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Issue 3 (189), article 4

DOI:https://doi.org/10.15407/kvt189.03.061

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Vovk М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: dep140@irtc.org.ua
Kutsyak A.А., PhD (Engineering), Researcher, Bioelectrical Control & Medical Cybernetics Department
e-mail: spirotech85@ukr.net
Lauta A.D., PhD (Medicine), Senior Researcher,
Bioelectrical Control & Medical Cybernetics Department
Ovcharenko М.А., Junior Researcher,
Bioelectrical Control & Medical Cybernetics Department
e-mail: ovcharenko-marina@i.ua
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

INFORMATION SUPPORT OF RESEARCHES ON THE DYNAMICS OF MOVEMENT RESTORATION AFTER THE STROKE

Introduction. The results of clinical testing of the innovative technology TRENAR® confirmed its effectiveness in motor and speech recovery after a stroke. The main advantage of the technology that enables a more efficient motor and speech recovery is advanced training programs, based on different methods. This allows one to select individual approach to the rehabilitation process. In order to determine a personalized recovery plan it is necessary to develop criteria to quantify motor recovery dynamics.
The purpose of the research is to determine criteria for separately quantifying recovery dynamics in proximal and distal parts of the upper and lower extremities, as well as to perform an integral quantitative assessment of the severity of motor function disorders after a stroke.
Results. The method for quantitative estimation of the effectiveness of motor function rehabilitation after a stroke was developed.
One special feature of the technique is separate quantitative assessment of the motor function deficit dynamics of the affected lower and upper extremities, their proximal and distal sections, including fine motor skills, according to basic and additional criteria. The technique allows us to study the contribution of these indicators to the integral quantitative evaluation of the effectiveness of motor recovery during rehabilitation process. The technique has successfully passed pilot clinical trials during the studies of motor function recovery dynamics after a stroke when innovative technology TRENAR® for motor training / recovery was used. It is intended for informational support of medical decision-making when devising an individual plan for the rehabilitation of motor and speech functions after a stroke.
Conclusion. The method for quantitative assessment of motor function recovery dynamics forms the basis for assessing the effectiveness of rehabilitation processes in patients after a stroke and for developing individual plans for rehabilitation. It serves as the digital informational support for physicians and will be essential for developing mobile applications for smartphones and tablets that can be used during the rehabilitation process.
Keywords: quantitative assessment, criteria, rehabilitation, effectiveness, stroke, motor functions, speech, disorders.

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REFERENCES

  1. Kolenko Ph.H., Stetsenko A.V., Stetsenko N.N. Optimization of the rehab process during cerebral stroke. Herald Sumy State University. Series Medicine. 2007. № 2. P. 61–66
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  8. Dolgova N.Yu., Vorontsov S.A., Kirichenko O.V., Makarova I.Yu. Rehabilitation treatment of neurological patients in a sanatorium. The Kremlin medicine. Clinical Herald. 2013, №4. P. 53–56 (in Russian).
  9. Belova A.N., Schepetova O.N. Scales, tests and questionnaires in medical rehabilitation M.: Antidor, 2002. 440 p. (in Russian).
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  11. Smychek V.B., Ponomareva E.N. Craniocerebral injury (clinic, treatment, examination, rehabilitation). Mn.: Research Institute of ME and R, 2010. 430 p. (in Russian).
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Received 9.06.2017

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Issue 3 (189), article 3

DOI:https://doi.org/10.15407/kvt189.03.044

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Balovsyak S.V., PhD (Phys-Math), Docent
Associate Professor (Docent) of the Department of Computer Systems and Networks
e-mail: s.balovsyak@chnu.edu.ua
Odaiska Kh.S., Postgraduate Student of the Department of Computer Systems and Networks
e-mail: k.odaiska@chnu.edu.ua
Yuriy Fedkovych Chernivtsi National University,
Kotsyubynsky St., 2, 58032, Chernivtsi, Ukraine

AUTOMATIC DETERMINATION OF LEVEL OF GAUSSIAN NOISE IN DIGITAL IM-AGES BY METHOD OF THE SELECTED REGIONS

Introduction. The noise level is an important parameter for common application tasks of digital image processing: noise removal, segmentation, recognition and other. At the experimental image processing the noise level in most cases is unknown, so development of a method for automatic and accurate determination of noise level in images is an actual and important for practice task [1–7]. Additive White Gaussian Noise (AWGN) belongs to the widespread noise model, since many noises in the real images are described rather accurately by the AWGN model [1]. For this reason this article will consider the methods of noise level determination in the images within the AWGN model, and this noise will further be called Gaussian noise for the sake of simplicity. Level of Gaussian noise is expressed by standard deviation of noise.
The purpose of the article is to develop an automatic method of Gaussian noise level determination in digital images, which uses the selection of image region based on its low-frequency filtering and performs calculation of noise level by analyzing of histograms of the selected region. The article is aimed at software implementation of the elaborated method in the MATLAB system and estimation of its accuracy by processing the collection of test images.
Methods. The method of the selected regions for calculation of Gaussian noise level, which involves the selection of image region and analysis of its histogram is used. Convolution operation to filter digital images is applied. For estimation of accuracy of noise level determination the root mean square error (RMSE) between the experimental and theoretical noise levels for the test images is used.
Results. The method of automatic determination of level of Gaussian noise in digital images is developed. A method consists in the selection of the image ROI (Region Of Interest) [1, 3], where the noise is mainly present, in the calculation of histogram of the selected region and standard deviation of histogram and in the calculation of experimental level of noise based on value . If the variation does not exceed the established limit, the process of the ROI clarification is completed. The proposed method is software implemented in MATLAB system [3].
Conclusion. The mathematical model of image filtering with Gaussian noise is created. The created model allows selecting the ROI regions with the prevailed Gaussian noise [8, 9]. The modification of the algorithm for determining the level of Gaussian noise in images is implemented [10–12], which specifies the minimum permissive value of the ROI area, leading to reducing the root mean square error of calculation of noise level on 0.1%.
The accuracy of the proposed method is studied by processing the set of 100 test images [11, 12], and the root mean square error of calculation equals to 0.257%. The resulting error of calculation is less, than the one obtained for the most accurate modern methods of determination of noise level [4, 5]. With the use of more precise method of the ROI noise level determination, different from histogram analysis, the accuracy of the proposed method can be improve.
Keywords: digital image processing, noise level determination, standard deviation of Gaussian noise, histogram of image.

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REFERENCES

1 Gonzalez R., Woods R. Digital image processing. M.: Technosphere, 2005. 1072 p. (in Russian).

2 Bovik A.L. The Essential Guide to Image Processing. Elsevier Inc., 2009. 853 p.

3 Gonzalez R., Woods R., Eddins S. Digital image processing using MATLAB. M.: Technosphere, 2006. 616 p. (in Russian).

4 Liu X., Tanaka M., Okutomi M. Single-Image Noise Level Estimation for Blind Denoising. IEEE Transactions on Image Processing. 2013. Vol. 22, No. 12. P. 5226–5237.
https://doi.org/10.1109/TIP.2013.2283400

5 Pyatykh S., Hesser J., Zheng L. Image noise level estimation by principal component analysis. IEEE Transaction on Image Processing. 2013. Vol. 22, No. 2. P.687–699.
https://doi.org/10.1109/TIP.2012.2221728

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7 Zoran D., Weiss Y. Scale invariance and noise in natural images. Proc. IEEE 12th Int. Conf. Comput. Vis., Sep./ Oct. 2009. P. 2209–2216.
https://doi.org/10.1109/ICCV.2009.5459476

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10 Image Processing Place. Image Databases. URL: http://www.imageprocessingplace.com/ root_files_V3/image_databases.htm.

11 Fowlkes C., Martin D., Malik J. Local Figure/Ground Cues are Valid for Natural Images. Journal of Vision. 2007. Vol. 7 (8), No. 2. P. 1–9.

12 The Berkeley Segmentation Dataset and Benchmark. BSDS300. URL: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds.

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Issue 3 (189), article 2

DOI:https://doi.org/10.15407/kvt189.03.029

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Zhiteckii L.S., PhD (Engineering), Acting Head of the Department of Intelligent Automatic Systems
e-mail: leonid_zhiteckii@i.ua
Solovchuk K.Yu., Postgraduate Student
e-mail: solovchuk_ok@mail.ru
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

DISCRETE-TIME STEADY-STATE CONTROL OF INTERCONNECTED SYSTEMS BASED ON PSEUDOINVERSION CONCEPT

Introduction. The problem of controlling interconnected systems subjected to arbitrary unmeasurable disturbances remains actual up to now. It is important problem from both theoretical and practical points of view. During the last decades, the internal model control principle becomes popular among other methods dealing with an improvement of the control system. A perspective modification of the internal model control principle is the so-called model inverse approach. Unfortunately, the inverse model approach is quite unacceptable if the systems to be controlled are square but singular or if they are nonsquare. It turned out that the so-called pseudoinverse (generalized inverse) model approach can be exploited to cope with the noninevitability of singular square and also nonsquare system.
The purpose of the paper is to generalize the results obtained by the authors in their last works which are related to the asymptotic properties of the pseudoinverse model-based method for designing an efficient steady-state control of interconnected systems with uncertainties and arbitrary bounded disturbances and also to present some new results.
Results. In this paper, the main effort is focused on analyzing the asymptotic properties of the closed-loop systems containing the pseudoinverse model-based controllers. In the framework of the pseudoinversion concept, new theoretical results related to the asymptotic behavior of these systems are obtained. Namely, in the case of nonsingular gain matrices with known elements, the upper bounds on the ultimate norms of output and control input vectors are found. Next, in the case of nonsquare gain matrices whose elements are also known, the asymptotic behavior of the feedback control systems designed on the basis of pseudoinverse approach are studied. Further, the sufficient conditions guaranteeing the boundedness of the output and control input signals for the linear and certain class of nonlinear interconnected systems in the presence of uncertainties are derived.
Conclusion. It has been established that the pseudoinverse model-based concept can be used as a unified concept to deal with the steady-state regulation of the linear interconnected discrete-time systems and of some classes of nonlinear interconnected systems with possible uncertainties in the presence of arbitrary unmeasured but bounded disturbances.
Keywords: discrete time, feedback, pseudoinversion, interconnected systems, optimality, stability, uncertainty.

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https://doi.org/10.1109/TAC.1972.1100084

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3 Lyubchyk L. M. Disturbance rejection in linear discrete Multivariable systems: inverse model approach. Prep. 18th IFAC World Congress, Milano, Italy, 2011, pp. 7921–7926.
https://doi.org/10.3182/20110828-6-IT-1002.02121

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https://doi.org/10.1016/S1474-6670(17)69194-8

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19 Zhiteckii L. S., Azarskov V. N., Solovchuk K. Yu., Sushchenko O. A. Discrete-time robust steady-state control of nonlinear multivariable systems: a unified approach. Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 8140–8145.
https://doi.org/10.3182/20140824-6-ZA-1003.01985

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21 Albert A. Regression and the Moore-Penrose Pseudoinverse. New York: Academic Press, 1972.

22 Zhiteckii L. S., Skurikhin V. I. Adaptive Control Systems with Parametric and Nonparametric Uncertainties. Kiev: Nauk. dumka, 2010 (in Russian).

23 Lancaster P., Tismenetsky M. The Theory of Matrices: 2nd ed. With Applications. N.Y.: Academic Press, 1985.

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Issue 3 (189), article 1

DOI:https://doi.org/10.15407/kvt189.03.005

Kibern. vyčisl. teh., 2017, Issue 3 (189), pp.

Orikhovska K.B., Postgraduate student,
Junior Researcher of the Department of Intelligent Automatic Systems
e-mail: kseniaor@gmail.com
Fainzilberg L.S., Dr (Engineering), Associate Professor (Docent), Chief Researcher of Data Processing Department
e-mail: fainzilberg@gmail.com

International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03680, Ukraine

COMPARATIVE ANALYSIS OF ESTIMATION METHODS OF THE PHYSIOLOGICAL SIGNALS VARIABILITY

Introduction. In the modern world, more attention is paid to the study of the behavior of complexly organized medical and biological systems. The fundamental concept of synergetics is the generalized entropy, which quantitatively characterizes the degree of the system chaoticness. Of special interest are studies of changes in the dynamic series chaotic parameters generated by various biological systems.
The purpose of the article is further development and experimental research of methods for analyzing the variability of physiological signals under external influences on the body.
Methods. Two alternative approaches of estimating the variability of dynamic series are investigated: based on the calculation of the sample variance relative changes and entropy estimates (in a sliding window with the specified parameters) in relation to the first window. The theoretical and experimental dependences between the Shannon entropy and the standard deviation for a normal distribution of a random variable that generates a dynamic series are studied. Comparison of these estimates with real and model data is carried out.
Results. To increase the sensitivity of entropy estimates to the variability of the dynamic series, it is proposed to move from a series of discrete entropy values at the -th point, calculated by the sliding window method, to its phase portrait on the plane , where is the estimate of the first derivative . For an integral assessment of the chaotic nature of physiological signals, it is suggested to estimate the area of the convex hull of the entropy phase portrait and the coordinates of the phase portrait gravity center , . Experimental studies have confirmed the diagnostic value of these parameters in the assessment of variability of the electrocardiograms and rhythmograms indices with external influences on the body (intravenous therapy, surgery and physical activity).
Conclusions. Deviations of the integral parameters of the entropy phase portrait under the effect of external influences on the organism were detected, which open new possibilities in the evaluation of the cardiac activity regulation in preventive and clinical medicine. These integral parameters require further study to confirm their statistical significance in representative samples of observations.

Keywords: variability of physiological signals, entropy estimates, diagnostic criteria.

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REFERENCES

1 Klimontovich Yu.L. Introduction to physics of open systems. Moscow: Janus–K; 2002. 284 p.

2 Martin-Sanchez F., Iakovidis I., Norager S., Maojo V., de Groen P., Van der Lei J., Jones T., Abraham-Fuchs K., Apweiler R., Babic A., Baud R., Breton V. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. Journal of Biomedical Informatics. 2004. Vol. 37. N 1. P. 30–42.
https://doi.org/10.1016/j.jbi.2003.09.003

3 Weippert M., Behrens M., Rieger A., Behrens K. Sample entropy and traditional measures of heart rate dynamics reveal different modes of cardiovascular control during low intensity exercise. Entropy. 2014. Vol. 16. P. 5698–5711.
https://doi.org/10.3390/e16115698

4 Durnova N.Yu., Dovgalevskiy Ya.P., Burlaka A.N., Kiselev A.R., Furman N.V. Interdependence of parameters of variational pulsometry, entropy of heart rate, temporal and spectral analyses of heart rate variability in normal state and in ischemic heart disease. Saratov journal of medical scientific research. 2011. Vol. 7. N 3. P. 607–611.

5 Ban A.S., Paramonova N.A., Zagorodnyy G.M., Ban D.S. Analysis of the relationship of heart rate variability indices. Voennaya Meditsina. 2010. N 4. P. 21–24.

6 Joshua S., Richman J., Moorman R. Physiological time-series analysis using approximate entropy and sample entropy. The American journal of physiology. 2000. Vol. 278. N 6. P. 2039–2049.

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https://doi.org/10.1063/1.166141

8 Iyengar N., Peng C.K., Morin R., Goldberger A.L., Lipsitz L.A. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 1996. Vol. 271. P. 1078–1084.
https://doi.org/10.1152/ajpregu.1996.271.4.R1078

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22 Vlasova I.V. There are more and more side effects in drugs. Commercial biotechnology. 2007. Vol. 10. P. 14–19.

Received 5.06.2017

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Issue 3 (189)

DOI:https://doi.org/10.15407/kvt189.03

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TABLE OF CONTENTS:

Informatics and Information Technologies:

Orikhovska K.B., Fainzilberg L.S.
Comparative Analysis of Estimation Methods of the Physiological Signals Variability

Intellectual Control and Systems:

Zhiteckii L.S., Solovchuk K.Yu.
Discrete-Time Steady-State Control of Interconnected Systems Based on Pseudoinversion Concept

Balovsyak S.V., Odaiska Kh.S.
Automatic Determination of Level of Gaussian Noise in Digital Images by Method of the Selected Regions

Medical and Biological Cybernetics:

Vovk М.І., Kutsyak A.А., Lauta A.D., Ovcharenko М.А.
Information Support of Researches on the Dynamics of Movement Restoration After the Stroke

Shvets A.V., Kich A.Y.
The Decision Support Model for Forecasting of Wounded and Sick Restoration in Hospital Conditions Based on Psychophysiological Data

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Issue 2 (188), article 6

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

80TH ANNIVERSARY OF CORRESPONDING MEMBER OF NAS OF UKRAINE VLADIMIR ILYICH GRITSENKO

May 23, 2017 the 80th anniversary of Vladimir Ilyich Gritsenko, known scientist in computer science, information technologies and its applications in economics, industrial and technological field, biological and medical cybernetics, computer technology training, director of the International Scientific and Training Center for Information Technologies and Systems. He is an initiator of development of a new class of high technologies — intelligent information technologies. Gritsenko V.I. is a member of a number of leading international and state councils of Ukraine on informatics, Permanent Representative of Ukraine to the Council of UNESCO Intergovernmental Programme on the information and communications, heads the UNESCO Chair “New Information Technologies in Education for All”, the chief editor of the scientific journals “Control Systems and Machines” and “Cybernetics”.

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Issue 2 (188), article 5

DOI:https://doi.org/10.15407/kvt188.02.075

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Rudenko А.V.1, Professor, Corresponding member NAS of Ukraine,
First deputy of director
e-mail: info@rudenkoav.com.ua
Nastenko I.А.1,2, Doctor (in Biology), PhD (in Technics),
Head of the Department of Information technologies and mathematical modeling
of physiological processes, Head of the Department of Biomedical Cybernetics
e-mail: nastenko@inbox.ru
Zhurba O.А.1, Cardiovascular surgeon
e-mail: olegzhurba2009@yandex.ua
Nosovets О.K.2, (in Technics),
Senior lecturer of the Department of Biomedical Cybernetics
e-mail: e.nosovets@yandex.ua
Shardukova Y.V.1, Researcher at the Department of Information technology
and mathematical modeling of physiological processes
e-mail: julie_sea@mail.ru
Lasoryshinets V.V.1, Professor, (in Medicine), Director
e-mail: lazorch@ukr.net
1 National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», 37, Pobedi st., Kyiv, 03056, Ukraine
2 Government Facility “M.M. Amosov National Institute of Cardiovascular Surgery of National Academy of Medical Sciences of Ukraine”, 6, Amosov st., Kyiv, 03110, Ukraine

EVALUATION OF RISK FACTORS FOR OPERATIONS CORONARY BYPASS SURGERY ON A BEATING HEART

Introduction. The planned beating heart coronary aortic bypass grafting operations (BH CABG) prepares with and without parallel perfusion circulatory support. In second case the necessity of emergent use of circulatory support can appear. In these situations, the frequency of postoperative complications in circulatory system increases.

The decision about the planned use of circulatory support makes with analysis of big number of clinical data. This causes necessity to create the computer decision support systems to minimize the risk of emergent use of parallel perfusion.

The purpose of the article is to analyse statistically the risk factors for BH CABG operations on a working heart with the aim to minimize the risk of circulatory support emergent use.

Clinical material. 972 patients which undergone the BH CABG without circulatory support, 178 patients with planned use of circulatory support and 90 patients with emergent use of circulatory support. The 67 clinical parameters with package IBM SPSS Statistics 21.0 were analyzed.

Methods. The algorithm of binary logistic regression (BLR) for decision support systems development were used.

Results and discussion. The association between clinical values in groups without circulatory support, their planned and emergent use as well as their odds ratios and confidence intervals were analyzed. Then BLR algorithm to build the models for estimation of probability of planned and emergent use of circulatory support was used. The strategies of compulsory and stepwise inclusion of parameters were applied. The sensitivity, specificity and accuracy of the models obtained for learning and examination samples. The best models were chosen were calculated.

Conclusions. The created predictive models have a high sensitivity, specificity and accuracy, and can reduce the influence of subjective factors on medical decisions making regarding the use of the artificial circulatory support during off-pump coronary artery bypass surgery. Analysis of the variables included in the model, can contribute to a better understanding of the structure of existing pathogenic factors. The additional estimation the risk of cardiopulmonary bypass emergent use can reduce the likelihood of such situations occurrence.

Keywords: the beating heart coronary aortic bypass grafting, circulatory support with cardiopulmonary bypass, predicting algorithms.

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REFERENCE

1 Mykheev A.A., Klyuzhev V.M., Karpun N.A. Surgery on coronary arteries on a working heart without artificial circulation in IHD patients. M.: Medicine, 2001. 43 p. (in Russian).

2 Allen B. S., Rosenkranz E.R., Buckberg G.D. Studies of controlled reperfusion after ischemia. VII. High oxygen requirements of dyskinetic cardiac muscle. Journal of Thoracic and Cardiovascular Surgery. 1986. No92. P. 543–552.

3 Mo A., Lin H., Wen Z.Efficacy and safety of on-pump beating heart surgery. Ann Thorac Surg. 2008. No 86. P. 1914–1918.
https://doi.org/10.1016/j.athoracsur.2008.07.003

4 Puskas J. Presidential Address, 2009: ISMICS Means Innovation. Innovations: Technology & Techniques in Cardiothoracic & Vascular Surgery. 2009. No 4. P. 240–247.
https://doi.org/10.1097/IMI.0b013e3181bae75a

5 Shabalkyn B.V., Zhbanov Y.V. Minimally invasive myocardial revascularization or aortocoronary bypass without artificial circulation? Bulletin of the Center Bakulev RAMS. V All-Russian Congress of Cardiovascular Surgeons. Novosibirsk, 1999. 152 p. (in Russian).

6 Beauford R.B., Goldstein D.J., Sardari F.F. Multivessel off-pump revascularization in octogenarians: early and midterm outcomes. Ann. Thorac. Surg. 2003. Vol. 76. P. 12–17.
https://doi.org/10.1016/S0003-4975(03)00014-6

7 Stamou S., Bail A., Boyce S.Coronary revascularization of the circumflex. Ann. Thorac. Surg. 2000. Vol. 70. P. 1371–1377.
https://doi.org/10.1016/S0003-4975(00)01680-5

8 Witten Ian H., Frank Eibe, Hall Mark A. Data Mining: Practical Machine Learning Tools and Techniques. [3rd Ed.]. Morgan Kaufmann, 2011. P. 664.

9 McHugh M. L. The odds ratio: calculation, usage, and interpretation. Biochemia Medica. 2009. No19 (2). P. 120–126.

10 Sperandei S. Understanding logistic regression analysis. Biochemia Medica. 2014. 24(1). P. 12–18.
https://doi.org/10.11613/BM.2014.003

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Issue 2 (188), article 4

DOI:https://doi.org/10.15407/kvt188.02.065

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Grygoryan R.D., Dr (in biology),
Chief of Department of human systems modeling and reliability
e-mail: rgrygoryan@gmail.com
Aksenova T.V., Junior-researcher
e-mail: akstanya@ukr.net
Degoda A.G., Senior-researcher, PhD (in math.)
e-mail: mag-87@inbox.ru
Institute of software systems of National Аcademy of Sciences of Ukraine,
40, Acad.Glushkov ave., corp.5, Kiev, 052187, Ukraine

A COMPUTER SIMULATOR OF MECHANISMS PROVIDING ENERGY BALANCE IN HUMAN CELLS

Introduction. Human organism is a too complex system to be empirically examined and comprehended: there is no method for simultaneously measuring or integrally analyzing of billions of multi-scale life variables. Experts need models and information technologies that causally incorporate cell-scale and organism-scale biophysical and physiological data.

The purpose of the article is to describe a specialized simulator (SS) providing users of additional information concerning scenarios and multi-level mechanisms of energy optimization in the human organism.

Results. Multi-scale mechanisms providing cell energy balance (CEB) are in the basis of SS. At cell-level, providers of CEB form a battery of autonomous mechanisms (BAM). Under energy lack (EL), BAM increases the rate of ATP synthesis using local opportunities. If EL remains, extracellular providers of aerobic synthesis of ATP enlarge current potentials of the EL-cell. The SS provides simulation scenarios increasing the lung ventilation, the erythropoiesis, blood concentrations of carbohydrates, and of other nutrients for biogenesis of mitochondria. The role of the cardiovascular system is in regulating of blood incomes to EL-cells.

Conclusion. The SS is a novel informational technology of dual goals. Explaining the function of mechanisms-providers of CEB, the SS also can be used by applied physiologists and physicians for the planning of optimal scenarios for physical empowering of healthy people.

Keywords: mathematical models, mitochondria, glucose, integrative physiology, medicine.

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REFERENCE

1 Skulachev V.A.,Bogachev A.V., Kasparinsky F.O. Principles of Bioenergetics. Springer-Verlag, Berlin Heidelberg, 2013. 436 p.
https://doi.org/10.1007/978-3-642-33430-6

2 Michiels C. Physiological and Pathological Responses to Hypoxia. Am J Pathol. 2004. No164. P. 1875–1882.
https://doi.org/10.1016/S0002-9440(10)63747-9

3 Kandel J., Angelin A.A., Wallace D.C. Mitochondrial respiration is sensitive to cytoarchitectural breakdown. Integr. Biol. (Camb). 2016, 8 (11). P. 1170–1182.
https://doi.org/10.1039/C6IB00192K

4 Finsterer J. Hematological manifestations of primary mitochondrial disorders. Acta Haematol. 2007. 118 (2). P. 88–98.
https://doi.org/10.1159/000105676

5 Mali V.R., pan G., Deshpande M. Cardiac Mitochondrial Respiratory Dysfunction and Tissue Damage in Chronic Hyperglycemia Correlate with Reduced Aldehyde Dehydrogenase-2 Activity. PLoS One. 2016.11 (10):e0163158.

6 Grygoryan R.D. The Energy Basis of Reversible Adaptation. N.Y.: Nova Science, 2012. 252 p.

7 Grygoryan R.D., Lyabakh K.G. Arterial pressure: a comprehension. Kyiv: ISS of National Academy of Sciences of Ukraine. 2015. 458 p. (In Russian).

8 Grygoryan R.D. The “floating” arterial pressure paradigm: a concept of physiological supersystems. Dusseldorf: Palmarium Academic Publishing. 2016. 417 p. (In Russian).

9 Grygoryan R.D., Deriev I.I., Aksionova T.V. A software simulator of aerobe cell’s responses to energy imbalance. Problems in programming. 2014. No 1. P. 90–98. (In Russian).

10 Grygoryan R.D., Aksionova T.V., Markevich R.A. A software simulator of pancreas. Problems in programming. 2013. No 1. P. 100–106. (In Russian).

11 Grygoryan R.D., Aksionova T.V., Degoda A.G. Modeling of mechanisms and hemodynamic effects of heart hypertrophy. Cybernetics and computer engineering. 2016. Issue.184. P. 72–83. (In Russian).

12 Grygoryan R.D., Aksionova T.V. Modeling of organism-scale mechanisms fighting against energy shortage in cells. Bulletin of University “Ukraine”. Series: Informatics, computers, and cybernetics. 2016. P. 91–99. (In Russian).

13 Aksionova T.V. A software technology providing simulations of mathematical models of physiological systems. Problems in programming. 2012. No1. P. 110–120. (In Russian).

14 Chada S.R., Hollenbeck P.J. Nerve growth factor signaling regulates motility and docking of axonal mitochondria. Curr. Biol. 2004. V.14. P. 1272–1276.
https://doi.org/10.1016/j.cub.2004.07.027

15 Ramamurthy S., Ronnett G. AMP-activated protein kinase (AMPK) and energy-sensing in the brain. Neurobiol. 2012. 21, No 2. P. 52–60.
https://doi.org/10.5607/en.2012.21.2.52

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Issue 2 (188), article 3

DOI:https://doi.org/10.15407/kvt188.02.049

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Aralova N.I., senior researcher of department of optimization of controlled processes
e-mail: aralova@ukr.net
Institute of cybernetics of National Academy of Science of Ukraine,
Acad. Glushkov ave., 40, Kiev, 03680 GSP, Ukraine

RESERCH THE ROLE OF HYPOXIA, HYPERCAPHNIA AND HYPOMETABOLISM IN THE REGULATION OF THE RESPIRATORY SYSTEM IN THEIR INTERNAL AND EXTERNAL DISTURBANCES BASED ON THE MATHEMATICAL MODEL

Introduction. Under conditions of the physical exertion and human presence on the altitude, the oxygen deficiency in tissues occurs. For a theoretical study of the role of various mechanisms in the regulation of the respiratory system, the use of the mathematical model for the transport of respiratory gases in the body was proposed.

Purpose. Researches of the role of hypoxia, hypercapnia and hypometabolism in external and internal disturbances, based on the mathematical model of the respiratory system.

Results. On the mathematical model of respiratory gas transport in the dynamics of the respiratory cycle, as control parameters, pulmonary ventilation, minute blood volume and local blood flow, as well as self-regulation mechanisms — respiratory muscles, cardiac muscle and smooth muscle vessels — were used. It resolved the conflict situation that arises between the managers and the executive tissues in the fight for oxygen. An analysis of the results of numerical experiments in simulating hypoxia and hypoxic hypoxia and their comparison with experimental data was made.

Conclusion. The proposed approach can be useful in assessing the role of hypoxia, hypercapnia and hypometabolism in the disturbances of the internal and external environment in the process of human vital activity under extreme conditions and leads to the formulation of new tasks in the physiology of sports, work and leisure.

Keywords: Mathematical model of respiratory gas transport, load hypoxia, hypoxic hypoxia, regulation of the respiratory system, disturbing effects, oxygen deficiency.

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REFERENCE

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    tics. 1986. № 3. P. 100–104 (in Russian).
  2. Bioecomedicine. Unified informative space / ed. IN AND. Grytsenko. Kyiv: Nauk. dumka, 2001. 314 p. (in Russian).
  3. Filippov M.M. Modes of mass transfer of oxygen and carbon dioxide in muscle activity. Special and clinical physiology of hypoxic conditions. Kiev: Nauk. dumka, 1979. 3. P. 208–214 (in Russian).
  4. Secondary tissue hypoxia / under the general. Ed. A.Z. Kolchinskaya. K.: Nauk. dumka. 1983. 253 p. (in Russian).
  5. Kolchinskaya A.Z. On the classification of hypoxic states. Pathological physiology and experiment. Therapy. 1981. Issue 4. P. 9–10 (in Russian).
  6. Mudrik V.I. Features of the development of oxygen deficiency in humans under the influence of muscular activity of moderate intensity. Special and clinical physiology of hypoxic conditions. Kiev: Nauk.duma, 1979. 3. P. 173–178 (in Russian).
  7. Lyabakh E.G. Study of hypoxia in skeletal muscle on a mathematical model. Special and clinical physiology of hypoxic states. Kiev: Nauk. dumka, 1979. 2. P. 189–194 (in Russian).
  8. Kolchinskaya A.Z., Misyura I.N., Mankovskaya A.G. Breathing and oxygen regimes of dolphins. Kiev: Nauk. dumka, 1980. 332 p. (in Russian).
  9. Filippov A.F. Differential equations with discontinuous right-hand side. M.: Nauka, 1985. 224p. (in Russian).
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  12. Onopchuk Yu.N. Equilibrium systems and transient processes in the systems of external respiration and circulation. Research on the mathematical model. Cybernetics. 1991.
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  15. Bobriakova I.L. Mathematical modeling of the hypometabolism of the human body. Cybernetics and computer technology. 2014. Issue.178. P. 64–69.

Recieved 29.03.2017

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Issue 2 (188), article 2

DOI:https://doi.org/10.15407/kvt188.02.036

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Khorozov O.A., Ph.D (Phys-Math), Leading Researcher
e-mail: oleh753@hotmail.com
Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine,
Chokolovskiy ave., 13, Kiev 04186, Ukraine

APPLICATION OF FUZZY LOGIC FOR TELEMEDICINE SYSTEMS

Introduction. The telemonitoring system of patient’s vital signs for primary diagnosis and detection of abnormal values biophysical indicators is described. Expert estimates inherent in fuzzy logic rules are compared with the measured values of the vital signs for disease risk counting. The system is implemented at the Arduino with code for fuzzy logic controller. The structure of distributed management of the warning system is represented.

The purpose of the article is to develop an expert system based on fuzzy logic rules to calculate the risk level of the patient and use feedback control in decision-making.

Method. Expert estimates inherent in fuzzy logic rules are compared with the measured values of the vital signs for disease risk estimation.

Results. Expert system was considered for determination of patient’s health risk level. The fuzzy logic rules was formed for determination of belonging variables to risk groups and used for reflect the input to the decision making process. The application detects anomalous values of monitoring data, generates a medical report and sends it to the server for decision-making. The system includes monitors vital signs of the patients, warning services based on Fuzzy Logic techniques with the objective of reducing the risk from the slow provision of health care. The architecture of the integrated mHealth platform with functional models was proposed.

Conclusions. Telemedicine system was designed for primary diagnosis and monitoring of patients on the basis of fuzzy logic. The method based on expert knowledge, which are incorporated in the rules of fuzzy logic to compare the values of the input parameters of patients and disease risk prediction was used. The technique is common in detecting abnormal values biophysical indicators for disease risk assessment.

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REFERENCE

  1. S.Dutta, A.Maeder, J.Basilakis, Using Fuzzy Logic for Decision Support in Vital Signs Monitoring Jjint Workshop Proceedings, 26th Australasian Joint Conference on Artificial Intelligence, 2013, p. 29–33.
  2. M. Mayilvaganan, K. Rajeswari, Risk Factor Analysis to Patient Based on Fuzzy Logic Control System. International Journal of Engineering Research and General Science. 2014. Vol. 2. Issue 5. P. 185–190.
  3. M.K. Choudhury, N. Baruah, A Fuzzy Logic Based Expert System for Denermination of Health Risk Level of Patient. International Journal of research in Engineering and Technology. 2015. Vol. 4. Issue 5. P.261–267.
  4. A.Povoroznjuk, E.Kharchenko, The use of fuzzy logic in computer systems medical diagnostics. Vestnik National Technical University. 2015. № 33. P. 125–133.
  5. Aj O. Alves URL: https://github.com/zerokol/eFLL.
  6. S. Sriparasa. JavaScript and JSON Essentials, 2013. URL: https://books.google.co.in/books?id=MZOkAQAAQBAJ

Recieved 09.03.2017