VSB – Technical University of Ostrava

Faculty of Electrical Engineering and Computer Science

Department of Cybernetics and Biomedical Engineering

Methods of Diagnosis and Stimulation of Heart

Habilitation Thesis Summary

2017 Ing. Martin Augustynek, Ph.D.

List of Abbreviations and Symbols

C Concentration of the substance [mmol/l]

C0-7(t) Constant

C0-7, Cp, Cs Concentration of the substance in different compartments [mmol/l]

Ca Concentration of input substance [mmol/l]

Cb Concentration of output substance [mmol/l]

Cd Concentration indicator [mmol/l]

Ci Specific heat injection [°C]

Ck Specific heat of blood [°C]

CaO2 Oxygen concentration in the blood artery [ml/l]

CO Cardiac output [5000 – 5600 ml/min]

COAP Cardiac output in the pulmonary artery [l/min]

CvO2AP Oxygen concentration the pulmonary artery [ml/l]

CvO2PS Oxygen concentration in the right atrium [ml/l]

Dp, Ds Designation for compartments with delay

F0, F1, F7, Fs Input and output volumetric flow [ml/s]

Fa Input volume flow [ml/s]

Fb Output volume flow [ml/s]

Fd Volumetric flow rate indicator [ml/s]

FVSD Short circuit volumetric flow [ml/s]

GAP Glucose concentration in the blood pulmonary artery [mmol/l]

GPS Glucose concentration in the right atrium [mmol/l]

Hb Hemoglobin

M0-7, Mb Designations of the individual compartments

paO2 Partial pressure of oxygen in arterial blood [mmHg]

pvO2PS Oxygen partial pressure in the right atrium [mmHg]

q0-7, q, qb, qp, qs, Amount of substance [l/min]

SATaO2 Blood oxygen saturation in the artery [%]

SATO2AP Blood oxygen saturation in the pulmonary artery [%]

SATO2PS Blood oxygen saturation in the right atrium [%]

V0-7, V, Vb, Vp, Vs Volume substances [ml]

Index

INTRODUCTION ................................................................................................................... - 1 -

1. THE ORIGINAL PROPOSAL OF THE METHOD FOR CARDIAC PACING... - 2 -

1.1 MANAGING PACEMAKERS USING ACTIMETRY ........................................................... - 4 -

1.2 FUZZY MODELS OF EXPERT MODULES ....................................................................... - 6 -

1.2.1 The CO2 computing module ................................................................................ - 6 -

1.2.2 The FMA model ................................................................................................... - 7 -

1.2.3 The MTF model .................................................................................................... - 7 -

1.3 SUMMARY AND OPPORTUNITIES FOR FURTHER DEVELOPMENT OF THE PROPOSED

SYSTEM ................................................................................................................................ - 10 -

2. DIAGNOSING CARDIAC HEMODYNAMIC ....................................................... - 11 -

2.1 THE PRINCIPLE OF THE NEW DIAGNOSTIC METHODS ............................................... - 13 -

2.1 EXPERIMENTAL VERIFICATION OF THE METHOD ..................................................... - 14 -

2.1.1 The advantages of glucose used as an indicator ................................................. - 14 -

2.2 VERIFICATION OF THE METHOD USING A MATHEMATICAL MODEL ......................... - 17 -

2.3 PRECLINICAL VERIFICATION OF THE PROPOSED METHOD........................................ - 20 -

2.3.1 Evaluation of the measured data ......................................................................... - 20 -

2.4 VERIFICATION OF THE PROPOSED METHOD USING CLINICAL DATA ......................... - 23 -

2.5 SUMMARY AND OPPORTUNITIES FOR FURTHER DEVELOPMENT OF THE PROPOSED

METHOD ............................................................................................................................... - 24 -

3. CONCLUSION ........................................................................................................... - 25 -

REFERENCES ...................................................................................................................... - 26 -

CURICILLUM VITAE ........................................................................................................ - 28 -

- 1 -

Introduction

In recent years, it has been possible to see a very rapid development in medical technology,

which has been possible due to developments in other disciplines such as electronics, sensorics,

materials engineering, data processing speed, etc. Thanks to this development, today's medicine

can capture biological signals with greater sensitivity, more accurately and more quickly; we thus

obtain more information and can determine a patient's diagnosis much faster, more accurately and

with less stress on the human body. The area of therapeutic devices is being developed together

with the development of diagnostics.

One of the fields that has recently seen great development is the area of diagnostics and

electrotherapy of the heart. This development is a response to the advances in other scientific

disciplines and is contingent on new possibilities of diagnosing heart diseases. Studies by

pacemaker manufacturers also clearly show that the number of implantations over the last ten

years has increased by several orders. Present-day pacemakers are implanted into patients with a

variety of arrhythmias and cardiac diseases associated with heart failure. These devices monitor

and regulate not only permanent or temporary automatism disorders of the atria and ventricles,

they also adjust their timing; they are also able to respond dynamically to the physiological needs

of the body and stimulate only when it is really required, thereby significantly reducing the load

on heart muscle.

The implantation of such a device is preceded by extensive diagnostics of cardiac

parameters, which include the hemodynamic parameters of the heart. There are many diagnostic

methods used to make this determination. Currently, the most widely used invasive method is the

dilution (thermodilution) method; non-invasive methods include ultrasound examination. These

methods have been verified and established in clinical practice for many years. Nevertheless, it is

generally known that even these methods have their own error rate which can be significantly

reduced or completely eliminated through the use of new materials and the development of new

sensors, contributing to a more accurate diagnosis of the disease.

The two areas offer the possibility of further development. In this work, the author aims to

define specific problems in both of these areas and contribute to their solutions, which he

demonstrates herein through his publication and scientific activities.

Both areas addressed in this work are widely used in clinical medicine. The draft new

algorithm has been further developed in cooperation with Mediatrade, which owns the patent

license. The method of measuring cardiac output has undergone preclinical validation. Actual

results indicate a much greater accuracy in comparison with the current methods; in the case of

clinical use, this will be of great benefit for a more timely and accurate diagnosis of many types

of diseased, not just heart disease.

- 2 -

1. The original proposal of the method for cardiac pacing

The current pacemaker technology is not perfect and there will always be a room for further

development which seeks to improve physiological cardiac pacing. One of the major weaknesses

of the current pacing art is the imperfection of the sensors that are used for sensing a range of

variables. One possible solution is to combine multiple sensors – preferably, a fast sensor (usually

physical activity) should be combined with a metabolic sensor (QT interval, minute ventilation).

The above overview of the measured parameters and the used individual sensors clearly

indicates that neither of the methods is ideal. This can be prevented by using a combination of

multiple sensors, removing insensitivity to certain kinds of changes in a biological subject. In this

way, it is possible to increase the speed of pacemaker response or its sensitivity. It is also possible

to monitor parameters that are not directly related to cardiac activity.

An example of the necessity to increase the speed of pacemaker response is the speed of the

response to physical stress. The below graph (Figure 1) shows the relationship between the pulse

rate changes and the patient’s actimetry. For this purpose, measurements were taken and

statistically processed. This graph presents two reactions of the body. The first of them, labelled

“Actual load”, represents the response of the body to current physical stress which is demonstrated

by the increase in the number of steps. The second reaction, labelled “Long-term load”, is the

response of the body to a long-term physical load, which is clearly manifested as an increase in

pulse rate [1-3].

Long-term load

Current load

0 5 10 15 20 25 30 35 400

20

40

60

80

100

120

140

Va

lue

of h

ear

t b

ea

t/ N

um

ber

of s

tep

s

Moving average of steps

Moving average of heart beat

Time [s]

Figure 1.: Dependence of the pulse rate on actimetry [1].

- 3 -

It is clear that an important role in the field of cardiac stimulation (and not only in it, but also

in medicine generally) is played by motion sensors whose use is versatile and convenient,

especially in home care. These sensors may be partially provided by smartphones (but also

external devices) that monitor the vital functions of the patient. A no less important role is played

by the appropriate processing of measured biological signals and their correct interpretation [4].

An example of such use is a wireless system to detect the position and movement of the patient

with the detection of falls, which allows for a software analysis of measured data and is intended

for use in home care and sports medicine [5; 6]. In medicine, an example application of these

sensors is a system for detecting epileptic seizures using accelerometers, described herein [7].

This system is based on the assumption that a seizure with convulsions can be detected by tremors

of the afflicted person and his/her bed. Sensors with accelerometers are installed on the bed and

measure its vibrations. Epileptic seizures by the measured person can be detected thanks to an

appropriate mathematical processing of the measured data [7].

Emphasis is placed not only on the sensors and the processing of measured data, but also on

the design of pacemakers, both in terms of battery life and the possibility to easily control them,

[8; 9], and in terms of their connection to the human body, i.e. namely electrodes [10]. All these

parameters are constantly tested under various clinical and experimental conditions [11-14] based

on the knowledge obtained, these vital devices are constantly being improved.

Another problematic area is the pacemaker settings for a given patient, which is based on

the experience of the physician, the patient's history and examination. It is desirable that the

setting does not take place by “trial-error” methods, but on the basis of objective measurements

and patient needs.

These findings indicate that the aim of today's pacing technology is to approach the

“physiological pacemaker” as much as possible, reduce its reaction time, i.e. the response to

physical, psychological or other body loads that affect pulse rate, and adjust this stimulator to the

individual needs of its users.

Given the above defined areas for possible further development, the greatest room for further

development is seen especially in the following segments:

Mathematical modelling of biological systems related to the function of the

cardiovascular system and ways to demonstrate wider links for influencing the final

pacing rate of the pacemaker.

Linguistic modelling (the use of artificial intelligence) in decision-making

processes, with emphasis on diagnostics and therapy in medicine.

Development and use of advanced sensors and sensor systems for more accurate and

stable sensing of physiological parameters that could serve as one of the input

parameters for the control algorithm of pacemakers.

The previous research activity by the author was carried out especially in these areas. The

following chapters describe the most important outcomes of this activity, which were further

applied when teaching the “Biocybernetics”, “Transducers and sensors” and “Medical electrical

equipment” courses.

- 4 -

1.1 Managing pacemakers using actimetry

The work deals with the methodology of controlling the pacing rate of pacemakers. It

describes the draft methodology for controlling the pacing rate of pacemakers based on the

measurement of physical activity, the influence of the autonomic nervous system and

pharmacological treatment which affects the pulse rate. As a knowledge base of an online expert

system, a fuzzy rule-based linguistic model was designed for deriving the magnitude of the pulse

rate change. The system function uses the principles of fuzzy set mathematics and fuzzy logic.

The proposed system is implemented in the Fuzzy Toolbox MATLAB environment, whose

simulation features were used to verify the solution’s effectiveness [15; 16].

As already mentioned, this work advantageously uses its own control algorithm for the

pacemaker, whose structure and function vary depending on the manufacturer, and suitably adds

an input parameter providing information about the expected changes in TF when the patient is

under physical stress. At the same time, this algorithm also takes into account other factors that

may affect the regulation of TF [17].

Current pacemaker control algorithms use a variety of measured parameters; these can be

obtained through a variety of sensors, but have different predictive values regarding the physical

activity of the patient [15].

ModelStimulation

Scanning Input data

DTF

External data

(farmacological)

Figure 1.: A block diagram of the proposed system for pacemaker control

(DTF - change in heart rate) [15].

The above figure (Figure 1) shows a block diagram illustrating the concept of managing the

pacemaker through physical activity. This diagram clearly presents the connection of the intended

model to the existing pacemaker control. The proposed model includes an algorithm that

processes input parameters used for determining the control variable for the existing pacemaker

algorithm, which is a change in the pulse rate [15].

In this model, the following aspects were chosen as being the most important factors

influencing the pulse rate:

Physical load

Mental load, stress, age of the patient (his/her mental condition)

Pharmaceuticals (drugs)

These factors were discussed with a physician and were found to be adequate and appropriate

for determining changes in the pulse rate as a control variable of the pacemaker. Three measurable

input variables, best representing the above factors, were therefore selected. They are:

The difference in oxygen concentration between arterial and venous blood. This

parameter represents the physical exertion component. It is a parameter that directly

correlates with physical load and reflects oxygen consumption in the muscles. It is

therefore a primary parameter which directly correlates with the physical load of the

body.

- 5 -

Inotropy. This is a parameter that is a component of mental load which is

represented by the effect of the autonomic nervous system, particularly the influence

of sympathetic and parasympathetic. Some of the current models of pacemakers

(e.g. the Cyclos 990 from Biotronik, etc.) already include the effect of ANS in their

control algorithm.

Pharmaceuticals. Patients with implanted pacemakers usually take medications

that directly or indirectly affect pulse rate regulation. These drugs may affect the

final value of TF; their influence must therefore be taken into account when

designing the model.

Model

motion senzor

QT interval

R-R interval

temperature, etc.

inotropy

CO2

stimulation

DF

T

INT

CO

2

drugs

Figure 2.: A block diagram of the proposed system for pacemaker control based on

physical activity with defined model inputs [15].

For the final diagnosis of the patient’s condition based on the above-selected parameters in

terms of the need to increase or reduce the pulse rate and also for predicting the magnitude of

changes in pulse rate, the method of a fuzzy oriented diagnostic expert system was chosen.

Figure 3 illustrates a pacemaker control system with two hierarchically interconnected

models. MTF (Model of Pulse Rate) is the main model whose task is to derive the resulting

magnitude of the pulse rate change based on inputs. FMA (Pharmacological Model) is the second

model whose task is to classify the effect of drugs taken by the patient and their influence on the

final pulse rate. The DO2 block is a computing block that uses the measured data to calculate the

resulting concentration of oxygen in the blood, which is an input into the MTF model.

- 6 -

MTF

FMA

CO2

1

Pacemaker

2

3

4

DO2

INT

FAF

KVA

TYP

DTF

CO2,v

CO2,a

TF

Fuzzy model Pacient

CO2,a – O2 concentration in the arterial blood

CO2,v – O2 concentration in the venous blood

INT – inotropy

TYP – type of the active substance

KVA– dosing drugs

TF – instant heart rate

DO2 – O2 concentration in the blood

DTF – change of heart rate

FAF – pharmacological correction factor

CO2 – calculation block of concentration of O2 in the blood

FMA– pharmacological fuzzy model

MTF – fuzzy model of heart rate

Figure 3.: A block diagram of the proposed system for pacemaker control with the

definition of proposed models and input variables.

1.2 Fuzzy models of expert modules

As mentioned above, the proposed system comprises two hierarchically interconnected

fuzzy models (see Figure 3). Input linguistic variables were defined to specify the bases of rules

for both proposed models. The next part of this work describes individual blocks of the diagram

shown in the figure above (see Figure 3), including linguistic variables of these models, their

linguistic values and ways of determining the values of linguistic variables, including their

eventual standardization.

1.2.1 The CO2 computing module

The CO2 computing module processes input measurement data using mathematical

operations. The result of this computing module is the only value that provides the MTF model

with information about the physical activity of the patient in the form of oxygen consumed by the

body.

Inputs of the CO2 module

As noted above, the CO2 module has two measured input values which are:

CO2a oxygen concentration in arterial blood

CO2v oxygen concentration in venous blood

- 7 -

The oxygen concentration in arterial blood CO2v is the value of oxygen in oxygenated blood

which is distributed throughout the body. Its value is close to 100% in healthy persons and

decreases in smokers and people with lung disease.

The CO2v value, i.e. the oxygen concentration in venous blood which returns from the body

back to oxygenation, is depleted of oxygen consumed by the body for its work. If the body is at

rest, without any physical load, the assumed oxygen consumption is about 15%. This value

corresponds to the consumption of basal metabolism.

The basal metabolic rate (BMR) is the amount of energy expended during a state of rest.

Energy expenditure during this state is only due to the work of vital organs, such as the heart,

lungs, brain and the rest of the nervous system, liver, kidneys, sex organs, and the basic tone of

postural muscles and the skin.

The CO2v value under a load may therefore be between 0% - 85%; for the needs of the model,

we assume that 85% and 0% corresponds to the minimum physical load (no physical load) and

maximum physical load, respectively.

The output from the CO2 module is a change in oxygen concentration, which represents the

difference between the concentration of oxygen in arterial blood (CO2a ) and the concentration of

oxygen in venous blood (CO2v ).

DO2= CO2a-CO2v

(1)

DO2 can range between 15% and 100%, i.e. the greater the physical load of the body, the

greater the difference in oxygen concentration between arterial and venous blood (CO2a-CO2v

).

1.2.2 The FMA model

The task of the FMA model is to predict the impact of input variables on changes in pulse

rate in the form of the “pharmacological factor” (FAF) output variable. As indicated herein [18],

there are several kinds of active substances which, by themselves or in combination with other

pharmaceuticals, have a primary or secondary effect on pulse rate. After consulting with experts

in this field, it was determined that the mere possibility of modelling the impact of individual

active substances in combination with other substances would be very beneficial for practice.

For this model, the drugs affecting pulse rate were divided into two basic groups: drugs

lowering pulse rate and drugs increasing pulse rate.

Regarding the FMA model, discussions with a physician specializing in this area resulted in

the selection of only two inputs that can affect changes in pulse rate. These inputs are:

They type of active substance (TYP)

The quantity of the active substance administered (KVA)

1.2.3 The MTF model

The pulse rate model (MTF) is the main model for the entire proposed system. Its task is to

predict changes in the pacing rate of a pacemaker based on the input data. The output of this

model is the change in pulse rate, i.e. information about the value by which the pulse rate should

be changed in the next step, i.e. the pacing rate of the pacemaker.

Discussions with a physician resulted in a selection of three model inputs which are

sufficient to determine the desired changes in pulse rate. These inputs are as follows:

changes in oxygen concentration (DO2), representing the physical load

- 8 -

inotropy (INT), representing the influence of ANS on the changes in TF

the pharmacological factor (FAF), representing the influence of drugs on TF

A test data set was compiled to verify the dynamics of the MTF model. Input values were

determined based on the expert knowledge of the given issue and are intended to demonstrate the

boundary states of the implemented system. Input values were entered into the created GUI

application, and the graphs containing the course of linguistic values of the output DTF variable

and the course of the converted pulse rate were then later exported.

Samples

Heart rate

Samples

Defuzzifications value of the output variable DTF of model MTF

Lan

guag

e va

lues

of t

he

outp

ut v

aria

ble

DTF

Figure 4.: Defuzzied values of the DTF output variable of the MTF model for setting the

input values of the TYP model = 0, KVA = 0-1, INT = 0, DO2 = 0.

The above figure (see Figure 4) shows TF dynamics which, in this case, are only influenced

by the effect of drugs increasing the pulse rate. In this case, one pill increasing the pulse rate was

administered. A similar but decreasing course would be seen for drugs reducing the pulse rate.

Figure 5 shows a block diagram of the measuring chain for implementing the HW module

that will contain the proposed fuzzy model for predicting changes in TF. This general block

diagram describes the basic implementation in the measuring chain with a pacemaker. This

scheme already includes a specific device type (EPG 10P). Serial communication is then used for

transferring data between the external module and pacemaker in both directions, i.e. from the

pacemaker and to the pacemaker.

- 9 -

Pacemaker

Calculation of the

input parameters of

fuzzy model

Determining

ΔTF

External inputs

(drugs)

Patient

Measureddata

Change ofheart rate

Measureddata

Pacingrate

External module Serial communications

Figure 5.: A block diagram of the draft control system.

Figure 6 shows the implementation of the model in the existing cardiac pacing system. The

figure indicates that the fuzzy model works in real time in an endless loop. During the process

shown in Figure 6, the application of the fuzzy model can lead to two states:

The fuzzy model recommends a reduction or increase of the pacing rate, and the

pacemaker will do it.

The fuzzy model recommends a change in the pacing rate, but the pacemaker will

not intervene in the actual heart rhythm because it is not needed.

Implantation PM

Initialization

Algorithm PM

Monitoring

Fuzzy model

Patient

Pacemaker

DTF = f (CO2,A, CO2,V, INT, TYP, KVA)

TF

DTF

TFIN

Figure 6.: A block diagram describing the process of cardiac pacing with the implemented

fuzzy model.

The task of the proposed model is to solve the problem of determining the dynamic changes

in pulse rate based on the current state of the patient. The objective of the proposed solution is to

- 10 -

include new criteria for determining the dynamic changes in pulse rate and reduce the time delays

between the physiological needs of the body and the stimulation of the pacemaker frequency.

In addition to the control of pacemakers based on actimetry (physical load) [19] the

implemented model also deals with control in terms of the influence of the autonomic nervous

system and the impact of drugs on changes in pulse rate. Based on existing management methods,

the new proposed method of control adequately reflects the physical and mental load on the

patient. Moreover, this method was expanded to include the influence of drugs that can affect

pulse rate as one of the external inputs. An algorithm for calculating the control variable of the

pacemaker based on measured data was developed at the same time. The output of this algorithm

is information for the pacemaker, recommending a reduction or increase of the pacing rate.

As a knowledge base of an online fuzzy oriented expert system, a fuzzy rule-based linguistic

model is designed to derive the magnitude of changes in pulse rate. This solution uses artificial

intelligence and enabled the integration of medical expert knowledge into the calculation

algorithm. The deduced algorithm determines the magnitude of the immediate correction of pulse

rate based on the input variables that respect the physical activity of the patient, the state of his /

her autonomic nervous system and the influence of any drugs used. The system function uses the

principles of fuzzy set mathematics and fuzzy logic. The linguistic model for determining pulse

rate corrections can be expanded by other rules. Implementation of the proposed system is carried

out in the MATLAB Fuzzy Toolbox environment whose stimulation means are used to verify the

solution’s effectiveness.

1.3 Summary and opportunities for further development of the

proposed system The proposed control system provides two areas of benefit. The first of them is the reduced

response time of the pacemaker to physical stress due to considering the oxygen concentration in

blood, which is the fastest physiological parameter reflecting the physical load. This results in

shortening the response time of the pacemaker, which brings us closer to the parameters of a

physiological pacemaker. The second area in which the proposed solution provides advantages,

is the possibility to more precisely set the pacemaker’s parameters according to the physiological

needs of the patient. This ensures a smaller load on the heart muscle and especially enhanced

patient safety. An important advantage of the proposed system is that the algorithm for

determining the change in pulse rate includes the effect of drugs that significantly affect this

change. The rules of the fuzzy model can be modified at any time.

The above-described issues, i.e. the pacing rate control through physical activity, have been

granted a national patent that is associated with a licensing agreement with Mediatrade.

Cooperation with this company is being developed; the proposed solution to pacing rate

management is integrated in one of its external pacemaker [20].

In the area of managing the pacing rate of pacemakers, another possible application of this

model is the use of non-invasive detection of a physical load and the creation of a detailed

pharmacological model which would simulate not only the influence of cardiotonics, but also

drugs that have secondary effects on pulse rate.

The next chapter of this work is devoted to a detailed diagnosis of cardiac hemodynamic,

which is one of the most commonly measured parameters when diagnosing the heart, and the

design of new diagnostic methods that would effectively eliminate the shortcomings of the

currently used methods.

- 11 -

2. Diagnosing cardiac hemodynamic

As mentioned in my habilitation work, the current dilution methods have their drawbacks

which are caused by the behaviour of the indicators used in the blood. Therefore, researchers have

tried to find a substance which would satisfy not only the above mentioned general requirements

for indicators usable in the bloodstream, but which would also disperse throughout the whole

blood volume, i.e. not only in the water phase (plasma), but also inside the red blood cells.

The reason for seeking such a new indicator is simple. Cardiac output, as well as blood flow

in another segment of the bloodstream, is a quantity that describes the flow of the whole (full)

blood; any individual calibration and averaged constant used in the calculation complicates the

entire measurement and makes it inaccurate. Therefore, efforts were made to obtain a substance

which would behave in the blood so that the resulting value of its concentration in blood after

mixing was given by a simple mathematical relationship valid for blood irrespective of its

haematocrit, and which would be metabolised in the patient's body in order to prevent its

recirculation. All the above features are met by glucose.

It should be emphasized that the method further introduced is not new with regard to the

type of measurement. It belongs in the category of dilution methods for measuring liquid flow

rate, where a substance (indicator) is added in a certain place of the measured flow (application

point), intermingles with the measured fluid and its resulting concentration is measured in a

certain place (measurement point) located downstream from the application point. Dilution

methods are based on the following mathematical argument: the higher the speed of the measured

flow, the lower the concentration of the indicator at the measurement point.

There are two basic ways of measuring the flow rate of fluids using the dilution method:

A single-shot administration of a defined amount of indicator at the application point

(bolus) and an evaluation of the course of changes in indicator concentration at the

measurement point. The result is a dilution curve with a typical course. We assess the

area of the dilution curve which has a mathematical relation to the fluid flow at the

measurement point.

Permanent or temporarily permanent administration of the indicator with a defined

concentration in the form of a defined flow at the application point. We assess its

concentration at the measurement point prior to its administration and at the time of

reaching a balanced state when the indicator concentration plateau is formed at the

measurement point.

An additional description will no longer be general, but related to biomedicine, i.e. the

use of a dilution measurement method on humans; it will especially focus on the

determination of minute cardiac output through measuring the blood flow in the

pulmonary artery.

The principle of the proposed method of measuring blood flow through a certain segment of

the bloodstream – where this segment for measuring minute cardiac output (CO) in the right heart

catheterization is as follows: the outlet of the top or bottom vein just prior to the right atrium

(application point) and the stem of the pulmonary artery (measurement point) – consists in a

uniform administration of glucose solution of a known concentration (Gi) and known flow rate

(q) at the application point, when taking at least two blood samples from the measurement point

- 12 -

as follows: the sample for determining the initial glucose concentration (Gk) and the sample to

determine the glucose concentration in the mixture of both flows (Gs), where the mixed blood

sampling is performed at the time of glucose concentration plateau which forms at the

measurement point within 15-20 seconds from the start of indicator administration at the

application point. After taking the blood sample Gs, indicator administration stops.

From the measurement point, it is possible to take more blood samples, e.g. every 4 seconds,

and the resulting values of blood sugar can be used to display the dilution curve. From the

measurement point, it is also possible to obtain continuous blood sugar values using a suitable

electrochemical sensor mounted on a special catheter.

A principle general block diagram of such a measuring chain is shown in the figure below

(Figure 7).

Left atrium/left vetricle

Right atrium/right vetricle

Indicator

L-R short cut

QQ

Linear pump

q

SW for analysing of measured data

Evaluation unit

Sensing device

Figure 7.: A block diagram of the measuring chain for determining cardiac output and

shunt, where q is the speed of indicator administration into the bloodstream and Q is

minute cardiac output.

The figure above (Figure 7) shows a block diagram of the measuring chain for determining

cardiac output. The following text explains each part of this diagram:

Block “indicator” represents the glucose solution with a predefined concentration.

The blocks labeled “left atrium/left ventricle” and “right atrium / right ventricle”

represent the application point of indicator and the point of blood sampling. Generally,

the application point can be any part of the bloodstream and the sampling point can also

be any part of the bloodstream, located downstream from the application point.

For making a laboratory determination of blood glucose concentration, the sampling tool

is a sampling syringe. In the case of online measurements, blood samples are not taken,

but the glucose concentration is measured continuously in real time using a sensor

introduced into the bloodstream on the tip of the catheter.

In the case of manual sampling, the evaluation unit is a biochemical analyser; in the case

of continuous measurement, it is a unit which evaluates the measured data, i.e. which

converts the measured quantity into the blood glucose concentration.

The SW used for analysing measured data is the algorithm for determining cardiac output

and detecting a cardiac shunt from the measured data.

- 13 -

2.1 The principle of the new diagnostic methods

Using glucose as an indicator for the dilution method for measuring blood flow rate, the

proposed method aims to determine blood flow rate in the bloodstream of living organisms in

order to simply determine the amount of blood flowing through a certain segment of the

bloodstream.

The above objective is achieved by determining the blood flow through the bloodstream of

the living organism when using glucose as an indicator continuously supplied to the bloodstream.

The principle is as follows: First of all, it is necessary to take a reference sample at a selected

point of the measured bloodstream segment. Subsequently, the indicator of known concentration

(𝐺𝑖) and known flow rate (𝑞) is administered into the bloodstream. After a certain time interval,

at least one blood sample is taken from the measurement point located downstream from the

application point and the resulting flow through the measured segment is determined according

to the equation below:

𝑄 =𝑞(𝐺𝑖 − 𝐺𝑠)

(𝐺𝑠 − 𝐺𝑘) (2)

where:

𝐺𝑠 [𝑚𝑚𝑜𝑙/𝑙] is the concentration of glucose in the mixture of blood and added

indicator

𝐺𝑘 [𝑚𝑚𝑜𝑙/𝑙] is the concentration of glucose in the blood before administration of

the indicator

𝐺𝑖 [𝑚𝑚𝑜𝑙/𝑙] is the concentration of glucose in the indicator solution

𝑄 [𝑙/𝑚𝑖𝑛] is the blood flow rate

𝑞 [𝑙/𝑚𝑖𝑛] is the flow rate of added glucose solution (indicator).

The advantage of this method of determining blood flow rate in the bloodstream is that no

special catheter or technical means are required. Injectomats for administering the indicator and

catheters are standard equipment in all clinical departments. The indicator is a common infusion

solution and glucose is a substance natural to the body.

The sugar load on the body during the indicator flow, e.g. 0.06 litres per minute (1 ml per

second for 20 to 30 seconds) is very low and can be tolerated, even by diabetics.

Measurements of the glucose concentration in taken blood samples (𝐺𝑘 – glucose

concentration in blood and 𝐺𝑠 – glucose concentration in the mixture blood+indicator) can be

performed STATIM in a biochemical laboratory. The indicator is stable during handling (unlike

the cooled fluid). Measurement costs in the arrangement described above are very low. In

addition, the proposed method offers the following advantages:

Compared to dye dilutions, the laborious calibration is eliminated.

The formula (2) for calculating 𝑄 does not contain any correction or calibration factor.

Heparinization of the patient is not necessary.

Technical problems with blood flow through the measurement chamber (photometry) are

eliminated.

- 14 -

The method removes errors associated with determining areas of dilution curves

(variation of the curve baseline, interpolation of the recirculation wave, i.e. removal of

deformations caused by the recirculation wave).

Compared with other methods, the dilution glucose measurement is more accurate

because it measures the flow of whole blood, which is a suspension of liquid and solid

phases (plasma and red blood cells). The high level of accuracy is supported by the large

signal-to-noise ratio because the indicator concentration at the measurement point (𝐺𝑠)

within the above arrangement of measuring 𝐶𝑂 is more than twice the initial value (𝐺𝑘).

Dilution glucose measurements have a high reproducibility.

Compared with other methods, the dilution glucose measurement process is simple and

complements the conventional measurement of pressures in small circulation.

2.1 Experimental verification of the method

he principle of the diagnostic method will be explained using an example of determining

minute cardiac output (𝐶𝑂 = 𝑄) in right heart catheterization.

To determine cardiac output, two blood samples were taken. These samples are as follows:

one sample for determining the initial (reference) glucose concentration (𝐺𝑘) and one sample for

determining glucose concentration in the mixture of both flows (𝐺𝑠), where the sampling of

mixed blood is performed at a time when the glucose concentration at the measurement point

remains constant (plateau of the dilution curve) and is not yet affected by the recirculation of the

added glucose solution. After taking the blood sample 𝐺𝑠, the indicator administration stops.

When measuring minute cardiac output (𝐶𝑂 = 𝑄) in right heart catheterization, the

measurement segment consists of the following parts:

the outlet of the upper or lower vein just before the right atrium (application point)

the stem of the pulmonary artery (measurement point).

At this measurement point, the glucose concentration plateau is formed in 15 to 20 seconds

from the start of administration of the indicator at the application point with a known flow rate 𝑞.

The recirculation wave of the used indicator does not affect the concentration plateau 𝐺𝑠 during

40 seconds of its administration. From the measurement point, it is posssible to take more blood

samples, e.g. every four seconds from starting the indicator administration, and the resulting

values of blood sugar (𝐺𝑠1 𝑡𝑜 𝐺𝑠𝑛) can be used to model the course of the dilution curve. Even

more advantageous (not a condition), is the possibility to continuously measure glucose

concentration at the measurement point, which allows us to register a detailed course of the

dilution curve and determine the value of 𝐺𝑠 very precisely.

2.1.1 The advantages of glucose used as an indicator

Glucose is a polar substance, i.e. the electrical charge of its molecules is unevenly distributed

in space and forms electric poles which can be mutually attracted or repelled. Besides glucose,

such substances also include alcohols and water. Polar substances are water-soluble (hydrophilic).

The molecular weight of glucose is 180.155 𝑔/𝑚𝑜𝑙, and its formula is 𝐶6𝐻12𝑂6.

Mixing an aqueous solution of glucose in the whole blood is due to the fact that its molecules

pass through the cell membrane of red blood cells. The passage of substances through cell

- 15 -

membranes is a very complex process which, in addition to physical forces, depends on the

transport proteins of the cell membranes, on the concentration, size and electric charge of

molecules, and other factors.

For the most part, this facilitated diffusion and, on a small scale, free diffusion and symport

with 𝑁𝑎+ions are the mechanisms that established the assumption that glucose molecules (upon

mixing the aqueous solution with glucose) spread relatively quickly and evenly throughout the

volume of blood, plasma suspension and the intracellular space of red blood cells. Two laboratory

measurements were conducted to verify this assumption. They compared the final glucose

concentration in the mixture of blood and glucose solution using the measurement (𝑐𝑠𝑚) and

calculation (𝑐𝑠𝑣) which was conducted according to the equation derived from the mixing of

solutions according to their quantity (𝑚) and concentration (𝑐) of the tested substance, i.e.

glucose:

𝑐𝑠𝑣 = (𝑚𝑘 ∙ 𝑐𝑘 + 𝑚𝑖 ∙ 𝑐𝑖)

(𝑚𝑘 + 𝑚𝑖) (3)

where

𝑐𝑠𝑣 − glucose concentration in the mixture of blood and added aqueous glucose solution

𝑐𝑘 − glucose concentration in a blood sample taken

𝑐𝑖 − glucose concentration in the aqueous solution, in mmol/l

𝑚𝑘 − the amount of whole blood in ml

𝑚𝑖 − the amount of the added aqueous glucose solution

The first measurement was taken using hand pipettes. 0.02 𝑚𝑙 (𝑚𝑖) of 10% glucose solution

(10% solution = 10 𝑔 is dissolved in 100 𝑚𝑙 of water = 555 𝑚𝑚𝑜𝑙/𝑙) were added to 2 𝑚𝑙 (𝑚𝑘)

of venous blood taken. The volumes of both blood and solution were measured by precision

pipettes; the blood glucose concentration and the mixture concentration were measured in a

biochemical lab.

The second measurement was carried out with the same parameters by an experienced

laboratory technician using automated pipettes.

The results of both measurements, i.e. a comparison of the measured glucose concentration

in the mixture of blood and aqueous solution of glucose (𝑐𝑠𝑚) with the calculation (𝑐𝑠𝑣), are

shown as graphs in the figure below. Figure 8 compares the concordance rate of glucose

concentration in the mixture of blood and indicator between the measured sample (𝑐𝑠𝑚) and the

calculation (𝑐𝑠𝑣) for the first measurement and the second measurement. From these graphs, it is

evident that the concordance rate of glucose concentration in the mixture determined by

measurement and calculation is very high.

- 16 -

Figure 8.: A comparison of two measurements performed by manual sampling (on the left)

and by a professional using automated pipettes (on the right), where csm is the mixture

concentration in the solution determined by taking a measurement and csv is the mixture

concentration in the solution determined by a calculation.

The equation for glucose dilution was determined as follows.

It is a modification of the general equation (3) which was used above to calculate the mixing

of two solutions with different concentrations (𝑘) of the same substance, having unequal

amounts (𝑚):

𝑐 ∙ (𝑚1 + 𝑚2) = (𝑘1 ∙ 𝑚1) + (𝑘2 ∙ 𝑚2) (4)

Where:

𝑐 − the final concentration of the mixture of two identical substances

𝑘1, 𝑘2 − the concentration of the first and second substance in the solution

𝑚1, 𝑚2 − the amounts of the two solutions in which the concentrations of substances are

𝑘1, 𝑘2.

The form of the same equation for mixing solutions but using symbols according to the

aforementioned method of measuring cardiac output (CO) using glucose dilution, in which the

static volume (𝑚) is replaced with the flow rate (𝑄, 𝑞), is as follows:

𝐺𝑠 ∙ (𝑄 + 𝑞) = (𝐺𝑘 ∙ 𝑄) + (𝐺𝑖 ∙ 𝑞) (5)

Where:

𝐺𝑠 − the glucose concentration in the mixture of blood and added indicator, in 𝑚𝑚𝑜𝑙/𝑙

𝐺𝑘 − the blood glucose concentration prior to administration of the indicator, in 𝑚𝑚𝑜𝑙/𝑙

𝐺𝑖 − the glucose concentration in the indicator solution, in 𝑚𝑚𝑜𝑙/𝑙

𝑄 − the blood flow rate in 𝑙/𝑚𝑖𝑛

𝑞 − the flow rate of the added glucose solution (indicator), in 𝑙/𝑚𝑖𝑛

y = 0.9801x - 0.1789R² = 0.9993

10

15

20

25

30

35

40

10 15 20 25 30 35 40

csv

[mm

ol/

l]

csm [mmol/l]

csm(x) vs csv(y) - profesional samples

y = 0.9637x + 0.9124R² = 0.9647

0

5

10

15

20

25

30

0 10 20 30

csv

[mm

ol/

l]

csm [mmol/l]

csm(x) vs csv(y) - manual samples

- 17 -

By modifying the equation (5), we obtain the final form for determining 𝐶𝑂 (𝑄), which was

previously given in the text as equation (2).

Thanks to this solution, it is possible to determine not only minute cardiac output, but also

the cardiac shunt, i.e. its exact extent. For this purpose, a SW model was developed; it allows for

the calculation based on the above measured data.

2.2 Verification of the method using a mathematical model

To verify the accuracy of the method, the original mathematical model was used as a control

mechanism. The measured data were applied to this model [21-24].

Creating mathematical models is based on the block diagram where the indicator is injected

into the pulmonary artery. This model can be adjusted for the administration of solution into the

right atrium. Models (dynamic and static) are created in the Matlab & Simulink computing

environments. The dynamic model is created in Simulink in order to simulate the course of

glucose concentration versus time when entering all the required parameters, including the value

of cardiac output and the percentage value of the left-right shunt. The static model is created in

Matlab.

Verification of the models is based on the assumption that there is a patient suspected of

having a left-right shunt. A 10% glucose solution is administered to the patient and subsequently

two patients’ blood samples are taken. A dynamic model is then created in Matlab & Simulink. It

simulates time courses of blood glucose concentration within individual compartments. This

model mainly serves to verify the actual measured data.

The real benefit, however, consists in an inverse task: It is assumed that we know steady

glucose concentration values at the beginning and end of the experiment, which are obtained by

measurement, and the task is to determine the percentage value of the left-right shunt. To calculate

this value and the steady states of all variables in the compartments, it is possible to use the static

model that is created using the Matlab Symbolic Toolbox.

This model calculates the steady states of all variables in compartments based on the Laplace

transform containing the original integrodifferential equations that describe the multi-

compartment model of the cardiovascular system [21; 23]. An important purpose of these models

is to obtain steady concentration values based on two values (blood glucose concentration before

and after the administration of the solution) which are used to determine the percentage value of

the left-right shunt, which is the main and most important output of the model. Calculations show

that the value of the left-right shunt can be determined based on two values of concentration (when

knowing other parameters listed in the table below (Table 1.: )).

Calculations and simulations in both mathematical models use parameters listed in the table

below (Table 1.: ) with typical values corresponding to an adult male. Table 1.: shows typical

values of volume and time for each compartment of the cardiovascular system. In different parts

of the cardiovascular system, the values are different [25-28].

- 18 -

Designation of

compartments

Anatomical representation Volume [ml] T [s]

0 (M) Right ventricle 125 1.25

1 (M) Pulmonary artery 250 2.5

P (D) Pulmonary capillaries and veins 500 5.0

2 (M) Left atrium 125 1.25

3 (M) Left ventricle 125 1.25

4 (M) Aorta, large arteries 750 7.5

5 (M) Small arteries 200 2.0

S (D) Capillary system and small veins 800 8.0

6 (M) Venous system 1000 10

7 (M) Right atrium 125 1.25

Table 1.: Parameters for calculations and simulations (M0 – M7 are compartments which

represent different segments of the cardiovascular system, compartment DP represents the

delay in the pulmonary system and compartment DS represents the delay in the overall

system)[21; 22].

The model is further explained with the aid of the diagram shown in the figure below (Figure

9). This figure clearly shows that the model consists of 10 compartments which in turn represent

different segments of the cardiovascular system, from the pulmonary artery to the right ventricle

which again connects to the pulmonary artery.

Figure 9.: The KVS compartment model with the application of the indicator in the

pulmonary artery [21; 22]. Model inputs are parameters 𝑭𝒅 (the volumetric flow rate of

the indicator [ml/s]) and 𝑪𝒅 (the indicator concentration [mmol/l]). Model outputs are the

output parameters of each compartment, i.e. the indicator concentration and flow rate in

different parts of the cardiovascular system.

- 19 -

In individual compartments, it is possible to calculate the blood glucose concentration. The

scheme depicts the combination of time delay and thorough mixing of the indicator with the blood

in different compartments. It shows the point where the indicator is administered into the

pulmonary artery, as well as the left-right shunt when the blood in the left ventricle divides – part

of the blood continues into the aorta and part of the blood flows into the right ventricle [29; 30].

A certain amount of time delay takes place in compartments 𝐷𝑝 and 𝐷𝑠. The first

compartment represents the delay in the pulmonary system, and the second compartment

represents the delay in the overall system. Other compartments depict dilution curves which show

the concentration-time charactersistic. This model is used to create a mathematical model in

Simulink for rendering individual dilution curves, where it is also possible to simulate the left-

right shunt. The values of glucose concentration are expressed based on integrodifferential

equations. [21]

To compare the left-right shunt effect, the model provides the below graphs which depict

the dilution curve in the pulmonary artery, in which the solution was applied to the right atrium

for 24 seconds. Figure 10 shows a normal course of the dilution curve that is not affected by the

left-right shunt.

Time [s]

Co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentration

Time [s]

Co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentration

Time [s]

Co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentration

Time [s]

Co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentrationBlood glucose concentration

Time [s]

Co

nce

ntr

atio

n [

mm

ol/

l]

Figure 10.: Dilution curve, compartment: pulmonary artery, shunt: 0%, CO: 100 ml/s,

application time 24 s [21].

Figure 11 shows the course of the dilution curve when the model left-right shunt is set at

20%. This course differs from the course when the shunt is set at 0%; this is caused by a timely

blood recirculation due to the shunt.

The course of dilution curves can be used to compare the effect of a left-right shunt on

cardiac output. The simulation included dilution curves without any shunts and with shunts when

the indicator was administered at the pulmonary artery. In the compared graphs, these courses

were different; this is due to the fact that the left-right shunt causes a timely blood recirculation

and the curve then does not exhibit any plateau.

- 20 -

Time [s]

Co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentration

Figure 11.: Dilution curve, compartment: pulmonary artery, shunt: 20%, CO: 100 ml/s,

application time 24 s [21].

A comparison of these graphs was based on input values from the literature [11] and the

application time of the indicator was set at 24 seconds so as to correspond with the real

measurements taken.

2.3 Preclinical verification of the proposed method

As previously mentioned, the principle of measuring the blood flow rate through a certain

segment of the bloodstream lies in the uniform administration of a glucose solution with a known

concentration (𝐺𝑖) and known flow rate (𝑞) at the application point, with taking at least two blood

samples from the measurement point. It is the sample used for determining the initial glucose

concentration (𝐺𝑘) and the sample used to determine the glucose concentration in the mixture of

both flows (𝐺𝑠), while the mixed blood sampling takes place at the time of glucose concentration

plateau. After taking the blood sample (𝐺𝑠), administration of the indicator is stopped.

When measuring minute cardiac output (𝑄) in the right heart catheterization, the

measurement segment consists of the following parts: outlet of the upper or lower vein just before

the right atrium (application point) and the stem of the pulmonary artery (measurement point). At

the measurement point, the glucose concentration plateau is formed in 15 to 20 seconds after

starting indicator administration at the application point [27].

From the measurement point, it is posssible to take more blood samples, e.g. every four

seconds, and the resulting values of blood sugar (𝐺𝑠1 až 𝐺𝑠𝑛) can be used to model the course of

the dilution curve.

Minute cardiac output (𝑄), analogously the blood flow through other measured segment of

the bloodstream, remains the only unknown quantity and is calculated according to an equation

(2).

2.3.1 Evaluation of the measured data

The measured data were recorded over two days, i.e. three animals were measured each day.

The interval between individual measurements on each animal was 20 minutes. After taking

measurements on the specific animal, the blood samples were delivered to a biochemical

laboratory for evaluation. The below table (Table 2) shows an example of one measurement.

Within the trial, 30 records of this type were obtained.

- 21 -

CO

[l/min] Gk Gsm1 Gsm2 Gsm3 Gsm4 Gsm5 Gsm6 Gsm7 Gsm8 Gsm9

Time [s] 0 21 26 31 37 43 49 55 61 67

Gluk [mmol/l] 5.56 3.76 3.92 4.66 6.93 7.48 7.67 7.43 7.63 8.09 8.54

Table 2.: Measurement record No. 1 on Subject No. 6.

The measured data listed in the table were used to plot the dilution curve which is shown in

the figure below (Figure 12). The graph illustrates the emergence of the plateau phase which was

assumed at 20 seconds and beyond. The relationship (2) was then used to calculate the value of

𝐶𝑂 measured by the glucodilution method; in this case, the value is 5.56 𝑙/𝑚𝑖𝑛.

Figure 12.: Dilution curve from measurement No. 1 on Subject No. 6.

The following table (Table 3.: ) summarizes the values of cardiac output, detected by

methods of glucodilution, thermodilution, lithium thermodilution and ultrasound. Unfortunately,

lithium thermodilution was not measured during the first part of the clinical trial due to a technical

defect.

CO [l/min] CO [l/min] CO [l/min] CO [l/min]

Glykodilution Termodilution LidCo UZV

Subject 1 4.2 6.4 0 7.3

Subject 2 8.1 6.4 0 7.4

Subject 3 3.7 5.2 0 6.1

Subject 4 4.6 9.3 8.5 9.1

Subject 5 5.3 5.1 3.9 5.8

Subject 6 5.6 4.3 5.1 5.7

Table 3.: A comparison of the cardiac output measured by various methods.

The following graph (Figure 13) presents a comparison of the most commonly used methods

for measuring cardiac output. The graph always compares the first measurement on each subject.

3.76 3.92

4.66

6.937.48 7.67 7.43 7.63

8.098.54

0

1

2

3

4

5

6

7

8

9

0 21 26 31 37 43 49 55 61 67

G[m

mo

l/l]

Time [s]

The course of the blood glucose concentration

- 22 -

Figure 13.: A comparison of the cardiac output measured by various methods. The

comparison is always made for the first measurement on the respective subject.

The graph (Figure 13) clearly shows that the difference in the values of cardiac output

measured by various methods can be very significant. The accuracy of dilution methods is

indicated as +/- 1 𝑙/𝑚𝑖𝑛. The graph also shows that the results were close to the biggest

concordance in measurements on subject No. 5 and No. 6. This is mainly due to the fact that the

first measurements were conducted with time intervals between samplings of up to 15 seconds.

In these cases, in such a large time span, we missed the recirculation wave and the graph then did

not contain the plateau phase; therefore, the calculation of the CO value was thus distorted. An

example of such a measurement is shown in the figure below (Figure 14).

Figure 14.: The dilution curve from measurement No. 3 on subject No. 3.

Given the fact that some of the measurements did not have the expected information value

due to inaccuracies and imperfections in sampling, we plan to carry out another clinical trial where

the blood glucose concentration will be measured continuously using a sensor which has been

developed for this purpose and is the subject of patent proceedings.

4.2

8.1

3.7

4.6 5

.3 5.6

6.4

6.4

5.2

9.3

5.1

4.3

0 0 0

8.5

3.9

5.1

7.3 7.4

6.1

9.1

5.8

5.7

0

2

4

6

8

10

Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6

Q [

l/m

in]

COMPARSION OF CARDIAC OUTPUT MEASURED BY DIFFERENT METHODS

Glykodilution Termodilution LidCo USG

0

2

4

6

8

10

12

14

0 35 45 53 62 72 80 88 97 104

G[m

mo

l/l]

time [s]

The course of the blood glucose concentration

- 23 -

2.4 Verification of the proposed method using clinical data

The data used for verifying this method were obtained from measurements taken in clinical

practice, conducted over the past several years. The catheters used and the indicator were and are

standard equipment of current intensive medicine. Nevertheless, it is necessary to proceed in

compliance with Act No. 373/2011 Coll. and accordingly apply for registration of the new method

– determination of blood flow through measured bloodstream segments using glucodilution. The

application must be supported by clinical trials according to currently valid legislation.

We used the clinical data to verify the methodology and were applied to the mathematical

models created. The data relate to patients with a left-right shunt. The percentage shunt value was

calculated manually according to the following equation:

𝐿 − 𝑃(%) =100(𝐶𝑣𝑂2𝐴𝑃 − 𝐶𝑣𝑂2𝑃𝑆)

(𝐶𝑎𝑂2 − 𝐶𝑣𝑂2𝑃𝑆)=

100(119.00 − 87.87)

(139.28 − 87.87)= 60.6 % (6)

The cardiac output was calculated according to the following equation:

𝑄(𝐶𝑂𝐴𝑃) =𝑞 ∙ (𝐺𝑖 − 𝐺𝑠)

(𝐺𝑠 − 𝐺𝑘)=

0.06 ∙ (555 − 14.5)

(14,5 − 6.4)= 4 𝑙/𝑚𝑖𝑛 (7)

To verify the model, it is important to specify the value of cardiac output in 𝑚𝑙/𝑠𝑒𝑐; the

value must therefore be converted according to the following equation:

𝑄 =(𝐺𝑖 − 𝐺𝑠)

(𝐺𝑠 − 𝐺𝑘)=

(555 − 14.5)

(14.5 − 6.4)= 66.7 𝑚𝑙/𝑠 (8)

The above equations ((6), (7) and (8)) are given only as an example of a calculation for

patient A. These calculated values are then used to validate the model.

The below graph (Figure 15) shows a typical shape of the dilution curve measured in the

pulmonary artery, with the plateau phase occurring after 14 – 20 seconds. In this case, the left-

right shunt is 0%.

time [s]

glu

cose

co

nce

ntr

ati

on

[m

mo

l/l]

Blood glucose concentration

plato

The peak of the dilution wave

Figure 15.: A dilution curve in the pulmonary artery, shunt: 0%, CO: 66.7 ml/sec,

application time: 2 sec [21].

- 24 -

The next graph (Figure 16) shows a dilution curve which simulates a left-right shunt with a

total value of 60.6%. It is evident that the plateau phase is completely missing.

Blood glucose concentration

time [s]

gluc

ose

co

nce

ntr

atio

n [m

mo

l/l]

The plato is missing due to an LP short cut

Figure 16.: A dilution curve in the pulmonary artery, shunt: 60.6%, CO: 66.7 ml/sec,

application time: 2 sec [21].

The application of measurement data to this mathematical model enables the verification of

the accuracy of the proposed method of measuring cardiac output and determining a left-right

shunt. Moreover, thanks to this model and measurement data, it is possible to determine its precise

extent.

2.5 Summary and opportunities for further development of the

proposed method

The above chapter summarizes the basic dilution methods used to measure cardiac output

and their basic comparison. This chapter is mostly dedicated to the design of a new diagnostic

method, a dilution method, which uses a previously unused indicator, glucose. The chapter

describes its advantages over previously known methods and also provides validation of the

proposed methodology through a mathematical model and its preclinical verification on animals.

The presented results clearly show that the proposed method provides more accurate results and

that its further development mainly lies in improving the measurement chain for continuous

glucose sensing and evaluating measured data.

- 25 -

3. Conclusion

The presented work provides a comprehensive look at new possibilities for managing cardiac

pacemakers and measuring hemodynamic parameters of the heart. The area of cardiology is a

very narrow part of modern medicine; nevertheless, it is very often mentioned in connection with

new scientific findings and opportunities for further development. This is mainly due to the

diversity of this field which enables the application of new materials, sensors, electronics,

biological signal processing, etc.

These findings are the main basis of the author's previous activities which resulted in this

work where the author describes the design of new algorithm for controlling pacemakers and the

design and verification of a new methodology for measuring hemodynamic parameters of the

heart. The objective of the proposed solution is to include new criteria for determining the

dynamic changes in pulse rate, and for reducing the time delay between the physiological needs

of the body and the actual stimulation rate of the pacemaker. Regarding the draft methodology

for measuring hemodynamic parameters, the aim is to improve the accuracy of current diagnostic

methods and actual heart diagnostics; using this method, it is possible confidently detect a cardiac

shunt and its extent.

The solution is innovative and beneficial to society; this is evidenced by publications by the

author in the field and means of intellectual property protection to protect the results achieved.

These include a US patent on the new diagnostic method and a Czech patent on the algorithm for

pacemaker management, associated with a license agreement. Collaboration within the

Development in the area of electro-stimulation of the heart is further being developed in

collaboration with Mediatrade.

All of the above findings and scientific outputs were used by the author in his teaching as

educational materials, laboratory tasks and exercises. However, they are mainly used in a newly

built laboratory which provides professional background to biomedical engineering students in

the following fields: sensors, biocybernetics, medical instrumentation, infusion and

haemodialysis technology, and is especially a leading site in the electrotherapy of the heart.

As already mentioned, medicine, and specifically cardiology, is associated with technical

sciences and has a huge potential for further development. An example of such development in

the pacemaker art is the potential completion of algorithms for controlling pacemakers with other

sensors which would thus draw these devices near to physiological pacemakers. Furthermore, the

development includes the possibility of using new sensors for detecting physical activity. For

example, there is great potential with regard to inertial sensors, which are increasingly found in

normal commercial electronics and are used in many applications. One of the other areas of

potential further research is the development of continuous glycaemic sensors, which would find

use not only when taking measurements of hemodynamic parameters of the heart, but also in

many other medical fields.

- 26 -

References

[1] Augustynek, M. Signal processing of ECG. VŠB – Technical University of Ostrava, 2008.

[2] Augustynek, M., D. Friedmannova, M. Cmielova. Measuring of Dependency between

Heart Rate, Respiratory Rate and the Human Movement. In Programmable Devices and

Embedded Systems. 2013, vol. 12, p. 292-297.

[3] Cobelli, C., E. Carson Introduction to Modeling in Physiology and Medicine Edtion ed.:

Elsevier Science, 2008. ISBN 9780080559988.

[4] Augustynek, M., L. Cajka, V. Kasik, Z. Slanina, S. V Measurement, signal processing and

visualisation of actimetry, electrocardiography and body temperature. Ifac Workshop on

Programmable Devices and Embedded Systems (Pdes 2009), Proceedings, 2009 2009, 282-

285.

[5] Augustynek, M., O. Adamec, M. Cerny. Pedometer with detection of Step. In World

Congress on Medical Physics and Biomedical Engineering May 26-31, 2012, Beijing,

China. Springer Berlin Heidelberg, 2013, p. 1408-1411.

[6] Cerny, M., O. Dossel, W. Schlegel Movement Monitoring in the HomeCare System. World

Congress on Medical Physics and Biomedical Engineering, Vol 25, Pt 5, 2009 2009, 25,

356-357.

[7] Vasickova, Z., M. Augustynek New method for detection of epileptic seizure. Journal of

Vibroengineering, Jun 2009, 11(2), 279-282.

[8] Gála, M., I. Vajdikova, B. Babusiak, M. Penhaker, M. Cerny, M. Augustynek. Pacemaker

Battery State Checking by Stimulation Pulse Width Detection. In The 15th International

Conference on Biomedical Engineering. Springer International Publishing, 2014, p. 659-

662.

[9] Gala, M., I. Vajdikova, B. Babusiak, M. Penhaker, M. Cerny, M. Augustynek. Battery

check test on pacemaker by advanced technique. In Applied Machine Intelligence and

Informatics (SAMI), 2014 IEEE 12th International Symposium on. IEEE, 2014, p. 45-48.

[10] Penhaker, M., M. Darebnikova, F. Jurek, M. Augustynek. Evaluation of

Electrocardiographic Leads and Establishing Significance Intra-individuality. In

Innovations in Bio-inspired Computing and Applications. Springer International

Publishing, 2014, p. 295-303.

[11] Augustynek, M., M. Penhaker, P. Sazel, D. Korpas. Stimulation Parameter Testing and

Verification during Pacing. In XII Mediterranean Conference on Medical and Biological

Engineering and Computing 2010. Springer Berlin Heidelberg, 2010, p. 533-536.

[12] Augustynek, M., Z. Labza, M. Penhaker, D. Korpas. Verification of Set Up Dual-Chamber

Pacemaker Electrical Parameters. In Computer Engineering and Applications (ICCEA),

2010 Second International Conference on. IEEE, 2010, vol. 2, p. 168-172.

[13] Augustynek, M., M. Penhaker, D. Korpas. Notice of Retraction Controlling Peacemakers

by Accelerometers. In Computer Engineering and Applications (ICCEA), 2010 Second

International Conference on. IEEE, 2010, vol. 2, p. 161-163.

[14] Penhaker, M., T. Stula, M. Augustynek. Long-Term Heart Rate Variability Assessment. In

5th Kuala Lumpur International Conference on Biomedical Engineering (BIOMED 2011).

Kuala Lumpur, MALAYSIA, 2011, vol. 35, p. 532-535.

[15] Augustynek, M. Pacemaker Control Using Accelerometer Data. VSB Technical University

of Ostrava, 2014.

[16] Cooper, D. COMPARISON OF ACTIVITY SENSORS AND ALGORITHMS FOR

RATE-RESPONSIVE PACEMAKERS USING AMBULATORY MONITORING. In A.

MURRAY ed. Computers in Cardiology 1993, Proceedings. Los Alamitos: I E E E,

Computer Soc Press, 1993, p. 851-854.

[17] Lau, C. A., Y. T. Tai, P. C. Fong, J. P. S. Li, S. K. Leung, F. L. W. Chung, et al. CLINICAL-

EXPERIENCE WITH AN ACTIVITY SENSING DDDR PACEMAKER USING AN

ACCELEROMETER SENSOR. Pace-Pacing and Clinical Electrophysiology, Mar 1992,

15(3), 334-343.

[18] Augustynek, M. Řízení kardiostimulátorů pomocí aktimetrie 2014.

- 27 -

[19] Augustynek, M., M. Penhaker, D. Korpas, I. C. Society. Controlling Peacemakers by

Accelerometers. In 2010 Second International Conference on Computer Engineering and

Applications: Iccea 2010, Proceedings, Vol 2. 2010, p. 161-163.

[20] Způsob monitorování vibrací pacienta pro řízení kardiostimulátoru. Augustynek, M., M.

Penhaker, D. Korpas.

[21] Sindelkova, K. Modelování a analýza výpočtu srdečního minutového výdeje pomocí diluce

glukózy. Bakalářská práce VŠB – Technická univerzita Ostrava, 2013.

[22] Rideout, V. C. Mathematical and computer modeling of physiological systems. Edtion ed.:

Prentice Hall Englewood Cliffs, NJ:, 1991. ISBN 0135633540.

[23] Kveder, M., Z. Bajzer, J. Nosil A Mathematical-Model For The Quantitative Study Of Left

To Right Cardiac Shunt. Physics in Medicine and Biology, 1985 1985, 30(3), 207-215.

[24] Danilov, V. V., R. G. Litvinov, O. M. Gerget. Mathematical modeling the electrical activity

of the heart. In O. BERESTNEVA, A. TIKHOMIROV AND A. TRUFANOV eds.

Proceedings of the 2016 Conference on Information Technologies in Science,

Management, Social Sphere and Medicine. Paris: Atlantis Press, 2016, vol. 51, p. 187-191.

[25] Lozek, M., B. Nedvedova, J. Havlik, Ieee. Mechanical Model of Cardiovascular System

Determination of Cardiac Output by Thermodilution Method. In 2013 International

Conference on Applied Electronics. New York: Ieee, 2013, p. 177-179.

[26] Alayoud, A., K. Hassani, M. Benyahia A model to calculate cardiac output in hemodialysis

patients by thermodilution. Theoretical Biology and Medical Modelling, Jun 2012, 9, 2.

[27] Ahmed, A., A. Ahmed, H. Kaoutar, B. Mohammed, O. Zouhir A MODEL TO

CALCULATE CARDIAC OUTPUT IN HAEMODIALYSIS PATIENTS BY

THERMODILUTION. Nephrology Dialysis Transplantation, May 2012, 27, 201-201.

[28] Sweeney, T. E., J. W. Miller A mechanical model of the cardiovascular system - a

pedagogical tool. Faseb Journal, Apr 2009, 23, 1.

[29] Batzel, J. J. Cardiovascular and respiratory systems, modeling, analysis, and control. In.

Philadelphia: Society for Industrial and Applied Mathematics, 2007, p. 265.

[30] Melchior, F. M., R. S. Srinivasan, J. B. Charles MATHEMATICAL-MODELING OF

HUMAN CARDIOVASCULAR-SYSTEM FOR SIMULATION OF ORTHOSTATIC

RESPONSE. American Journal of Physiology, Jun 1992, 262(6), H1920-H1933.

- 28 -

Curicillum Vitae

Ing, Martin Augustynek, Ph.D.

Date of Birth: 28.8.1984

Place of Birth: Ostrava

Home Adress: Požárnická 137/51, 748 01, Hlučín Bobrovníky, Czech Republic

Office Adress: VSB - Technical University of Ostrava

Faculty of Electrical Engineering and Computer Science

Department of Cybernetics and Biomedical Engineering

17. listopadu 15, 708 33, Ostrava – Poruba

Phone: +420 59 732 5852

E-mail: martin.augustynek@vsb.cz

EDUCATION

2008 – 2014 Ph.D., VSB - Technical University of Ostrava, Czech Republic,

Faculty of Electrical Engineering and Computer Science, Department of

Cybernetics and Biomedical Engineering. Specialization: Technical

cybernetics. Doctoral thesis: „Pacemaker Control Using Accelerometer

Data“

2006 – 2008 M.Sc., VSB - Technical University of Ostrava, Faculty of Electrical

Engineering and Computer Science, Department of Measurement and

Control. Specialization: Measurement and Control Engineering. Thesis:

„Processing of the ECG signal“

2006 – 2008 IngPaedIgip., University of Ostrava, Pedagogical faculty, Department

of Teacher Training in Specialist Subjects. Final thesis: Usage interactive

boards at schools

2003 – 2006 Bc., VSB - Technical University of Ostrava, Faculty of Electrical

Engineering and Computer Science, Department of Measurement and

Control. Specialization: Control and Information systems. Bachelor

thesis: Measurement and visualization blood pressure.

1999 – 2003 Secondary Technical School at Ostrava, Specialization:

Elektrotechnics

WORK EXPIERENCE

2011 – now VSB - Technical University of Ostrava, Assistant Professor

Lecturer/Researcher full time

2012 – now University of Ostrava, Faculty of Medicine, Lecturer 30 %

2012 – 2014 Czech Technical University (CVUT), Faculty of Biomedical

Engineering (FBMI), Lecturer 30 %

2007 – 2012 School of Nursing and College of Nursing, Teaching vocational

subjects - Instrument Medical Technology I and II., Teaching physics for

medical fields.

- 29 -

RESEARCH ACTIVITY SUMMARY

Web of Science publications (http://www.researcherid.com/rid/F-1323-2016 )

Impacted journal 4

Proceedings paper 18

Citations 75

h-index 6

Scopus publications (http://orcid.org/0000-0002-0165-7317 )

Publications 39

Citations 113

h-index 6

Google Scholar (https://scholar.google.cz/citations?user=myN-jOYAAAAJ )

Publications 52

Citations 207

h-index 9

Education books

University 5

High School 3

Intellectual Property

National Patent (accepted) 1

US Patent (handed up) 1

Utility models 5

Functional Samples 7

Software 8

Trademarks 2

PEDAGOGIC ACTIVITY

Techniques of electronic devices 2010 - 2016

Medical electrical equipment 2 2010 - 2012

Transducers and sensors in biomedicine 2009 - 2011

The basics of biocybernetics 2010 - 2016

Biocybernetics 2010 - 2016

Biophysics 2010 - 2016

Medical propaedeutics 1 2013 - 2016

Signals and systems 2008 - 2010

Electrical circuits I 2012 - 2013

Electrical circuits II. 2012 - 2013

Continual guided practice 2011 - 2016

Practical training 2011 - 2016

Infusion and haemodialysis technology 2013 - 2016

Special medical technology 2011 - 2016

Public health service and and medical technology management 2013 - 2016

Diagnostic methods in medicine 2014 - 2016

Data transfers in medicine (OSU) 2011 - 2013

- 30 -

Technique in prehospital emergency care and medicine (OSU) 2011 – 2013

Radiocommunication systems of rescue services (OSU) 2011 - 2016

PROFESIONAL APPRECICIATION

Gold Medal, Invento, Prague 2013 Award, Martin Augustynek, Marek

Penhaker, David Korpas: The Method of pacient’s vibrations for pacemaker

controlling, Praha, 6.-8.6.2013

Dean’s price for Ph.D students of the faculty of Electrical Engineering and Computer

Science., 2010

The scholarship of the City of Ostrava for a prescribed grade point average,

participation in scientific activities, presentation of the city and community activities in

the 2009/2010 and 2010/2011 academic years.

Dean’s price for MSc. students of the faculty of Electrical Engineering and Computer

Science, 2008

RESEARCH PROJECTS

Augustynek, M., Maresova, P., Honegr, J.: Economic, Aspects of Medical Devices

Development, GAČR, reg. n. 17-03037S, 2017-2019, co-applicant

Support for science and research in the Moravian-Silesian Region, 2014 DT3 – PostDoc

Augustynek, M.: Support for science and research in the Moravian-Silesian Region, 2013

DT2 – Support of science through investments, Design of a catheterization system,

MK9333422, Applicant

Srovnal, V.:TACR (Technology Agency of Czech Republic), Project TA01010632,

SCADA System for Control and Monitoring Processes in Real Time, Faculty of Electrical

Engineering and Computer Science, VSB - TU Ostrava, co-applicant

Augustynek, M., Penhaker, M., Peterek, T.: Measurement chain for wireless

measurement and processing of actimetry and body temperature, FRVŠ - G1 1787/2009,

applicant

Srovnal, V.:GAČR 102/08/1429 Safety and security of networked embedded systems,

co-applicant

Černohorský, J.: CAK 1M0567 CAK Centrum of applied cybernetics, co-applicant

PROJECT WITH EXTERNAL COMPANIES

Augustynek, M., et al., MEDIATRADE s.r.o., HS4501610

Augustynek, M., et al.: Conference YBERC 2016. HS4501608

Penhaker, M., et al.: Perform a Life Scope Patient Monitor Disorder Diagnostic, Faculty

Hospital of Ostrava, HS 450111, co-applicant

Penhaker, M. et al.: Measurement and testing of invasive pressure monitors and

manometers, Faculty Hospital of Ostrava, č. HS455903, co-applicant

Augustynek, M. et al.: Perform the defibrillator parameter check. Faculty Hospital of

Ostrava, HS455903.

- 31 -

EUROPEAN SOUCIAL FUNDS PROJECTS

Černý, M., Hána, K., Augustynek, M. et al.: Informatics in telemedicine at VSB – TU

Ostrava and CTU FBMI Praha, CZ 1.07/2.2.00/28.0322, co-author, project manager,

2011-2014 (21 506 830 CZK)

Černohorský, J., Augustynek, M. et al.: Improvement of Biomedical Technics absolvent

competitiveness at VSB – TU Ostrava, CZ 1.07/2.2.00/15.0112, co-author, project

manager, 2010-2013 (18 747 340,27 CZK)

Penhaker, M., Augustynek, M. et al.: Biomedical Technics at secondary schools,

CZ 1.07/1.1.07.0075, co-author, project manager, 2009-2012 (8 351 078,16 CZK)

CONFERENCE TCP MEMBER AND ORGANIZER

MissTral 2012 Special Session Organizer, PC Member, Vietnam 2012

Young Biomedical Engineers Conference (YBERC), PC Member, Co-organizer, 2008,

2010, 2012, 2014, 2016

Trends in Biomedical Engineering, PC Member (2009, 2011, 2013), organizer 2011

Quality of health care, PC Member, organizer (2013, 2015)

MEMBERSHIP IN PROFESIONAL ORGANIZATIONS

Czech Society for Medical Devices (from 2012), member of the board

Czech Society for Biomedical Engineering and Medical Informatics Member of Czech

Medical Association Jan Evangelista Purkyně

IEEE member

ERASMUS TEACHER ACTIVITIES

Erasmus Teacher exchange program: Technical University of Kosice, Slovakia, 2016

Erasmus Teacher exchange program: Polytech Grenoble, Grenoble, France, 2016

Erasmus Teacher exchange program: Žilina University, Žilina, Slovakia, 2015

Erasmus Teacher exchange program: Žilina University, Žilina, Slovakia, 2015

Erasmus Teacher exchange program: Universite Lyon 1, Polytech Lyon, France, 2014

Erasmus Teacher exchange program: Technical University of Kosice, Slovakia. 2012

Erasmus Teacher exchange program: Žilina University, Žilina, Slovakia, 2012

LIST OF SELECTED PUBLICATIONS

1. AUGUSTYNEK, M., KORPAS, D., PENHAKER, M., CVEK, J., BINAROVA., A.

Monitoring of CRT-D devices during radiation therapy in vitro. In. BioMedical

Engineering OnLine [online]. 2016, 15(1), - [cit. 2016-10-18]. DOI: 10.1186/s12938-

016-0144-7. ISSN 1475-925x. (Impact Factor (2015 Thomson JCR Science Edition):

1.382, ENGINEERING, BIMEDICAL Q3)

2. AUGUSTYNEK, M., PENHAKER, M., VYBIRAL, D. Devices for position detection.

In Journal of Vibroengineering,2011, p. 531 – 535., ISSN: 1392-8716, IDS Number

828IY, (Impact Factor (2010 Thomson JCR Science Edition: 0.323, ENGINEERING,

BIOMEDICAL 71 of 76, Q4))

- 32 -

3. AUGUSTYNEK, M., PENHAKER, M. Non Invasive Measurement and Visualization of

Blood Presure. In Journal Electronics and Electrical Engeneering, 2011, ISSN 1392-

1215, (Impact Factor (2010 Thomson JCR Science Edition): 0.659, ENGINEERING,

ELECTRICAL & ELECTRONIC 191 of 249,Q4))

4. VASICKOVA, Z., AUGUSTYNEK, M. New method for detection of epileptic seizure.

In Journal of Vibroengineering, 2009, p. 209., ISSN: 1392-8716, IDS Number: 468VT,

(Impact Factor (2009 Thomson JCR Science Edition: 0.357, ENGINEERING,

BIOMEDICAL 71 of 76, Q4))

5. AUGUSTYNEK, M., J. KUBICEK, M. CERNY AND M. BACHRATA. Model Kidney

Function in Stabilizing of Blood Pressure. In N.T. NGUYEN, B. TRAWINSKI, H. FUJITA

AND T.P. HONG eds. Intelligent Information and Database Systems, ACIIDS 2016, Pt I.

Berlin: Springer-Verlag Berlin, 2016, vol. 9621, p. 430-439. ISBN 978-3-662-49381-6;

978-3-662-49380-9. ISSN: 978-3-662-49381-6; 978-3-662-49380-9. 10.1007/978-3-

662-49381-6_41. <Go to ISI>://WOS:000389380500041.

6. AUGUSTYNEK, Martin, David KORPAS, Marek PENHAKER, Jakub CVEK a Andrea

BINAROVA. Monitoring of CRT-D devices during radiation therapy in

vitro. BioMedical Engineering OnLine [online]. 2016, 15(1), - [cit. 2016-10-18]. DOI:

10.1186/s12938-016-0144-7. ISSN 1475-925x. Dostupné z: http://www.biomedical-

engineering-online.com/content/15/1/29

7. PENHAKER, Marek, Monika DAREBNIKOVA, Frantisek JUREK a Martin

AUGUSTYNEK. Evaluation of Electrocardiographic Leads and Establishing

Significance Intra-individuality. Innovations in Bio-inspired Computing and

Applications: Advances in Intelligent Systems and Computing. Springer International

Publishing, 2014, s. 295-303. ISBN 9783319017808.

8. AUGUSTYNEK, Martin, Darina FRIEDMANNOVA a Martina CMIELOVA.

Measuring of dependency between heart rate, respiratory rate and the human movement.

In: 12th IFAC Conference on Programmable Devices and Embedded Systems, PDeS

2013; Velke Karlovice; Czech Republic; 25 - 27 September 2013 2013-09-25, s. 292-

297. ISBN 978-390282353-3ISSN 14746670. DOI: 10.3182/20130925-3-CZ-

3023.00053.

9. PENHAKER, M., STULA, T., AUGUSTYNEK, M. Long-Term Heart Rate Variability

Assessment. In 5th Kuala Lumpur internal conference on biomedical engineering 2011,

BIOMED 2011. Jun 20-23, 2011, Kuala Lumpur, Malaysia, Volume35, IFMBE

Proceedings, p. 532 - 535. ISBN 978-3-642-21728-9, IDS Number: BBW52

10. AUGUSTYNEK, M., PENHAKER, M., KORPAS, D. Controlling Peacemakers by

Accelerometers. In 2010 The 2nd International Conference on Telecom Technology and

Applications, ICTTA 2010. March 19-21, 2010, Bali Island, Indonesia, Volume2 NJ.

IEEE Conference Publishing Services, 2010, p. 161–163. ISBN 978-0-7695-3982-9,

DOI: 10.1109/ICCEA.2010.288