Transcript Проект для научной выставки

Presentation for WSC-8
The thermospectroscopic noninvasive glucometer
Sergey S. Krivenko
Anatolii A. Pulavskyi.
RPC “BioPromin”,Kharkov,Ukraine
Scope
The present document describes the detailed mapping a non-invasive easy-to-use blood glucose
meter. Diabetes mellitus is a chronic disease which is caused by deficit of insulin producing
by the pancreas or ineffective assimilation of the insulin by a body. There were 171 million
diabetics in the world as for 2000. 366 million diabetics were forecasted for 2030 [World
Health Organization].
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Abstract
The control of blood glucose concentration is a first-priority task, aimed at prevention of
complication, related to the consequences of diabetes mellitus. Existing methods of
controlling blood glucose concentration are invasive, i.e. requiring blood sampling (usually,
fingertip capillary blood). These methods have a number of restrictions, and main of them
is pain during carrying-out of an analysis. There is a non-invasive biochemical blood
analyzer AMP in medical practice, which uses the method of Professor Malykhin. It is
characterized by reliability, efficiency and acceptable accuracy. But this analizer uses five
spectral sensors and requires trained medical personnel to properly use. In addition, it is not
fully using the spectral information obtained from sensors.
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Objective of the work
The objective of our work is to create non-invasive home-used blood glucose meter that does
not require medical staff for its operation and need only two sensors. In addition, the
method uses the spectral information coming from sensors entirety. glucose meters.
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Agenda
• Methods for monitoring of blood glucose concentration
• Biological environment model
• The thermospectroscopic noninvasive glucometer prototype
• Results
• Conclusions
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The structure of primary medical
care (according to WHO)
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Primary patient’s measures and invasive
methods disadvantages
Primary measures, aimed at
diagnosis
 blood glucose concentration
control
 blood pressure control
 chiropody
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Disadvantages of invasive
methods
 painful during blood sampling
 possibility of a patient’s
infection
 daily fingertip puncture creates
nuisances in everyday life
 an average diabetic makes less
than 2 tests per day instead of
recommended 4-7 tests
WSC-8 Krivenko S.S., Pulavskyi A.A.
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Light and skin
Interaction between light and
skin
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Thermally stabilized spectra of left
armpit
𝑻𝒆𝒓𝒎𝒐𝒔𝒑𝒆𝒄𝒕𝒓𝒖𝒎 = 𝒇(𝑺𝒑𝒆𝒄𝒕𝒓𝒖𝒎; 𝑯𝒖𝒎𝒊𝒅𝒊𝒕𝒚; 𝑻𝒆𝒎𝒑𝒆𝒓𝒂𝒕𝒖𝒓𝒆; … 𝒐𝒕𝒉𝒆𝒓𝒔 𝒔𝒆𝒏𝒔𝒐𝒓𝒔)
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Multivariate modeling
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Stages of developing mathematical model of
glucose concentration analysis
Preparation phase
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clinical data collection – patients’ blood sampling
testing of glucose concentration in blood samples
measurement of thermo-stabilized spectra in the corresponding points
splitting of spectrum combination into two parts – calibration set and validation set
(duplex, Kennard-Stone method, histogram method)
spectra preprocessing
(Multiplicative scatter correction /standard deviation,
differentiating, nonlinear filtering)
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Stages of developing mathematical model of
glucose concentration analysis
Calibration phase
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choice of a calibration method (PLS,RBF-PLS or ANN)
testing of glucose concentration in blood samples
model development in increasing order of complexity/increase in the number of factors
prediction of concentrations using developed models for calibration data set and
comparison of results with reference data (clinical analysis)
identification of outliers and bad values, and their removal
model recalibration with removed outliers
evaluation of root mean square error of calibration (RMSEC) and cross-validation of
each model. Formation of model complexity dependency on mean square error to define
optimal number of model factors (components)
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Stages of developing mathematical model of
glucose concentration analysis
Validation phase
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prediction of concentrations using developed models for validation data set and
comparison of results with reference data (clinical analysis)model recalibration with
removed outliers
evaluation of root mean square error of prediction (RMSEP) for each model. Formation
of model complexity dependency on mean square error to define optimal number of
model factors (components)
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Neural Network Architecture Based on MLP
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ANN or RBF-PLS?
RBF-PLS (B. Walczak et al.) works better than
classical 3-layer perceptron in OUR case. Why?
We don’t know.
We're just users, which use chemometrics.
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Experiment terms
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Total patients' number – 238
Patients' age – 16-77 years
Patients belonged to Arab, Chinese, Slavic and Finno-Ugric groups
Diabetes: I and II types
Glucose concentration: 2-29 mmol/l
Calibration group – 166 patients, validation group – 72 patients
Hematocrit was not measured!
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Patients’ age histograms
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Glucose concentrations’ histograms
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Some comparison of results
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PLS
RBF-PLS
R2
0.4
0.9
RMSEP
3.78
1.34
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Experiment's result. Table with some values
(according to RBF-PLS)
Glucose concentration (clinic analysis), mmol/l
10
10
5,5
5,5
5,44
7,2
15,6
15
5,5
5,5
4,2
4,2
5,3
4,3
5,2
5,2
5
5
7,1
7,1
5,1
6,5
5,1
6,6
6,3
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8,9
Glucose concentration (model analysis), mmol/l
9,130446
9,942483
6,325168
5,429411
5,424803
6,255659
14,33417
10,79327
6,084031
6,087009
4,249031
4,193115
5,502326
3,69455
8,479128
5,508318
5,458402
5,138747
7,333616
7,357808
5,154192
6,444498
6,184803
6,567375
6,024245
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A.A.
Difference between clinical and model analysis
0,869554
0,057517
0,825168
0,070589
0,015197
0,944341
1,265825
4,206729
0,584031
0,587009
0,049031
0,006885
0,202326
0,60545
3,279128
0,308318
0,458402
0,138747
0,233616
0,257808
0,054192
0,055502
1,084803
0,032625
0,275755
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0,082031
Experiment's result. Clarke error grid
RMSEP=1.34; R2=0.9
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Experiment's result. Parkes error
grids for type I and II diabetics
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Experiment's result. ISO 15197:2003 tolerance
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Embedded calculate
RBF-PLS
Glucose
Sensors data
T=62,4 second
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Conclusions
A method for noninvasive evaluation of the concentration of glucose in human blood was
proposed. The advantages of this method is using of two sensors only, possibility of
frequent analysis, simplicity of operation by untrained user, which makes it suitable for selfdiagnosis. One of the best properties is the space robustness - the device is relatively
insensitive to the location of measurement points, that compensates the missing data by
timed analysis. Clinical analysis of the results showed their correctness – 100% of results lie
within tolerable limits of the generally accepted clinical error grids, 87.5% of recommended
95% comply with ISO 15197:2003.
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Acknowledgements
I want to thank
1) Oxana Rodionova – she helps me for 5 years. I’ve started
my chemometrics way with she. She is a beautiful woman.
2) Kim Esbensen – his book is my “bible” on chemometrics
3) Beata Walczak – RBF-PLS is cool approach. I like it.
4) WSC-8 – I feel a free breath with it
Thank you!
But…Could you write your question because of my English

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