AN ARTIFICIAL INTELLIGENCE CENTRED MULTIVARIATE ANALYSIS FOR
BLOOD GLUCOSE DIAGNOSIS
1Senthil
Kumar. A, 2Kavitha.S
Professor and Head/EEE, Dr. Mahalingam college of engineering & Technology,
Pollachi, India.
Assistant professor, Dr. Mahalingam college of engineering & Technology, Pollachi, India
Abstract: -Diabetes is a disease of disquiet evolving as one of the major health care epidemics of
contemporary era. Hence Painless control of blood glucose levels would improve the quality of life,
propounding better ruling of hyperglycaemia and hypoglycaemia thereby avoiding the complications
of present day lancet methods. Although many efforts have been taken by researchers in order to
successfully launch a device that measures blood glucose noninvasively, the results seems to be
intruded due to mismatch in correlation with the present day devices. This is so because the Noninvasive device designers mostly concentrate on optical methods which suffer from greater
interferences due to the less absorbing property of glucose. Aim of the paper is to draw together two
different optical techniques namely Absorption photometry and photo acoustics using embedded
technology, so that the results obtained produce better correlation after data handling with artificial
neural network. Baseline pre-processing is used which will eliminate the errors due to instrument
handling and temperature instability. Results obtained shows acceptable precision but in order to get
satisfactory standards, Non-invasive glucose monitoring requires further efforts.
Keywords:Diabetes, LASER light, PhotoAcoustic, Transducers, Photodiode, Neural network.
Diabetes is a major disease of concern
in 21 century due to the lack of exercise and
poor diet practices of people. World Health
Organization has stated that the count of
diabetic people is expected to grow from 246
million in 2006 to 380 million by 2005 and
this figure would even rise to 438 million by
2030. So it’s the right time for the Researchers
to design a continuous blood glucose
monitoring system that makes salutary
alertness among the people about the
terrifyingly growing disease of concern, so
that people are barred from becoming diabetic.
The present day technologies such as
Spectrophotometric measurement or use of
lancet devices require fresh blood samples
ranging from few millilitres to few drops
which limit continuous monitoring[1]. These
methods are invasive and eluded by people
due to the ins and outs such as pain involved;
time consumed and increased recurring cost
per measurement and also potential risk of
spreading diseases like Hepatitis, HIV through
interaction of needle with biotic fluids of
st
human skin. All these aspects makes prime
craving for what is called Non-invasiveness.
Diabetes mellitus is a condition of
deficiency rather than a disease because people
are said to be diabetic when their Pancreas (a
gland in the digestive system) no longer
produces insulin or when cells in the body stop
responding to insulin. Insulin is used by
human body to break and convert the raw food
particles into glucose; this glucose provides
energy which acts as fuel for human cells to do
their work actively. Diabetes is classified into
four types: Type 1 Diabetes: In this case the
pancreas no longer produces insulin. This
mostly occurs in childhood. Type 2 Diabetes:
In this case, cells do not effectively use insulin
that is produced. This accounts for vast
majority of cases for about 90%-95% and is
diagnosed in latter part of life at the age of
forty. Gestational diabetes: This type is less
common and occurs during pregnancy. Other
forms of Diabetes: This includes Pre-Diabetes,
Congenital Diabetes, Cystic fibrosis related
Diabetes, Steroidal Diabetes. Out of all these
first two types are most commonly noticed
ones. The symptoms of Diabetes include
frequent urination, unusual thirst, weight loss,
unusual hunger, fatigue, blurred vision, etc…
Glucose Diagnosing can be carried out under
three ways: - Invasive technique, minimally
invasive
technique
and
Non-invasive
technique[2].
Glucose monitoring is a crucial factor for
self-management of diabetes. Thus the
invasive or Semi-invasive technique seems to
be annoying because of the cost and pain
involved and the common people make use of
this diagnosis at no other way. So, Noninvasive blood glucose measurement is mostly
expected to take over the era, because it
involves no pain. This can be accomplished by
two ways either optically or Non-optically.
Non-optical means of measuring blood
glucose involves acquiring glucose values
from Sweat, breathe, tears or urine which does
not directly reflect blood glucose value and
hence most of the research works concentrates
on optical means[3–5]. By optical means, we
can consider two methods for measuring blood
glucose, one by using optical activity (such as
polarization) and other by determination of the
refractive index of the glucose. Both of the
above methods can be used to determine
glucose concentration with varying degree of
sensitivity and ranges of applicability.
One of the challenging tasks of Noninvasiveness is detection of weak blood signals
that lose energy through intervening tissues
and in addition separating glucose’s
information
from
other
overlapping
constituents with much higher concentration
(protein, urea, uric acid, haemoglobin, water,
etc…) this task can be performed by using
high quality transducers or sensors, so that the
sensors can measure either directly based on
chemical structure of glucose or indirectly by
measuring blood sugar based on secondary
terms like temperature or PH changes[6, 7].
The great volume of research works at
present concentrates on developing a Noninvasive device using one of the following
techniques such as: - Reverse Iontophorosis,
Polarimetry, Metabolic Heat Conformation,
Ultrasound Spectroscopy, Thermal Emission,
Electromagnetic technique, Photo acoustics,
Raman Spectroscopy and Bio-impedance
Spectroscopy. Few of the works concentrates
on Multivariate analysis for monitoring of
blood glucose. Out of the above mentioned
techniques Complex Impedance and Optical
Spectroscopy shows most promising results.
Appealing works based on impedance
spectroscopy was proposed by Caduff’s group
in 2003 but a limitation of this work is that it
requires an equilibrium process, where the
patient must rest for at least 60 minutes before
going for measurements[8,9].
Apart from the technology one must also
rely on several influencing factors like skin
temperature, pressure, sampling duration,
breathing artifacts, blood flow rate, body
movements,
tissue
thickness,
surface
roughness, sweat and skin colour while going
for calibration of such device. In this paper
two of the optical techniques namely
Absorption Spectroscopy and Photo acoustics
Spectroscopy because of their superiority,
have been compared and considered for
refinement in order to improve the efficiency
of the equipment. This paper deliberates the
design and outcomes of Photoacoustic
spectroscopy technique in section II and the
upshots of absorption spectroscopy in section
III.
II. PHOTOACOUSTIC SPECTROSCOPY
In 1880 Alexander Graham Bell
testifiedthe transfiguration of light energy into
acoustic energy by PhotoAcoustic (PA) effect.
Later on in 1970s, Allen brothers furnished RG theory that added hypothetical credit to
Photoacoustic effect in solids. In PA
technique, the glucose in blood is agitated by
short spell LASER pulse. Light absorption
produces stress in the internal tissues, ensuing
in heating of the medium as a result of which a
pressure wave is generated that travels
outwards that can be picked up by a
transducer. The amplitude of the pressure
wave gives the concentration of glucose. In
concurrence with Beer Lambert law, when a
laser beam having light intensity ‘I’ is incident
on the tissue, its intensity decreases
exponentially as a function of penetration
depthdue to the absorption of light energy by
the medium.
Figure1. PhotoAcoustic signal generation
mechanism
Figure1 shows the mechanism for PA
signal generation in the human tissue and the
amplitude of the pressure wave generated is
given by the following equation.
Where
R = the distance betweenthe PA source and
the observation point
α = 1 + light penetration depth
β = thermal expansion coefficient
Cp = specific heat
τL = optic pulse width
v = acoustic velocity
From the equation it’s clear that the glucose
concentration dependent terms are β, v and
Cp.Its found that the terms β and v increases as
glucose concentration increases but Cp
decreases as glucose concentration increases.
As composition of blood is complex with
many constituents the wavelength must be
chosen with judicial care. This region ranging
between 600nm – 1300nm is called the optical
window of biological tissue. In this region the
penetration depth of light is several
millimeters in the tissue [10-12]. Absorption
of light by oxy-haemoglobin and deoxyhaemoglobin are different above 1000nm but
the difference is fairly small around 900nm so
this wavelength range around 900nm is best
opted for glucose measurement optically.
Piezoelectric Transducer made of
Lead Zirconate Titanate (PZT) has been made
use of for detecting the PA signal . PA signal
can also be given the provision of being stored
and processed using a Digita Storage
Oscilloscope (DSO) at specific intervals.
Output of the transducer is amplified by Low
Noise
Amplifier
(LNA).
Baseline
preprocessing is done to filter random noise
and interferences by averaging the PA signals
over 1024 frames to achieve appreciable
Signal to Noise Ratio (SNR). Oral Glucose
Tolerance Test (OGTT) was carried out on
several entities for testing the system
functionality and the PA signals were recorded
at regular intervals and processed using Matlab
to obtain the peak value. The Peak values were
plotted and was compared with the measured
values which provided with nearly equivalent
values. Thus PhotoAcoustic technique has
been used to design a new prototype for noninvasive measurement of blood glucose.
III. ABSORPTION SPECTROSCOPY
Absorption
Spectroscopy
is
a
workhorse expertise extensively used in many
research fields mainly due to its innovative
approach. This technique involves light source
to impinge light on to the subject under study
and a photo detector for processing the sensed
signal. This method of measurement seems to
be attractive and smart because of the
advantages like: easy and convenient
operation, low cost, safety, increasing
availability of LASER light source and real
time assessment. Huge number of studies have
been proposed in visible (600-2500 nm) and
Near Infra-Red ranges. These wavelengths are
found in the remedial window for deep tissue
measurements[13,14].
The light focused on the skin is partly
absorbed and scattered due to its interaction
with the chemical components present in the
tissue. Attenuation of light is given by I = IO
exp (-µeff d), where I and IO are reflected and
incident light intensity and the effective
attenuation coefficient is given by µeff = f (µa,
µs), where µa and µs are absorption and
scattering coefficients respectively. Changes in
glucose concentration affect µa and µs and
hence the intensity of light, I gets varied[15].
When a person tries to diagnose
his/her blood sugar level, he/she must initially
switch on the LASER light operating under
visible range and place the finger on the sensor
and the reflected light intensity will be
detected by the photodiode. The photodiode
converts the received light intensity into
electrical analog voltage. In order to get larger
variants and to set apart, it was necessary to
produce the digital equivalent of the analog
voltage which was accomplished using an
ADC. The setup was tested for about 250
patients in JEBI Diagnostic Centre at repeated
intervals for different categories of people –
Normal, Hyperglycaemia and hypoglycaemia
persons and it was compared with
Spectrophotometric value obtained from the
same lab. To our surprise the peak values
likely matched the real values for most of the
cases. The digital equivalent was converted
into direct blood sugar value in mg/dL by
normal extrapolation and interpolation
methods and was used to frame a Look-Up
Table (LUT) which provided a correlation
coefficient, R of 0.71.
IV. ARTIFICIAL INTELLIGENCE IN
GLUCOSE DIAGNOSIS
As an improvement of the above
work, this paper highlights the use of artificial
intelligence based Neural Networks to
diagnose blood glucose values. The network
makes use of the lab data (blood glucose value
in mg/dL) taken as target vectors and the
digital value obtained from the photodiode as
input vectors to train the network.LevenbergMarquart back propagation algorithm is used
here to update the weight and bias values. This
Algorithm is highly recommended as a first–
choice supervised learning algorithm because
of its fastest computational capability. The
working of neural network in weight and
threshold updation is shown below in figure 2.
Figure 2. Neural Network Block Diagram
Table2. Comparison chart of photodiode output
(in volts) & conventional meter (mg/dL)
S.no
1
2
3
4
5
must
Person
Conventional
Meter Value
(mg/dL)
Photo
Digital
Diode
value
Output
From
(In
ADC
Volts)
A
79
4.82
1013
B
87
4.70
1004
C
106
4.61
997
D
112
4.42
990
E
139
4.36
959
Initially the input vector and test vector
be provided and the bias and weight are
assigned. Depending on the error the weight
and threshold must be adjusted to improve the
accuracy. The weight change rule is developed
from the perceptron learning rule.The output
unit error is used to alter the weights on the
output unit. The hidden layer errors can be
calculated by back propagating the errors at
the output unit and the hidden layer weights
are altered with those errors. For each data set,
the forward pass and backward pass is
continued until the error become tolerable.
Unlike other networks, in back propagation
network the errors can be back propagated to
the hidden layers, so that more accurate results
can be obtained.
In this paper a single layer network is
considered, that means one input layer, one
hidden layer and one output layer. To get the
more accurate results hundreds of iterations
should be done to achieve the target output.
The training vectors constituted the
readings of about 250 persons taken from the
lab from both the proposed prototype and the
conventional apparatus. Thus the conversion is
done and the look up table is constructed from
the neural network and the look up table is
burned into an external EEPROM and
interfaced to the microcontroller to display
direct blood glucose value in mg/dL. The
performance and result obtained from neural
network training in MATLAB is shown in
figure3 and 4 respectively.
Figure 4. Matlab Output of Trained Network
V.RESULTS AND DISCUSSION
Testing of the Non-invasive device
after training with neural network was done
for about 50 subjects in thesame diagnostic
centre and the results obtained for each person
was cross checked with the conventional blood
test taken in the same lab and it was found that
the device correlated well with the improved
correlation coefficient, R of 0.91.
Table 2. ADC value
For the Fasting and Post Prandial blood sugar
NAME
A
Figure 3. Performance chart of
TrainedNetwork
BLOOD
SUGAR
ADC
VALUE
VALUE
( mg/dl)
FASTING
167
939
BLOOD
SUGAR
ADC
VALUE
VALUE
(mg/dl)
POST PRANDIAL
222
860
B
170
968
237
834
C
274
854
307
824
Table 2 indicates the variation in ADC
value as the fasting and post prandial blood
sugar values vary. The experimental setup
showing direct blood glucose reading in
mg/dL is shown in Figure 5 respectively.
Table 3 shows the readings obtained in the
laboratory using the newly designed Noninvasive blood glucose monitor for about 15
persons out of50 subjects.
The deviations for one or two persons
may occur due to the reasons such as: - tough
skin tone, very thick skin surface and noise
due to external light source. The errors might
have occurred because there is no means of
aligning the light source with the 1 mm2 area
of photodiode. So there is a chance for
scattering to take place.Figure 6 shows the
accuracy between conventional and newly
developed prototype.
Figure 5. Image of the Proposed System
Displaying the Output after Interfacing
Table 3. Comparison between Conventional
and Designed Prototype
Conventional Experimental Deviation
S.No Blood Sugar
Kit
(δ)
Value(mg/dL) Value(mg/dL)
1
72
72
0
2
102
102
0
3
112
110
2
4
128
126
2
5
134
136
2
6
137
137
0
7
147
145
2
8
183
154
29
9
187
185
2
10
200
201
1
11
229
224
5
12
263
250
13
13
270
270
0
14
278
275
3
15
263
270
7
SUGAR VALUE
300
250
Conventiona
lab meter
accuray
200
150
New LASER
OPTIC
device
accuracy
100
50
0
1
3
5
7
9
11 13 15
PERSONS
Figure 6. Graph Showing Accuracy of
Conventional and New Prototype developed
VI. FUTURE ENHANCEMENTS
Thus a device for measuring blood glucose
non-invasively using a novel approach is
designed and it is found to be very simple,
painless and much economical. The result of
the proposed Non-Invasive equipment is
comparable with the conventional lab results.
Compared to the previous works, in this
proposal the accuracy has been increased to a
greater extent with much less cost eliminating
the need for complex digital processing circuit
and the reference signal. More over the
prototype has been tested, for about 50 persons
in the JEBI Diagnostic lab out of which above
35 persons got near accurate value with
±3mg/dL to 5mg/dL and other 15 persons with
±20mg/dl deviations which are advised to be
tolerable.
In conclusion this project has
suggested a means for Non-invasive diabetes
diagnosis. Though it’s at the initial stage it will
take a lead in near future eliminating the need
for Painful and costly test strips. The proposed
system accuracy is limited due to varying skin
thickness of the persons using the system and
also due to the external light disturbances that
affects the sensitivity of the photodiode. So the
future work may rely on the usage of hybrid
optical (optical-electrical) technique to reach
100% accuracy.
Hence the future work may possibly aim
to bring together both the absorption
spectroscopy and PhotoAcoustic techniques in
to a single block and making use of the fuzzyGenetic algorithm such as fire/flight to make
best decision and then to train the neural
network to get 100% accurate Non-invasive
device as a boon to the people for diabetic
diagnosis.
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