USING NOISE FILTERING AND WEARABLE BIOSENSOR …€¦ · of biosensors/sensors used in wearable devices for monitoring the content of various metabolites of biological fluid. This

http://www.iaeme.com/IJCIET/index.asp 2014 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 11, November 2018, pp. 2014–2033, Article ID: IJCIET_09_11_199

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

©IAEME Publication Scopus Indexed

USING NOISE FILTERING AND WEARABLE

BIOSENSOR PLATFORM SIGNAL QUALITY

IMPROVEMENT SYSTEM FOR

SIMULTANEOUS MEASURING THE GLUCOSE

AND LACTIC ACID CONTENT IN SWEAT ON A

MOVING OBJECT

I. I. Koltunov

Doctor of Technical Sciences, Professor,

Moscow Polytechnic University,

Scientific Secretary, Moscow, Russian Federation

A. V. Panfilov

Candidate of Economics,

General Director of ―Tradition group LTD,‖

Moscow, Russian Federation

I. A. Poselsky

Head of NTC ―Automated technical systems‖,

Moscow Polytechnic University,

38 Bolshaya Semenovskaya Str.,

Moscow, Russian Federation, 107023

N. N. Chubukov

Project Manager of ―Tradition group LTD,‖


S. V. Baranenko

Engineer of ―Tradition group LTD,‖


ABSTRACT

The purpose is to estimate the parameters of enzyme biosensors for the

determination of glucose and lactic acid and their use in a continuous flow collection

system installed on a volunteer. A system for processing signals of biosensors was

created to register them. The purpose was to increase the signal-to-noise ratio of the

used devices.

Using Noise Filtering and Wearable Biosensor Platform Signal Quality Improvement System for

Simultaneous Measuring the Glucose and Lactic Acid Content in Sweat on a Moving Object


Key words: Biosensor, Metabolites, Enzyme, Glucose, Lactic Acid, Non-Invasive

Monitoring, Cuvette System, Open Continuous Flow System, Chronoamuperimetric

Dependence, Potentiostat

Cite this Article: I. I. Koltunov, A. V. Panfilov, I. A. Poselsky, N. N. Chubukov, S. V.

Baranenko, Using Noise Filtering and Wearable Biosensor Platform Signal Quality

Improvement System for Simultaneous Measuring the Glucose and Lactic Acid

Content in Sweat on a Moving Object, International Journal of Civil Engineering and

Technology (IJCIET) 9(11), 2018, pp. 2014–2033.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11

1. INTRODUCTION

One of the modern trends in the creation of biosensor and sensor systems is the development

of biosensors/sensors used in wearable devices for monitoring the content of various

metabolites of biological fluid. This direction is being actively developed at present in

diagnostic technologies and relates to the implementation of non-invasive bloodless analyzes

[1, 2]. Such measurements employ, for example, saliva, tear fluid, sweat instead of blood. The

concentration of specific metabolites in them correlates with their concentration in the blood.

There are two essential metabolites, glucose and lactic acid [3-5], whose concentration in

one of the biological fluids, sweat, is directly proportional to the concentration in the blood.

Depending on its state, the composition and volume of sweat can vary widely in the human

body. It should be noted that the composition of sweat includes products of mineral

metabolism, sulfate compounds, phosphates, potassium chloride, calcium salts, as well as

products of protein metabolism: urea, lactic acid, uric acid, ammonia, some amino acids, and

volatile fatty acids. Thus, sweat can serve as a source of data on the content of these

compounds in the human body. Enzyme methods for the determination of analytes, including

glucose and lactic acid (lactic acid), provide high specificity, accuracy, and reproducibility.

Determination of glucose and lactic acid in sweat using enzyme biosensors is based on

recording the reaction of enzymatic oxidation of glucose or lactic acid in the presence of

dissolved oxygen.

Amperometric biosensors, which are based on the determination of hydrogen peroxide

released during the oxidation of the substrate use iron hexacyanoferrate (Prussian blue) as an

electrocatalyst for the reduction of hydrogen peroxide. Biosensors of the electrochemical type

based on the Prussian blue for the analysis of glucose (Gl) and lactic acid (La) were used to

register these components in the sweat of a person performing physical exercises. In

simulated conditions, biosensors had a sensitivity of 0.87 μA/mmol and 2.37 μA/mmol,

respectively, for the analysis of Gl and La. Biosensors were characterized by a signal-to-noise

ratio of 65 and 56 (Gl and La, respectively) when registering in a fixed-volume cuvette and a

ratio of 25-176 (for different glucose concentrations) and 25-39 (for different lactic acid

concentrations) in the continuous flow system.

Biosensors based on internal electrodes obtained by the method of graphite matrix

printing, Prussian blue, and immobilized enzymes glucose oxidase and lactic acid oxidase

were used for enzymatic detection of lactic acid (the basis of lactic acid oxidase) and glucose

(the basis of glucose oxidase). Biosensors had sensitivity in the cuvette measurement for the

detection of hydrogen peroxide of 8.07 μA/mmol, for the detection of lactic acid of 2.37

μA/mmol, and the detection of glucose of 0.87 μA/mmol. The Michaelis-Menten catalytic

constants are defined for biosensors — the Michaelis constant Km and the maximum reaction

rate value Vmax. The created biosensors were used to measure the content of lactic acid and

glucose in simulation and real samples of sweat. It was found that the content of lactic acid

I. I. Koltunov, A. V. Panfilov, I. A. Poselsky, N. N. Chubukov, S. V. Baranenko


and glucose in real samples of sweat amounts to the range from 7 to 45 and from 0.2 to 0.6

mmol, respectively, which is consistent with the literature data. The obtained results are new

and create a basis for the development of biosensors for the analysis of lactic acid and glucose

in the human body, considering the features of the application as part of a non-invasive

wearable biosensor platform in the hardware-software complex ―Body Sensor Network.‖

2. METHODS

2.1. Materials, Tools, Reagents

Potassium phosphate disubstituted 3-water, sodium hydroxide, potassium chloride, sodium

chloride,lactic acid, glucose, and acetic acid (MOSREAKTIV CJSC, Russia); ferric chloride

sulphide FeCl3 ∙ 6H2O, potassium hexacyanoferrate (III) K3 [Fe (CN) 6], hydrochloric acid

(Khimmed, Russia); low molecular weight chitosan (―Sigma-Aldrich‖, USA) were used in the

work. Enzyme preparations were used to create biosensors: Pediococcus sp. lactic acid

oxidase, lyophilized, 37 U/mg activity and Aspergillus niger glucose oxidase, activity 185000

U/g (Sigma-Aldrich, USA).

The biosensors were based on 3-pin electrodes obtained by a matrix seal

(KolorElectronics, LLC, Moscow, Russia). The deposition of Prussian Blue on the surface of

the working electrode was carried out from the reaction mixture composition: 0.1 M FeCl3

and 0.1 M K3 [Fe (CN) 6] in the background electrolyte 0.1 M KCl, 0.1 M HCl in accordance

with the procedure described in literature.

A 2% solution of chitosan in 1% acetic acid was used for immobilization of enzymes on

the electrode surface. The enzyme content on the electrode was 7 × 10–4 mg/mm2.

The measurements were carried out in a 1 ml cuvette. 25 mmol of potassium phosphate

buffer, pH 6.5, containing 10 mmol of sodium chloride was used as the base solution. The

volume of the applied sample (glucose) ranged from 2 to 100 μl. A universal portable

potentiostat, an EmStat galvanostat (PalmSens BV, the Netherlands), was used to record

electrode signals. The measurements were carried out according to the three-electrode

scheme. Chronoamperometric dependencies were recorded at a potential of 0 mV. Cyclic

current-voltage characteristics (CVA) were recorded at a potential scan rate of 3 mV/s in the

range from 500 to - 500 mV.

2.2. Measurement Systems

The research employed the following measurement systems: cuvette and continuous flow

systems. The measurements were carried out in a constant-volume cuvette with stirring

(cuvette system) and in a continuous flow system. The continuous flow system injects the

sample into the jet of the primary buffer solution using an injection device (―injection‖) and

reaches the measuring electrode/biosensor during time dependent on the flow rate. Sample

measurement occurs over time, also dependent on the flow rate.

A particular open continuous flow system (OFS) was also used to measure the samples.

When developing this system, the constructions described in the cited works were considered.

The originality of the approach was based on the application of fluid flow (sweat) due to

hydrostatic pressure, which was created due to the difference in levels at the ―inlet‖ and

―outlet‖ points. The second original solution to the problem was injecting a fluid channel

(moisture path) into the design, which ensures uniformity of the fluid flow.

The system had the form of a cartridge containing a biosensor, solution supply channels,

and a sweat selection system. The OFS was made based on an organic substrate (in this case

organic glass was used) and contained a channel-fixer for a biosensor having a size of 10 mm

× 65 mm × 0.5 mm and a moisture-conducting track with a high level of hygroscopicity.




GF/A chromatographic paper (Whatman) was used for this purpose. The moisture conducting

path was placed in the fixing channel in the space between the surface of the biosensor and

the surface of the cuvette (skin).

3. RESULTS

3.1. Characteristics of Biosensors for the Detection of Glucose (Gl) and Lactic

Acid (La) in Simulating Artificial Sweat by Injecting Various Concentrations of

Gl and La (Measurements in a Constant Volume Cuvette) into the Sample

3.1.1. Glucose Oxidase Immobilization

A method for immobilizing a biomaterial on the surface of an EMF into a chitosan gel has

been developed for the high-quality formation of the receptor element and ensuring the long-

term operation of biosensors. An optimal ratio of the enzyme and the immobilizing agent was

selected, ensuring at the same time the duration of the sensor operation and not impeding

access of the analyzed compounds to the sensor surface.

Table 1 presents data on sampling. Fig. 1 shows the general nature of the dependency;

Table 2 gives an idea of the kinetic constants and parameters of the used biosensor for the

detection of glucose.

Table 1 Reagents ratio in the mixture applied to the electrode with Prussian blue to immobilize the

enzyme

Sample number Chitosan, µl Glucose oxidase (10 mg/ml), μl Buffer, µl Response to 1 mmol of H2O2, µA

1 1 1 8 0.53

2 2 1 7 0.56

3 3 1 6 0.70

4 4 1 5 0.73

5 5 1 4 0.77

6 6 1 3 0.60

7 7 1 2 0.59

8 8 1 1 0.46

9 9 1 0 0.17

Figure 1. The dependence of the biosensor signals on the enzyme and chitosan ratio on the electrode surface

with Prussian blue (signals are highlighted in red in the first days after the biosensor has been made, in green

after a week of storage at +4С)



Table 2 Characteristics of the obtained electrodes (for the cuvette measurement method)

Parameter Glucose oxidase concentration at the electrode, μg/mm2

10 1 0.1

Equation describing the

calibration dependence max0

h

h h

M

V SV V

K S

,

Where 0V= 0.12; maxV

=

1.58;

h = 6.37; MK= 0.58;

R2=0.99

max0

h

h h

M

V SV V

K S

,

Where 0V= 0.07; maxV

=

1.39; = 2.08; MK= 1.22;

R2=0.99

max

h

h h

M

V SV

K S

,

Where maxV= 5.11;

h = 0.60; MK= 100.8;

R2=0.99

Linear detection range,

mmol 0.02 - 1 0.02 - 3 0.02 - 1

Regression equation for

linear plot y=1.6452x-0.0878 y=0.4385x+0.0611 y=0.3476x+0.0199

Correlation index R2 0.97 0.99 0.97

Sensitivity coefficient,

µA/mmol 1.65 0.44 0.35

Minimum detection limit,

mmol 0.02 0.02 0.02

Detection range, mmol 0.02 - 1 0.02 - 3 0.02 - 10

Variation coefficient, % 3.4 4.0 3.8

Notes.

0V= equation constant at zero substrate concentration, maxV

- maximum reaction rate, h -

the Hill constant, MK- the Michaelis constant, S - substrate concentration, R2 - correlation

coefficient.

Figure 2. Calibration curves for electrodes with different amounts of glucose oxidase enzyme on the

electrode surface: 1 – glucose oxidase concentration 10 μg/mm2; 2 – 1 μg/mm2; 3 – 0.1 μg/mm2; 4 –

0.01 μg/mm2

Fig. 2 shows the resulting calibration dependencies for various enzyme concentrations.

h




3.1.2. Lactic Acid Oxidase Immobilization

The lactic acid oxidase immobilization uses the same mixture of the enzyme with chitosan, as

for the glucose oxidase with a volume ratio of reagents in the applied mixture of

―enzyme:chitosan:buffer,‖ equal to 1:5:4. The result of measuring lactic acid and glucose is

presented in Table 3.

Table 3 Estimation of the measurement error of glucose and lactic acid when using the cuvette system

Measurement number Biosensor for Gl detection Biosensor for La detection

No. 1 No. 2 No. 3 No. 1 No. 2 No. 3

1 0.12 0.13 0.14 0.95 0.89 0.89

2 0.13 0.13 0.12 0.92 0.88 0.95

3 0.15 0.14 0.12 0.91 0.90 0.93

4 0.14 0.13 0.14 0.94 0.93 0.91

5 0.15 0.15 0.12 0.87 0.86 0.90

Average, µA 0.138 0.136 0.128 0.918 0.892 0.916

Dispersion, µA 0.013 0.009 0.011 0.031 0.025 0.024

Measuring error, % 9.45 6.58 8.56 3.39 2.90 2.63

Signal / standard deviation 65 65 65 56 56 56

The signal-to-standard deviation ratio was used to assess the signal-to-noise level under

various registration schemes since the value of the standard deviation characterizes the noise

level characteristic of this registration

When registering the dependence of the current on time, the signal/standard deviation

ratio was obtained for a given concentration of La, which is 56, which can be taken as a

satisfactory value of the parameter for the cuvette system, allowing reliable measurement of

concentrations.


ratio was obtained for a given concentration of Gl, which is 65, which can be taken as a

satisfactory value of the parameter for the cuvette system, allowing reliable measurement of

concentrations.

The presented data show that both for the biosensor for determining Gl and for the

biosensor for determining La have the measurement error of no more than 10%, which can be

taken as a satisfactory result for the cuvette measurement system.

Note. Methods of collection and biochemical analysis should ensure stable non-invasive

detection of changes in the relative concentration of lactic acid and glucose in the human body

by more than 10%, including in conditions of environmental noise and the collection of

biological samples (sweat) in the routine of wearing.

Accuracy is determined by the standard error (SE), calculated by the formula:

SE =

Where the standard deviation and n is the number of measurements. Thus, it is possible

to increase the accuracy of the analysis to the required one by increasing the number of

measurements of the same concentration of a substance.



3.2. Measurement of Samples in the Continuous Flow System

Fig. 3 and Fig. 4 show the signals recorded when using a continuous flow measurement

system.

Figure 3. A glucose biosensor signal in a continuous flow measurement system. The arrows indicate

injecting of 1 - 1 mmol, 2 - 5 mmol, 3 - 10 mmol of glucose. The signal develops in 80-100 s after

injecting the sample into the system

Figure 4. The lactic acid biosensor signal in a continuous flow measurement system. The arrows

indicate injecting of 1 - 50 μl of 2 mmol of lactic acid, 2 - 50 μl of 2 mmol of lactic acid, 3 - 50 μl of 4

mmol of lactic acid, 4 - 50 μl of 4 mmol of lactic acid. The signal develops in around 80-100 s after

injecting the sample into the system

The result of determining La and Gl in the continuous flow measurement system is

presented in Table 4.




Table 4 Estimation of Gl measurement error when using a continuous flow system

Measurement number Biosensor for Gl detection Biosensor for La detection

1 mm 5 mm 10 mm 2 mm 4 mm

1 0.055 0.310 0.420 0.061 0.092

2 0.059 0.299 0.401 0.068 0.099

3 0.062 0.325 0.422 0.055 0.083

4 0.061 0.330 0.451 0.053 0.102

5 0.054 0.341 0.410 0.057 0.090

Average, µA 0.058 0.321 0.421 0.058 0.093

Dispersion, µA 0.004 0.016 0.019 0.006 0.008

Measuring error, % 6.12 5.17 4.48 10.10 8.08

Signal/standard deviation 25 135 176 25 39


ratio was obtained for these concentrations of La, ranging from 25 to 39, which can be taken

as a satisfactory value of the parameter for the continuous flow system.


ratio for a given Gl concentration was obtained, ranging from 25 to 176, which can be taken

as a satisfactory value of the parameter for the continuous flow system.

The presented data show that both for the biosensor for determining Gl and for the

biosensor for determining La have the measurement error of no more than 10%, which can be

taken as a satisfactory result for the cuvette measurement system.

It should be noted that these examples indicate an increase in the noise level in a

continuous flow cuvette compared to measurements in a constant volume cuvette and may be

objects of registration to which it is advisable to apply signal-to-noise ratio algorithms.

3.3. Measurement of Artificial Sweat Samples; Cuvette and Continuous Flow

Systems; Volunteer Measurement

The developed biosensors were applied in the analysis of real sweat samples collected from

healthy people under conditions of elevated ambient temperature. Between measurements,

samples were stored frozen at a temperature of -20 С. These samples were tested in two

measurement systems: in the cuvette system and the continuous flow-injection system.

3.4. Measurements in the Cuvette System

Fig. 5 shows a view of the lactic acid oxidase biosensor signal in a cuvette measurement

system for injecting a sweat sample.

Figure 5. Dependence of the current on time during the registration of lactic acid in the sweat sample (10 μl

injected)



When injecting 10 μl of sweat into the cuvette, the measurements are not reliable, since

the concentration of lactic acid in the cuvette, in this case, is outside the linear range of

concentrations of this biosensor. Therefore, the volume of the injected sample was reduced,

and the dependencies obtained are presented in Fig. 6.

Figure 6. The dependence of the current on time during the registration of lactic acid in the sweat

sample (entered 3 times in 2 μl of the sample)

The obtained lactic acid content in the measured sweat sample is shown in Table 5.

Table 5 Measurement of sweat sample for lactic acid content using a lactic acid oxidase biosensor

Injected, µl Biosensor readings,

mmol

Dilution of the

original sample

Lactic acid content

in the sample,

mmol

Mean measurement

error

2 0.0665 500 33.25 33.56±0.46 mmol; 1.4

% 4 0.1363 250 34.08

6 0.200 166.7 33.34

Thus, it can be concluded that the total lactic acid content in a sweat sample is measured

by the developed biosensor reliably (the error does not exceed 1.4%), and the result does not

depend on the dilution of the original sample within the linear concentration range.

Figure 7. Glucose oxidase biosensor signals for sweat injection: 1 - 2 μl of sweat, 2 - 10 μl of sweat, 3

- 50 μl of sweat, 4 - 20 μl of 63 mmol of glucose solution, which corresponds to the concentration of

glucose in a cuvette equal to 1.24 mmol




Fig. 7 shows a view of the glucose oxidase biosensor signal in a cuvette measurement

system for injecting a sweat sample. The glucose content in the sample was low, so the

biosensor signal for injecting the sample was almost not noticeable against the background of

high noise. Therefore, the volume of the injected sample was increased to 200 μl, and the

signal is shown in Fig. 8.

Figure 8. Glucose oxidase biosensor signals for injection of 200 μl of sweat

Fig. 9 shows a comparison of the glucose oxidase biosensor signals for injecting real

(sweat) and laboratory glucose samples. Thus, the glucose content of the measured sweat

sample is 0.360 mmol.

Figure 9. Biosensor signal for injecting 200 μl of sweat (blue) against the background of signals for

different glucose concentrations (red — 0.05 mmol, green — 0.1 mmol)

The tables below provide data on measurements of sweat samples for lactic acid content

(Table 6) and glucose (Table 7). 6 samples of sweat taken at different times from different

people are measured.



Table 6 Lactic acid content in sweat samples, measured using a lactic acid oxidase biosensor

Sample

number Signal, nA

Concentration in the

cuvette, mmol Dilution, times

Concentration in the sample,

mmol

1 154.89 0.076

250

19.0

2 92.94 0.0552 13.8

3 58.27 0.0388 9.7

4 244.47 0.116 29.0

5 333.69 0.1808 45.2

6 266.27 0.1284 32.1

Note: the table shows the average values of the five measurements.

Table 7 Glucose content in sweat samples, measured using a lactic acid oxidase biosensor

Sample

number Signal, nA


cuvette, mmol Dilution, times


sample, mmol

1 25 0.12

5

0.6

2 15 0.08 0.4

3 15 0.08 0.4

4 20 0.09 0.45

5 25 0.12 0.6

6 20 0.09 0.45

Note: the table shows the average values of the five measurements.

The lactic acid content in the samples varied. Thus, the glucose content reached 0.2-0.6

mmol in the range of in the range of 7-45 mmol.

3.5. Measurement in the Continuous Flow System

The OFS work was verified by measuring laboratory samples of lactic acid. The type of

signals received in this system is shown in Figure 10.

Figure 10. Types of lactic acid oxidase biosensor signals in OFS: 1 - injection of 5 μl of 50 mm of

lactic acid; 2,3 - injection of 2 μl of 50 mm of lactic acid; 4,5 - injection of 2 μl of 5 mmol of lactic

acid

The results obtained using the OFS, compared with measurements carried out using a flow

cell. Types of signals are shown in Fig. 4.




Thus, from the presented data it can be concluded that both designs of the cuvette require

significant modifications to reduce noise, increase the tightness of the cuvette, the accuracy of

sample dosing and the reliability of the system as a whole.

The OFS was tested to analyze real sweat samples. The type of signals received is shown

in Fig. 11.

Figure 11. Type of lactic acid oxidase biosensors signals when measuring real samples of sweat in

OFS. The numbers show the injection: 1-1 μl of sweat; 2 - 1 µl of sweat; 3 - 1 µl of sweat; 4 - 10 μl of

sweat; 5 - 50 μl of sweat

From the presented data it can be seen that the signal level of the lactic acid oxidase

biosensor does not depend on the sample volume, but depends only on the analyte

concentration. In this case, the lactic acid concentration in the real sample was 35 ± 2 mmol.

3.6. Evaluation of the Mutual Influence of Biosensors in Measuring Gl and La

Simultaneously

A device designed to measure sweat samples was located either on a model of a person’s

hand, pressing it against the surface of a convex glass cylinder with adhesive tape, or on that

part of the body (using a volunteer's forearm) that was used to model the device work. The

introduction of a sample that simulates the appearance of sweat and contains lactic acid in

various concentrations from 1 mmol to 100 mmol was performed in the feed channel of the

basic solution. Testing used the following modes: the buffer feed rate was 200 μl/min, which

should correspond to the rate of sweat entering the sump; the sample volume ranged from 1 to

50 μl. The matrix printing electrode containing the immobilized enzyme was connected to a

potentiostat. The tests conducted with biosensors for the detection of lactic acid are shown.

The results were similar for glucose detection. The type of system signals in real time is

shown in Fig. 13. The moment of the sample injection is marked. Dependence of the

amplitude of the signals on the concentration of lactic acid is visible.

Since it is assumed that two biosensors are simultaneously present in the wearable body

sensor, the influence of electrodes with lactic acid oxidase and glucose oxidase on the mutual

reading when they were in one measuring cuvette when recording a signal using two

potentiostats was considered. The measurements were carried out with an electrode in a 5 ml

cuvette. Electrode with glucose oxidase was connected through PSTrace, an electrode with

lactic acid oxidase through IPC potentiostat. The measurement results are shown in Fig. 13.

Initially, a) 1 mmol of glucose was injected into the measuring cuvette, then b) 1 mmol of

lactic acid, then c) a mixture of 1 mmol of glucose and 1 mmol of lactic acid (left). It can be



seen that the lactic acid oxidase electrode does not respond to the injection of glucose, and the

glucose oxidase electrode does not respond to the injection of lactic acid. In the next test, the

concentration of the input substrates was reduced 10 times. The dependence of the electrode

current on time was recorded for the injected 0.1 mmol of glucose and 0.1 mmol of lactic acid

(figure at the right). It is seen that both electrodes react to the injection of the mixture.

Figure 12. Simultaneous measurement of samples containing Gl and La

Thus, the developed lactic acid oxidase and glucose oxidase biosensors are capable of

measuring the concentration of substrates in a complex mixture and can work in parallel with

one sample. A mathematical processing algorithm, aimed at reducing the noise level, was

applied for the considered examples of registration.

3.7. The Use of Noise Filtering System and Improving the Quality of the

Bioelectrochemical Reaction Signal

It should be noted that along with the principle of recording the biological reaction of the

detection of glucose and lactic acid, based on the classical idea of measuring

bioelectrochemical signals, there is a somewhat tricky task of developing a system for

processing the measurement data. Such a system is used to build the calibration dependencies

of biosensors and to find the desired analyte concentration. The need to solve the problem

depends on the fact that when registering signals on a moving object, as it seems to be a

person, directly during the registration there are significant disturbances caused by movement.

They are associated with unpredictable random changes in the resistance of biosensors,

leading to the appearance of noise interference.

Measurements on a real object (the state of moderate and active movement was

considered) showed that with movement, interference increased. The quality of the

bioelectrochemical response signal should be sufficient, including the automatic evaluation of

the need for medical intervention for the operation of the sensor as a part of the body's sensor

network. The system response time should not exceed several minutes, preferably less than a

minute. Two areas of activity focused on the solution of this task were considered: noise

suppression and noise compensation at the stage of preliminary signal processing.




3.8. Filtering and Smoothing for Noise Suppression

The filtering and smoothing efficiency for noise suppression was evaluated. Smoothing turned

out to be a more efficient tool than filtering. For example, one can consider the results of

processing the original signal presented in Fig. 3, using a low-frequency filter with a cut-off

frequency of 0.1 Hz and quadratic smoothing, shown in Fig. 13 and 14 respectively.

Figure 13. Filtration

Figure 14. Smoothing

The reason for the low filtration efficiency is quite apparent - the spectra of noise and the

useful signal overlap.

Quadratic smoothing has proven to be quite a useful tool for suppressing noise from

biosensors in model laboratory conditions. Fig. 15 and 16 show the results of processing the

source signals shown in Fig. 4 and 5, respectively.



Figure 15. Smoothing of Fig. 4 data (lactic acid biosensor signal in a continuous flow measurement

system)

Fig. 15 and 16 show that the noise is suppressed quite effectively, and the noise existing in

the laboratory can be identified during subsequent processing.

The situation with interference is more complicated than with noise. Fig. 17 shows the

result of smoothing the signal with noise.

Figure 16. Smoothing of Figure 5 data (current versus time when registering lactic acid in a sweat

sample (10 μl injected))

Figure 17. Smoothing of Figure 6 data (dependence of current on time when registering lactic acid in a sweat

sample)




Judging by the source data, the duration of the interference can reach four minutes. In this

case, the smoothing efficiency is insufficient. Therefore, at the preliminary stage of

processing, smoothing the output signal of the sensor is a necessary but not sufficient

condition.

3.9. Signal against the Background Noise

Signal processing in the presence of interference is possible in the case when the output signal

of the sensor is the linear sum of the signal and interference, i.e., the presence of interference

does not affect the nature of the signal. In this case, this is not the case; as an example, we can

consider fragments of graphs presented in Fig. 6 and 7 (see Fig. 18). It can be seen that

interference compensation will not lead to an increase in the quality of signal processing since

in the presence of interference, the nature of the signal is significantly different from what it

was without interference.

Fragment of Figure 6.

Fragment of Figure 7.

Figure 18. Signal change in the presence of interference

From the above-said, it can be concluded that the signal is invalid and should be excluded

from further processing under the proposed conditions for conducting experiments during the

duration of the interference. Therefore, the time of the automatic response of the system based

on the output signal of the biosensor will depend on the nature of the interference when using

the proposed design.

3.10. Dynamic Range and Availability

As shown in clause 5.1, the biosensor can go beyond the linear range of measurements. This

situation should be recognized by the processing algorithm and trigger the appropriate action:

issuing a message, recommendations for replacing or bringing the biosensor into a working

state, which is determined by the conditions of use.

Processing a signal that has come into saturation is possible. It should be considered that

the signal from a biosensor that went beyond the limits of the linear range contains

information about the state of the sensor, and not about the measured parameter. The

presented baseline data make it clear that the biosensor does not enter the operating mode

immediately after the start of measurements. It may take up to two minutes. This peculiarity

must be considered when processing.



3.11. Signal Extraction for Processing

Part of the biosensor data may not be suitable for processing. As an example, Fig. 19 shows

the smoothed signal of the biosensor of Fig. 6 with allocated intervals at which the data is

invalid.

Figure 19. Intervals with an invalid signal (1 - process stabilization, 2 - interference)

The grounds for making a decision on the presence of interference and its duration against

the background of a quasi-stationary signal: the onset of interference is determined by the rate

of rising (decay) and the difference in amplitude from the average signal level in the previous

section without interference, the end is the duration of the falling front is close to the duration

of the leading edge and returns to the level that existed before the interference began.

When the duration of the interference is less than the threshold value determined by the

smoothing parameters, there may be traces of unrecognized interference in the signal (see Fig.

20). Processing parameters shall be chosen so that the magnitude of these traces does not

affect the reliability of determining the state of the biosensor carrier.

The decision on the presence of interference, its start time and duration is taken after

completion of the interference, i.e., retroactively, based on the data accumulated in the

session.

An approximation may be used to replace areas with invalid data. As an example, Fig. 20

shows the previous signal with pieces of a piecewise linear approximation (shown by dotted

lines).




Figure 20. Approximation (1 - traces of unrecognized interference)

3.12. Time Reference

The automatic decision on the state of the biosensor carrier can be made only in the time

interval when the signal can be recognized as quasi-stationary. Consequently, the moment of

the automatic formation of a decision on the biosensor carrier condition can be shifted relative

to real time by the sum of the interference time and the time required for determining the

signal quality after the end of the interference.

The presented experimental data show that the duration of the interference reaches four

minutes, it may take up to 30 seconds to determine the signal quality. As a result, the delay in

decision making may be about 300 seconds. It should be noted that the above estimate of the

delay relates only to the version of the biosensor design used to collect the experimental data

and for the interference that existed during the experiments.

In cases where real-time referencing is not required (for example, when analyzing data

after a session is over), the accuracy of determining the moment of a state change can be

improved when used in processing the results of the approximation.

5. DISCUSSION

Does the considered biosensor allow for fixing the presence of anaerobic energy metabolism

(anaerobic glycolysis)?

Yes, it does. In order to solve this problem, it is necessary to detect a change in the

relative concentration of lactic acid and glucose in the human body by more than 10%, while

the measurement error does not exceed 10%.

Why is the continuous flow system needed?

The use of a continuous flow system prevents the biosensor from becoming saturated,

which prolongs its use.

How long does it take to fix the presence of anaerobic metabolism automatically?

80 to 120 seconds in the laboratory. When placing a sensor on a person, this time may

increase depending on the size and nature of interference.

What difficulties are associated with the use of the considered biosensor?

The main difficulty is that it is a disposable sensor. Therefore, it is necessary to ensure the

possibility of its replacement by the user. As a result, the reliability of the sensor may depend

on the skills of the user, which is undesirable.



Is there a prospect for the development of the considered sensor? If yes, in which

directions?

There is a prospect for the development. The sensor design based on the enzyme

biosensor, which allows for determining the glucose and lactic acid levels in the human body

and intended to work as part of the body's sensory network, should be modified in a way to

reduce the effect of interference at the time of an automated decision on the presence of

anaerobic metabolism. Besides, it is desirable to get rid of the need to replace the biosensor by

the user. Currently, the following solutions to these problems are being considered:

Refinement of the design of both flow and cuvette sensors in order to reduce the

sensitivity to interference. The magnitude and nature of interference at the output of

the biosensor should be brought to such a state that will allow not interrupting the

processing for the duration of the interference.

Refinement of the cuvette sensor design, which allows for extending the time of its

work up to ten hours.

Improvement of the biosensor, which will allow for refusing its replacement after each

session. It must be possible to restore the biosensor's working capacity without replacing it by

performing flushing or other operations upon completion of the session.

5. CONCLUSIONS

This manuscript discusses the possibility of using noise filtering and improving the signal

quality of the wearable biosensor platform while measuring the content of glucose and lactic

acid in sweat on a moving object. It was found that it is possible to use enzyme biosensors for

determining the level of glucose and lactic acid in a wearable continuous flow collection

system. The signal processing system allows for increasing the signal-to-noise ratio at the

biosensors' output to the values suitable for automated processing. Interference occurring at

the biosensors' output when placed on a person and caused by their movements can lead to a

delay in deciding a person's condition.

ACKNOWLEDGMENT

This research is carried out with the financial support of the Ministry of Education and

Science of the Russian Federation under the agreement No. 14.577.21.0232 as of September

29, 2016 (unique number RFMEFI57716X0232). The applied research is conducted on the

topic of ―Research of scientific and technical solutions and development of an intelligent

biosensor platform for preventive monitoring and assessment of indicators of the human body

―Body sensory network‖ with the ability to correlate data obtained from various sensors in a

noisy environment.‖

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ACCEPTED DESIGNATIONS

GL – glucose

LA – lactic acid

OFS – open continuous flow system

Documents

USING NOISE FILTERING AND WEARABLE BIOSENSOR …€¦ · of biosensors/sensors used in wearable devices for monitoring the content of various metabolites of biological fluid. This