Data set:There are33usable samples got by this experiment,and the glucose concentration values are distributed randomly between10.4mg dL?1to44.4mg dL?1. 3.2Human blood glucose noninvasive measurement in vivo using NIR spectrometer

In order to get a wide variety of blood glucose concentration in human blood glucose noninvasive measurement experiment,the Oral Glucose Tolerance Test(OGTT)is introduced.OGTT is a kind of glucose burden adjustability test for diagnosis of diabetes in clinic[23,24].For the healthy person,under this test,a varying scope of glucose concentration can be got within several hours.It can be used as a special experiment method for getting a calibration model with a certain concentration variety,which gives a novel way for the research of blood glucose noninvasive measurement by NIR spectroscopy.

Instrumentation and reagent:NIR spectra were got by Nicolet FT-IR6700Spectrometer (Thermo scientific,America)with InGaAs2.6μm detector,CaF2beam splitter and white light source.The integrating sphere sampling accessory was used in this experiment. Spectrum scanning area was from1000nm to2500nm.Resolution set was4.The One Touch?Ultra?2Blood Glucose Meter(LifeScan,Inc.America)was used for getting the

Experiment procedure:A healthy volunteer had been fasted for8hours before the experiment began.Then he drunk100mL water with75g glucose within5minutes,and the NIR diffuse reflection spectrum was collected from the finger pulp.At the time of sampling,the measurement position,measurement pressure as well as the psychology of the volunteer kept invariableness as far as possible.At meantime the corresponding blood glucose reference values were got by One Touch?Ultra?2Blood Glucose Meter.In the course of this experiment,the human blood glucose concentration value increased gradually until arriving at the peak value217.8mg dL?1,and then decreased.The experiment was over when the concentration value was down to the normal level84.6mg dL?1.It was cost about3hours in this experiment.

Data set:There are21usable samples got by this experiment.Each spectrum has3112 variables.The blood glucose concentration scope is between84.6mg dL?1to217.8mg dL?1. At each point of blood glucose concentration measurement,NIR spectra also were got.

3.3Calculations and software

Because of the human individual difference,it is impossible to build a universal prediction model by the current NIR spectroscopy technology for blood glucose noninvasive measurement.But the personal knowledge base is utilized usually.So in this paper,the experiment only included one volunteer.All the modeling is aiming to this one.Of course, the method can be repeated similarly for the other people.Additionally,the model transformation problem between different instruments or between different measure conditions is another key technology branch for blood glucose noninvasive measurement using NIR spectroscopy,which will not be discussed in this paper.So the target of this paper is to explore a method for extracting weak glucose information and improving prediction accuracy under the basis study of blood glucose noninvasive measurement using NIR spectroscopy.

Additionally,because of the limit of experiment condition,such as glucose concentration only changed in about3hours for OGTT experiment,pricking the finger several times is painful and the position can be pricked is also a limited region,there are many variables and fewer objects for these two NIR data sets.So the RMSEP of cross-validation is employed as an evaluation criterion for Partial Least Squares(PLS)regression.The mUVE-SPA algorithms and the PLS regression are compiled by Matlab7.6.0(The Mathworks, Inc.,Natick,MA,USA)and Wavelet Toolbox4.2.

4Results

4.1Information extraction for the NIR data of plasma samples

Firstly,for the respond data of plasma samples,thinking of the saturation phenomenon after the band of1900nm,and the noise is very distinct after the band of2500nm,and thinking that the absorption of glucose is always in the band of1150nm to1300nm,1510nm to 1850nm[25],so the band of1000nm to1890.36nm is employed in this paper.There are 4711variables in this NIR spectra data.

According to the theory and algorithm described in Section2.1,each spectra signal is decomposed by12levels.Under the mUVE criterion,the corresponding components to scale1,2,3and11are removed as noise and background simultaneously.When the

cross-validation RMSEP of PLS.In this study,the Daubechies family (db2~db10)has been investigated,the results indicate that the db3is the best choose.

As shown in Fig.2(a),(b)and (c),the original spectra,reconstructed spectra and eliminated information for the NIR data of plasma samples are given.It is clear that the absorption area about 1450nm and 1900nm are removed distinctly (see Fig.2(c)),which around the main absorption peak of water.It is very encouraging that one of the main matrix background interference (the optical absorption of water)of blood plasma samples is almost eliminated.

Then,according to the algorithm described in Section 2.2,the spectral variables can be selected.There are 31variables selected from the total 4711variables.The selected variables (marked with circle)are given in Fig.3.

4.2Information extraction for the NIR data of human OGTT samples

For the respond data of human OGTT samples,because the instrument and measure condition are different with plasma samples,and in order to include information as more as

A b s o r b a n c e

A b s o r b a n c e

Wavelength (nm)

A b s o r b a n c e a

c

b

Fig.2mUVE pretreatment results for the NIR data of plasma samples:(a)original spectra;(b)reconstructed spectra;

(c)eliminated information.10001200140016001800

1

2

3A b s o r b a n c e Fig.3Variable selection by

mUVE-SPA for the NIR data

of plasma samples.

possible,the full band (1000nm to 2500nm)is employed here.There are 3112variables in this NIR spectra data.

Similarly,mUVE is used firstly to eliminate background and noise.Under the signal continuation and wavelet transform,each spectra signal is decomposed by 12levels.Under the mUVE criterion,the corresponding components of scale 1,2,3and 8are removed as noise and background simultaneously.When the background and noise are eliminated,the best wavelet function is also selected by cross-validation RMSEP of PLS.The Daubechies family (db2~db10)has also been investigated,and the db3is the best choose here.

The original spectra,reconstructed spectra and eliminated information for the NIR data of human OGTT are given in Fig.4(a),(b)and (c).It is clear that the absorption position around 1450nm and 1900nm in NIR region are eliminated distinctly,which is similar to the experiment of plasma samples.

Then,SPA is used to select informative variables from reconstructed spectra.There are 16variables selected from the total 3112variables by mUVE-SPA.As shown in Fig.5,the selected variables are marked with circle.

01

2A b s o r b a n c e

01

2

A b s o r b a n c e 1000

150020002500

-0.500.5

Wavelength (nm)

A b s o r b a n c e

a b c

Fig.4mUVE pretreatment

results for the NIR data of human

OGTT samples:(a)

original spectra;(b)reconstructed spectra;

(c)eliminated information.1000150020002500A b s o r b a n c e Fig.5Variable selection by

mUVE-SPA for the NIR data

of human OGTT samples.

Table1Comparison between different pretreatment methods for the NIR data of plasma samples. Pretreatment Variable selection Factor RMSEP Correlation

(mg dL?1)

None Full spectra11 3.10.92

First-derivative Full spectra7 2.40.96 mUVE Full spectra10 2.10.96

5Discussion

5.1Comparison for the NIR data using different pretreatment methods

In order to comparison between different pretreatment methods,the first-derivative of the original spectrum was also employed in this paper.First-and second-derivative spectra are commonly used pretreatment method for minimizing the problems due to overlap and baselines.For the NIR data of plasma samples and the OGTT samples,the first-derivative of the spectrum were formed after the Savitzky-Golay algorithm.And the PLS regression was employed.Under different pretreatments,first-derivative and mUVE,the model parameters are given in Tables1and2respectively for the two experimental data sets.It is clear that the mUVE method has the best results.Although derivative spectra are commonly used to eliminate baseline and background,it is not useful to signal-noise ratio.mUVE can be used to eliminate matrix background and noise simultaneously,and the better prediction performance can be got.

5.2Comparison for the NIR data using different variable selection strategies

In this part,the feature wavelength region and direct SPA are introduced to compare with mUVE-SPA.

It has been shown that the optical absorption of water in NIR region is the main matrix background interference to plasma samples and human OGTT samples.And it is common that the main absorption peak of water is usually found in the wavelength about1450nm, 1900nm.The absorption area of human hemoglobin is lower to1100nm.Additionally, thinking of the combination and overtone molecular vibrations associated with C-H and O-H bands of the glucose molecular are always found in around1500nm to1800nm and 1100nm to1300nm[25],the spectra scope from1100nm to1700nm is selected in this Table2Comparison between different pretreatment methods for the NIR data of human OGTT samples. Pretreatment Variable selection Factor RMSEP Correlation

(mg dL?1)

None Full spectra822.60.85

First-derivative Full spectra822.40.86

study as a feature wavelength region.There are 3210variables employed for the NIR data of plasma samples and 1665variables employed for the NIR data of human OGTT samples.At meanwhile,direct SPA is also introduced as a variable selection method for the two NIR data.For plasma samples,there are 31variables selected,and for human OGTT samples,there are 19variables selected.

Under different variable selection strategies,the selected variables are marked in Figures 6and 7for the NIR data of plasma samples and human OGTT samples respectively.It is clear that the distributions of variables are different.And the PLS model parameters are given in Tables 3and 4.As shown in Table 3,for the NIR data of plasma samples,it is clear that the best prediction accuracy (1.7mg dL ?1)is obtained by modeling with selected variable of 1100nm to 1700nm (3210wavelength variables),and mUVE-SPA has approximate result (1.8mg dL ?1)by employing 31wavelength variables.It is implied that,under the method of mUVE-SPA,only several variables (19wavelength variables)are employed for quantitative calibration,the modeling efficiency is improved greatly and prediction accuracy is not decreased obviously.For the NIR data of human OGTT

samples,Fig.6Variables selected by

different variable selection

strategies for the NIR data of

plasma

samples.Fig.7Variables selected by

different variable selection

strategies for the NIR data of

human OGTT samples.

Table3Comparison between different spectra variable regions for the NIR data of plasma samples. Pretreatment Variable selection The number of variables Factor RMSEP Correlation

(mg dL?1)

None Full spectra471111 3.10.92 None1100–170032108 1.70.98 None SPA3111 3.50.90 mUVE SPA3120 1.80.98

as shown in Table4,it is also clear that,without pretreatment,although the prediction accuracy of modeling with selected variables by direct SPA is not better than full spectra (the case of plasma samples is the same),the modeling efficiency is improved greatly.And under the mUVE-SPA method,only16wavelength variables are employed to PLS regression,the prediction accuracy is the best one(5.0mg dL?1)compared with others.

5.3Prediction performance and adaptability

The NIR spectrum of human blood glucose noninvasive measurement in vivo is more complex than that of plasma samples experiment in vitro.Because the NIR spectra of human blood glucose measurement experiment can be affected by many uncertainty factors, such as measurement position,measurement time,measurement pressure,the human physiological state and so on.Those factors are strongly uncertain and can not be estimated, which makes the NIR spectra more complex.This problem is very distinct especially in the blood glucose noninvasive measurement using NIR spectroscopy.So the adaptability to complex spectra model is very important part.It is an appraisement item for the pretreatment method and multivariable calibration.

According to the PLS regression parameters showed in Tables3and4,it is validated that direct SPA can not help to calibration when the interference from background and noise is great.However,utilizing mUVE-SPA method can avoid this problem and have better prediction performance.For the NIR data of plasma samples,the correlation of prediction values and reference values yield by SPA-PLS and mUVE-SPA-PLS are given in Figs.8 and9respectively.For the NIR data of human OGTT samples,the correlation of prediction values and reference values yield by SPA-PLS and mUVE-SPA-PLS are given in Figs.10 and11.And the mUVE-SPA also has better adaptability to complex spectra model especially for the complex NIR spectra of blood glucose noninvasive measurement,the Table4Comparison between different spectra variable regions for the NIR data of human OGTT samples.

Pretreatment Variable selection The number of variables Factor RMSEP Correlation

(mg dL?1)

None Full spectra3112822.60.85 None1100–17001665337.60.58 None SPA191727.30.82

RMSEP is 5.0mg dL ?1,which is decreased 78%of one with no data processing.The improvement of prediction accuracy is better than plasma samples experiment,which is decreased 42%of one with no data processing.Additionally,the mUVE-SPA method is a determinate algorithm,which has good repeatability.And it is has no stochastic like GA do.The more initial variables have the more randomicity result GA has [26,27].And though GA has better prediction accuracy in one test,its stability is dubious for it may have different results even though under the repeat test with the same experimental condition,which can result in a bad adaptable to complex spectra model.In this regard,mUVE-SPA is a better alternative for human blood glucose noninvasive measurement using NIR spectroscopy.6Conclusions

In order to extract weak glucose concentration information from NIR experimental data of human blood glucose noninvasive measurement,a novel hybrid strategy named as mUVE-SPA is proposed by combining mUVE and SPA.Under the mUVE criterion,the spectra quality can be improved by elimination of matrix background and high-frequency noise.

010

20

30

40

50

60Reference value (mg dL-1)P r e d i c t i o n v a l u e (m g d L -1)Fig.8Correlation of prediction

values and reference values

yield by SPA-PLS model for the

NIR data of plasma

samples.P r e d i c t i o n v a l u e (m g d L -1)Fig.9Correlation of prediction

values and references value

yield by mUVE-SPA-PLS model

for the NIR data of plasma

samples.

SPA is followed to select informative variables with minimum colinearity.The method is applied to two NIR spectra data sets for basis study of human blood glucose noninvasive measurement:plasma samples and human OGTT samples.The results indicate that:

(1)In the case of low SNR,direct SPA is useless for multivariate calibration (the RMSEP

is 3.5mg dL ?1and 27.3mg dL ?1for the NIR spectra data sets of plasma samples and human OGTT samples respectively),especially when the interference is great and the NIR spectra signal of glucose is faint.mUVE method is helpful to improve spectra model and make the relationship of spectra model and glucose more closely by elimination information unwanted,such as the noises and the great optical absorption of water in NIR region,which can benefit to SPA selecting the representative variables for the faint glucose information;

(2)Only several original variables selected by mUVE-SPA can make regression models simpler and easier to have physical interpret.And the hybrid method can result in a substantial improvement on the prediction precision for blood glucose noninvasive measurement using NIR spectroscopy,and in this research the RMSEP is decreased to

1.8mg dL ?1and 5.0mg dL ?1for the two NIR spectra data sets respectively;

(3)

mUVE-SPA method has good adaptability and stability for complex spectra model of human OGTT samples NIR experiment,which has good potential to human blood glucose noninvasive measurement using NIR spectroscopy.

Reference value (mg dL-1)

P r e d i c t i o n v a l u e (m g d L -1)Fig.10Correlation of prediction

values and reference values

yield by SPA-PLS model for the

NIR data of human OGTT

samples.

P r e d i c t i o n v a l u e (m g d L -1)Fig.11Correlation of prediction values and reference values yield by mUVE-SPA-PLS model for the NIR data of human OGTT samples.

Acknowledgements This work was supported by the National Natural Science Foundation of China(No. 60708026),Talent Cultivation Foundation of Beijing((No.20081D1600600348),the Program for Changjiang Scholars and Innovative Research Team in University(No.IRT0705),and the Innovation Foundation of BUAA for PhD Graduate.And the experimental part of plasma was supported by the biological optics and spectroscopy application laboratory of Tianjing University.

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