Issue 2 (200), article 5

DOI:https://doi.org/10.15407/kvt200.02.076

Cybernetics and Computer Engineering, 2020, 2(200)

Belov V.M., DSc (Medicine), Professor,
Head of the Department
e-mail: motj@ukr.net

Hontar T.M., PhD (Biology),
Senior Researcher
e-mail: gtm_kiev@ukr.net

Kobzar T.A., PhD (Medicine),
Senior Researcher
e-mail: kobzarta@ukr.net

Kozlovska V.O.,
Researcher
e-mail: vittoria13apr@gmail.com

International Research and Training Centre for Information Technologies
and Systems of the NAS and MES of Ukraine, Department of Application Mathematical and Technical Methods in Biology and Medicine
40, Glushkov av., Kyiv, 03187, Ukraine

HEALTH SELF-ESTEEM INFORMATION TECHNOLOGY
FOR REHABILITATION OF POST-TRAUMATIC STRESS DISORDER

Introduction. Contemporary research aimed at preserving and maintaining human health is based on the use of intellectual information technology, developed on methodology of a systematic approach to the category of health as a trinity of its physical, mental and social aspects. The importance of a comprehensive approach to human health becomes especially evident in the case of breach of the harmonious interaction of the human body and personality with the environment in the example of post-traumatic stress disorder. At the present stage of economic and political development of the state, the factor of negative psychogenic impact on the health of the population has significantly increased. The creation of information technology for the assessment of health and rehabilitation of a person having post-traumatic stress disorder (PTSD) would make it possible to turn the rehabilitation process into a manageable and controlled one.

The purpose of the paper is to determine the information structure of post-traumatic stress disorder and the formation of main provisions of information technology of health self-assessment for the rehabilitation of post-traumatic stress disorder.

Results. The paper deals with the assessment of integral health level for people having PTSD and psychological and social rehabilitation of such patients. General features of psychogenic disorders and theoretical features of formation of post-traumatic stress disorder are discussed, taking into account the possibilities of the information approach. Information field of post-traumatic syndrome in general is offered, where on the basis of available literature the main exogenous stressors, protective variants of reactions of the organism and personality, variants of addictive behavior of persons suffering from post-traumatic stress disorder are determined. Information technology of self-assessment of physical, mental and social state of health by means of developed hardware-software “Express diagnostics of health” complex enabling to estimate integral health and its components using the applied questionnaire, is offered. The mental status of health determines the properties and strength of personality, which are important to consider and rely on in the process of rehabilitation of patients with post-traumatic stress disorder.

Conclusions. Availability of objective and subjective data on the state of health of a person with PTSD, including certain quantitative criteria for its health, as well as taking into account the information field of, which combines main exogenous stressors and their corresponding protective variants of organism reactions and variants of addictive behavior, gives the chance to analyze in detail its condition and compose individual programs of rehabilitation actions. At the same time, knowledge of a person’s character makes it possible with high probability to predict and, thus, adjust his expected actions and deeds. The use of developed hardware and software modules for health assessment in the case of PTSD will favor the effectiveness of rehabilitation measures.

Keywords: information technology, health self-assessment, mental health, personality character, methods of testing and diagnostics, post-traumatic stress disorder, psychosocial and social rehabilitation.

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REFERENCES

  1. Diagnostic criteria DSM-III-R. Kyiv: Abris, 1995, 272 p. (in Ukrainian).
  2. Chaban O.S. Frankova I.A. Current trends in the diagnosis and treatment of post-traumatic stress disorder. NeuroNews: Psychoneurology and Neuropsychiatry. 2015, No. 2 (66), pp. 818. (in Russian).
  3. Post-traumatic stress disorder — diagnosis, therapy, rehabilitation: from the materials of the scientific-practical conference with international participation “Modern approaches to the diagnosis, therapy and rehabilitation of post-traumatic stress disorders” (28th – 29th of May, Kharkiv). Kharkiv, Ukraine, 2015. Ukr. med. chasopis. 2015, No. 4 (108), VII / VIII, pp. 810. (in Russian).
  4. Post-traumatic stress disorder. GBOU VPO Rostov State Medical University of the Ministry of Health of Russia. Rostov-on-Don: Publishing house of Rostov State Medical University, 2015, 624 р. (in Russian).
  5. Tarabrina N.V. Psychology of post-traumatic stress: theory and practice. Moscow: Publishing House Institute of Psychology RAS, 2009, 304 p. (in Russian).
  6. Tarabrina N.V., Lazebnaya E.O., Zelenova, M.E., Petrukhin E.V. Levels of subjective-personal perception and experience of “invisible” stress. Moscow: Medicine, 1996. (in Russian).
  7. Crystal John. Post-traumatic stress syndrome. September 14, 2016. URL: https://postnauka.ru/faq/68268. (in Russian).
  8. World Bank experts have assessed the mental health sector in Ukraine – a statement from the Ministry of Health of Ukraine dated November 3, 2017. URL:  https://moz.gov.ua/article/news/eksperti-svitovogo-banku-dali-ocinku-galuzi-psihichnogo-zdorovja-v-ukraini. (in Ukrainian).
  9. Comer Ronald. Pathopsychology of behavior: disorders and pathologies of the psyche / trans. with English, 4th ed., International. St. Petersburg: PRIME EUROZNAK, Moscow: OLMA-PRESS, 2005, 638 p. (in Russian).
  10. Jaspers Carl. General psychopathology. Moscow: Practice, 1997, 1056 р. (in Russian).
  11. Belov V.M., Kotova A.B. Human health: challenges, methods, approaches. Kyiv: Naukova Dumka, 2017, 132 р. (in Russian).
  12. Belov V.M., Kotova A.B., Pustovoit O.G. Method of standardized unification of different quality information: utility model patent № 11235. Bull. №12. Publ. 12/15/2005. (in Ukrainian).
  13. Belov V.M., Kotova A.B., Dubovenko M. N., Kiforenko S.I. Computer program “System of express diagnostics of a state of health”: a certificate of registration of copyright law on the work №37242, Ukraine. – 04/03/2011. (in Ukrainian).
  14. Zotov V.P., Antomonov Yu.G., Kotova A.B., Belov V.M. Introduction to Wellness Rehabilitation. Kyiv: Madekol, RSPFN “Medicine-Ecology”, 1995, 181 p. (in Russian).
  15. Belov V.M. Structural and functional organization of the “I” system. Cybernetics and computing. 2006, Issue 152, pp.3-19. (in Russian).
  16. Raigorodsky D.Ya. (ed.-comp.). Psychology and psychoanalysis of character. A reader on psychology and typology of character. Samara: ВАХRAX, 1997, 640 р.
    (in Russian).
  17. Belov V.M., Hontar T.M., Semchinskaya E.I. Taking into account the state of the characterological component of a person in the synthesis of health self-government programs. Cybernetics and computing. 2009, Issue. 158, pp.3-10. (in Russian).

Received 20.03.2020

Issue 2 (200), article 4

DOI:https://doi.org/10.15407/kvt200.02.059

Cybernetics and Computer Engineering, 2020, 2(200)

KOCHINA M.L.1, DSc (Biology), Professor,
Head of the Medical and Biological
Basics of Sports and Physical Rehabilitation Department
e-mail: kochinaml@gmail.com

KOZAK L.M.2, DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
e-mail: lmkozak52@gmail.com

YAVORSKY O.V.3, DSc (Medicine),
Professor of Ophthalmology Department

FIRSOV O.G.4, PhD (Engineering),
Chief Designer
e-mail: shagrath.hire@gmail.com

YEVTUSHENKO A.S.5, PhD (Medicine),
Ophthalmologist
e-mail: andrey-eye@yandex.ru

1Petro Mohyla Black Sea National University
10, 68-Desantnykiv st., Mykolaiv, 54000, Ukraine
2International Research and Training Centre for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
3Kharkiv National Medical University,
4, Nauky av., Kharkiv, 61000, Ukraine
4ASTER-AITI, LTD
1, Aviation st., Kharkiv, 61166, Ukraine
5L.L. Hirschman Kharkiv city clinical hospital №14
5, Oles Honchar st., Kharkiv, 61000, Ukraine

MODEL AND METHOD FOR EVALUATION AND FORECAST
OF THE CHANGES OF VISUAL SYSTEM FUNCTIONAL STATE
IN CONSEQUENCE OF VISUAL WORK

Introduction. During mental work, 90% of information is perceived by the human visual system (VS), so the effectiveness of the activities depends on the quality of the VS functioning and the presenting of visual information, especially non-traditional forms (TV, personal computer monitor, miniature displays on mobile phones, e-books). Prolonged information overload can lead to the states such as chronic stress, chronic fatigue syndrome, neurosis, occupational burnout and asthenopia, which worsen the operator` functional state, affect the quality of work tasks performance, last a long time and require special correction and treatment.

The purpose of the paper is to develop a method for evaluating and predicting the operators` functional state based on a model for predicting changes of the VS state under the visual work, as well as to implement this method in clinical decision support system for analyze the SV states changes because of visual work.

Results. Two clusters have been identified according to the mechanisms of changes in the VS state due to visual work. A model for predicting these changes is developed based on a set of indicators of the SV functional state using the fuzzy clustering algorithm (c-means) and the fuzzy derivation system Sugeno. According to results of previous research and this forecast model, a method for assessing and forecasting the functional state of a operator and his visual system has been developed. The proposed method is implemented in clinical decision support system for analysis and prediction of changes of the operator’s VS state due to visual work.

Conclusions. Developed method and automated system allow to predict changes of VS state in the case of a given visual load, to compare the current functional state with the previous one, to obtain information about the effectiveness of the recommended preventive measures. Approbation of the developed system determined that the use of this method of operators` functional state assessment and prediction, as well as recommendations for individual correction of the existing state led to improving of visual function in 67% of patients, and reducing of overall complaints in 50%, visual complaints in 53%, eye complaints – in 40% of patients.

Key words: functional state of visual system, visual load, model for forecasting of VS state, asthenopia, fuzzy clustering, clinical decision support system

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REFERENCES

1 Kornyushina T.A. The role of accommodation in the occurrence of asthenopia. Biomechanics of the eye. 2007, Moscow, pp. 9-13. (in Russian)

2 Ovechkin I.G., Yudin V.E., Emelyanov G.A., Mironov A.V. A multidisciplinary approach to the correction of accommodation-refractive disorders in patients with visually-intense work. Ophthalmology. 2015, Vol. 12, no. 2, pp. 68-73. (in Russian)

3 Shapovalov S. L., Milyavskaya T. I., Ignatiev S. A. Accommodation of the eye and its disturbances. 2012, Moscow, 188 p. (in Russian)

4 Bali J., Navin N., Thakur B.R. Computer vision syndrome: A study of the knowledge, attitudes and practices in Indian Ophthalmologists. Indian J. Ophthalmol. 2007, Vol. 55, pp. 289-293.
https://doi.org/10.4103/0301-4738.33042

5 Rosenfield M. Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic Physiol. Opt. 2011, Vol. 31 (5), pp. 502-515.
https://doi.org/10.1111/j.1475-1313.2011.00834.x

6 Zadeh L.A. Biological applications of the theory of fuzzy sets and systems. The Proceedings of an International Symposium on Biocybernetics of the Central Nervous System. 1969, Boston, pp. 199-206.

7 Folkard S., Robertson K., Spencer M. A fatigue/Risk Index to assess work schedules. Somnologie. 2007, Vol. 11, pp. 177-185.
https://doi.org/10.1007/s11818-007-0308-6

8 Rutkovskaya D., Pilinsky M., Rutkovsky L. Neural networks, genetic algorithms and fuzzy systems. 2006, Moscow, 452 p. (in Russian)

9 Cheremukhina O. M. Mathematical modeling and prediction of the extent of internal twigs. Likarska sprava. 2011, no. 1/2, pp. 75-81. (in Ukrainian)

10 Zadeh L.A. Fuzzy logic and approximate reasoning. Synthese APRIL/MAY. 1975, Vol. 30, no 3/4, pp. 407-428.
https://doi.org/10.1007/BF00485052

11 Lisboa P.J., Taktak A.F.G. The use of artificial neural networks in decision support in cancer: a systematic review. Neural. Networks. 2006, Vol. 19, no 4, pp. 408-415.
https://doi.org/10.1016/j.neunet.2005.10.007

12 Kalnish V.V., Firsov A.G., Shvets A.V., Yeshenko A.I. Features of the classification of the state of a human operator by means of fuzzy logic. Kibernetika i vycislitel’naa tehnika. 2011, Iss. 166, pp. 55-67. (in Russian)

13 De Rivercourt M., Kuperus M.N., Post W.J., Mulder L.J.M. Cardiovascular and eye activity measures as indeces for momentary changes in mental effort during simulated flight. Ergonomics. 2008, Vol. 51, No 9, pp. 1295-1319.
https://doi.org/10.1080/00140130802120267

14 Vilela M. A. P., Castagno V. D., Meucci R. D., Fassa A. G. Asthenopia in schoolchildren. Clin. Ophthalmol. 2015, Vol. 9, pp. 1595-1603.
https://doi.org/10.2147/OPTH.S84976

15 Kochina M.L., Saykovskaya L.F., Yavorsky A.V., Lad S.N. Approaches to modeling the functional state of the visual system. Kibernetika i vycislitel’naa tehnika. 2009, Iss. 158, pp. 19-27. (in Russian)

16 Kochina M.L., Kalimanov V.G. Classification of lesions of the oculomotor muscles using the apparatus of fuzzy logic. Kibernetika i vycislitel’naa tehnika. 2011, Iss. 166, pp. 97-107. (in Russian)

17 Rykov S.O., Cheremukhina O.M. New information systems in ophthalmology. Philate reading: materials of scientific-practical conference and ophthalmologists with international participation. 2012, Odessa, pp. 334-335. (in Ukrainian)

18 Kochina M.L., Kozak L.M., Evtushenko A.S. Analysis of changes in factor structures of indicators of a person’s functional state with different types of visual load. Bulletin of problems in biology and medicine. 2013, Vol. 1, Iss. 1. pp.41-45. (in Russian)

19 Leonenkov A. V. Fuzzy modeling in MATLAB and fuzzyTECH. 2005, St. Petersburg, 736 p. (in Russian)

20 Yager R., Filev D. Essential of Fuzzy Modeling and Control. 1994, NY: JohnWilley&Sons, 388 p.

21 Odinets Yu.V., Kharchenko T.V., Trindyuk Yu.S. The use of fuzzy logic in the diagnosis of pyelonephritis in children. Actual problems of conventional medicine. 2011, Iss. 4 (36), Part 1, pp. 63-68. (in Russian)

22 Open source software for numerical computation. URL: http://www.scilab.org/

23 Fuzzy Logic Tool box. URL : http://atoms.scilab.org/ toolboxes/sciFLT/0.4.7

24 Nathan A. NET and COM: The Complete Interoperability Guide. 2002, Indianopolis: Sams Publishing, 1579 p.

25 Fuzzy Logic Library for Microsoft Net (fuzzynet). URL: https://sourceforge.net/projects/fuzzynet.

26 Microsoft Access 2016 Runtime. URL: https://www.microsoft.com/uk-ua/download/details.aspx?id=50040

27 Antomonov M.Yu. Methodology for the formation of complex indicators in environmental and hygienic research. Hygiene and sanitation. 1993, no 7, pp. 20-22. (in Russian)

28 Antomonov M.Yu. Mathematical processing and analysis of biomedical data, 2nd ed. 2018, Kyiv: MEC “Medinform”, 579 p. (in Russian)

Received 03.01.2020

Issue 2 (200), article 3

DOI:https://doi.org/10.15407/kvt200.02.041

Cybernetics and Computer Engineering, 2020, 2(200)

GRITSENKO V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and MES of Ukraine
e-mail: vig@irtc.org.ua

VOLKOV O.Ye., 
Senior Researcher of the Intelligent Control Department
e-mail: alexvolk@ukr.net

BOGACHUK Yu.P., PhD. (Engineering),
Leading Researcher of the Intelligent Control Department
e-mail: dep185@irtc.org.ua

GOSPODARCHUK O.Yu., 
Senior Researcher of the Intelligent Control Department
e-mail: dep185@irtc.org.ua

KOMAR M.M., 
Researcher of the Intelligent Control Department
e-mail: nickkomar08@gmail.com

SHEPETUKHA Yu.M., PhD. (Engineering),
Leading Researcher of the Intelligent Control Department
e-mail: dep185@irtc.org.ua

VOLOSHENIUK D.O., 
Researcher of the Intelligent Control Department
e-mail: p-h-o-e-n-i-x@ukr.net

International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

INTELLECTUAL CONTROL, LOCALIZATION AND MAPPING
IN GEOGRAPHIC INFORMATION SYSTEMS
BASED ON ANALYSIS OF VISUAL DATA

Introduction. Nowadays, geoinformation systems (GIS) are widely used in transport, construction, navigation, geology, geography, military affairs, topography, economics and more.

Problem Statement. Modern GIS publications highlight a number of pressing issues regarding the need to develop technologies and methods for the rapid formation of spatial-temporal geoinformation data bases and dynamic mapping images. The process of prompt formation of cartographic images of the area of unmanned aerial vehicles (UAV) flights in GIS databases is based on the simultaneous solution of two problems – determining the location of UAV in space, as well as the formation of a mapping image of the area under study.

 The purpose of the paper is to descript the method of topographic clustering of the obtained photographic images of UAV flights, which allows to combine visual images due to the semantic search of their topographic similarity, in order to realize the visual localization of UAV and high-precision layout of the mapping image of the navigation environment in the operational GIS database.

Materials and methods. The research conducted is based on the technologies of intelligent processing of large arrays of video and photo data, the theory of automatic control, methods of image processing and recognition based on descriptors of special points, methods of computer vision, as well as on methods and algorithms of own development, theory of navigation and dynamics of UAV flight.

Results. Procedures of topographic clustering of visual images obtained with UAV are developed, which are used for cognitive detection, description and matching among the characteristic features of the navigation environment.

Conclusions. The formation of a mapping image of the area of the navigation environment using the proposed method of topographic clustering of visual images achieved a decimeter accuracy in spatial coordinates, allowing visual localization and mapping with a high level of accuracy.

Keywords: unmanned aerial vehicle, geoinformation system, information technology, computer vision, intelligent control, cartographic image, aerial photography.

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REFERENCES

1 Artes T., Cencerrado A., Cortes A., Margalef T. Real-time genetic spatial optimization to improve forest fire spread forecasting in high-performance computing environments. International Journal of Geographical Information Science. 2016, Vol. 30, No 3, pp. 594-611.
https://doi.org/10.1080/13658816.2015.1085052

2 Li J., Bi Y., Lan M., Qin H., Shan M., Lin F., Chen B.M. Real-time Simultaneous Localization and Mapping for UAV: A Survey. Proc. of International micro air vehicle competition and conference. 2016, Beijing, China, 2016, pp. 237-242.

3 Kozub, A. M., Suvorova, N. O., & Chernyavsky, V. M. (2011). Analiz zasobiv zboru informatsiyi dlya heohrafichnykh informatsiynykh system. Systemy ozbroyennya i viys’kova tekhnika. 2011, No 3, pp. 42-47. (In Ukrainian)

4 Gonzales D., Harting S. Designing Unmanned Systems with Greater Autonomy: Using a Federated, Partially Open Systems Architecture Approach. Santa Monica, Calif: RAND, 2014, 96 p.

5 Agunbiade O., Zuva T., A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot. Preprints 2018. 2018050293 (doi: 10.20944/preprints201805.0293.v1).
https://doi.org/10.20944/preprints201805.0293.v1

6 Fuentes-Pacheco J., Ruiz-Ascencio J., Rendon-Mancha J.M. Visual simultaneous localization and mapping: a survey. Artificial Intelligence Review. 2015, Vol. 43, No1, pp. 55-81.
https://doi.org/10.1007/s10462-012-9365-8

7 Silpa C., Hartley R. Optimised KD-trees for fast image descriptor matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2008, pp. 1-8.

8 Dufournaud Y., Schmid C., Horaud R. Image matching with scale adjustment. Computer Vision Image Understanding. 2004, No 93(2), pp. 175-194.
https://doi.org/10.1016/j.cviu.2003.07.003

9 Zhang W., Kosecka J. Image based localization in urban environments. Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission. 2006, Chapel Hill, USA, pp. 33-40.
https://doi.org/10.1109/3DPVT.2006.80

10 Schubert J., Brynielsson J., Nilsson M., Svenmarck P. Artificial Intelligence for Decision Support in Command and Control Systems. Proceedings of the 23rd International Command and Control Research & Technology Symposium “Multi-Domain C2”. 2018, Playa Vista, California, USA, pp. 18-33.

11 Bradski G., Kaehler A. Learning OpenCV. O’Reilly Media, 2008, 576 p.

Received 27.02.2020

Issue 2 (200), article 2

DOI:https://doi.org/10.15407/kvt200.02.027

Cybernetics and Computer Engineering, 2020, 2(200)

YERMAKOVA I.I., DSc (Biology), Professor
Leading Researcher
e-mail: irena.yermakova@gmail.com

NIKOLAENKO A.Y., PhD (Engineering),
Researcher
e-mail: n_nastja@ukr.net

BOGATONKOVA A.I., PhD (Engineering),
Senior Researcher
e-mail: bogatonkova@gmail.com

HRYTSAIUK O.V.,
Junior Researcher
e-mail: olegva11@gmail.com

KRAVCHENKO P.M.,
Senior Engineer
e-mail: paul.kravchenko@gmail.com

International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, the Department of Complex Research of Information Technologies,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

INFORMATION TECHNOLOGY FOR PREDICTION
OF HUMAN STATE IN EXTREME ENVIRONMENTS

Introduction. Being in cold water refers to extreme effects. Due to its high thermal conductivity and heat capacity, water is an extreme factor for rapid cooling of the body. For the safe swimming and working of a man in cold water special protective equipment — wetsuits is used. The method of mathematical modeling makes it possible to study the processes of heat exchange between human and water environment, taking into account environmental conditions, level of physical activity and wetsuit characteristics.

The purpose of the paper is to develop information technology for evaluation and prediction of human thermophysiological state for safe staying in the water.  As a result computer module for influence of protective clothing on human thermal state has been developed.

Results. The information technology for prediction of human state in extreme conditions in water is proposed. The computer module for prediction and evaluation of human thermophysiological state in a wetsuit has been developed. This module is based on a complex of mathematical models of human thermoregulation in extreme environments. The adequacy of mathematical models is proved by comparing the modeling results with observations on people. This suggests that the information technology and computer module can be applied to perform theoretical and practical tasks related to human health in cold water.

With the help of the developed computer module modeling experiments of influence of the design of a wetsuit on the thermoregulation of person in water were held. Two wetsuits were researched: short sleeves and short trousers and long sleeves and long trousers. Were obtained the forecast and the analysis of thermophysiological state of swimming man, duration was one hour, speed was 1 m/s, temperature of water from 10 °C to 26 °C.

Conclusions. The information technology for predicting thermophysiological state of a man allows to investigate the influence of a protective suit of different design on the thermoregulation of a human body. It’s shown that the choice of wetsuit can be made only in combination with temperature of water and planned physical activity, otherwise mistakes can lead to a violation of thermal comfort in the case of human’ being in water.

Keywords: model of human thermoregulation, information technology, computer module, extreme conditions, water environment, wetsuit. 

Download full text! (ua)

REFERENCES

1 American Red Cross. Swimming and Water Safety, 3rd ed. 2009, Chapter 3, pp. 43-64. ISBN 978-1-58480-446-8

2 Tipton M.J., Brooks C.J. The Dangers of Sudden Immersion in Cold Water. Survival at Sea for Mariners, Aviators and Search and Rescue Personnel. Brussels, Belgium, 2008, Chapter 3, pp. 1-10. ISBN 978-92-837-0084.

3 Tipton M., Bradford C. Moving in extreme environments: open water swimming in cold and warm water. Extreme physiology & medicine. 2014, Vol. 3, No 1, pp. 12.
https://doi.org/10.1186/2046-7648-3-12

4 Yermakova I., Montgomery L. Predictive Simulation of Physiological Responses for Swimmers in Cold Water. Proceedings of the 38th International scientific conference electronics and nanotechnology. Institute of Electrical and Electronics Engineers, Kyiv, Ukraine, 2018, pp. 292-297.
https://doi.org/10.1109/ELNANO.2018.8477523

5 Gavrilova O.E., Nikitina L.L. The choice of constructive decisions and polymeric materials for clothing used in the water. Vestnik of the Kazan Technological University. 2015, Vol. 18, No 13, pp. 153-155. (in Russian)

6 Gritsenko V., Yermakova I., Dukchnovskaya K., Tadejeva J. Dynamic models and information technologies for prediction of human vital functions. Control Systems and Computers. 2004, Vol. 2, pp. 56-60. (in Russian)

7 Enescu D. Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions. Energies. 2019, Vol. 12, Iss. 5, No 841, pp. 1-43.
https://doi.org/10.3390/en12050841

8 Parsons K. Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort and performance. CRC press, 2002, 2nd Ed, 560 p.

9 Montgomery L.D. A model of heat transfer in immersed man. Annals of biomedical engineering. 1974, Vol. 2, No 1, pp. 19-46.
https://doi.org/10.1007/BF02368084

10 Miller N.C., Seagrave R.C. A model of human thermoregulation during water immersion. Computers in biology and medicine. 1974, Vol. 4, No 2, pp. 165-182.
https://doi.org/10.1016/0010-4825(74)90018-3

11 Tikuisis P., Gonzalez R.R., Pandolf K.B. Thermoregulatory model for immersion of humans in cold water. Journal of Applied Physiology. 1988, Vol. 64, No 2, pp. 719-727.
https://doi.org/10.1152/jappl.1988.64.2.719

12 Tikuisis P., Gonzalez R.R., Pandolf K.B. Prediction of human thermoregulatory responses and endurance time in water at 20 and 24 degrees C. Aviation, space, and environmental medicine. 1988, Vol. 59, No 8, pp. 742-748.

13 Yermakova I., Solopchuk Y. Computer model of human thermoregulation during water immersion. Kibernetika i vycislitelnaa tehnika. 2013, Vol. 172, pp. 39-48. (in Russian).

14 Wakabayashi H., Hanai A., Yokoyama S., Nomura T. Thermal insulation and body temperature wearing a thermal swimsuit during water immersion. Journal of physiological anthropology. 2006, Vol. 25, No 5, pp. 331-338.
https://doi.org/10.2114/jpa2.25.331

15 Toner M. M., Sawka M. N., Holden W. L., Pandolf K. B. Comparison of thermal responses between rest and leg exercise in water. Journal of Applied Physiology. 1985, Vol. 59, No 1, pp. 248-253.
https://doi.org/10.1152/jappl.1985.59.1.248

16 Yermakova I., Nikolaienko A., Tadeieva J., Montgomery L. Protective effect of wetsuits for swimmers in cold water: modelling results. Proceedings of the 7th European conference on protective clothing (ECPC 2016) (23th – 25th of May, Izmir). Izmir, Turkey. Izmir, Turkey, 2016, pp. 57-58.

Received 13.02.2020

Issue 2 (200), article 1

DOI:https://doi.org/10.15407/kvt200.02.005

Cybernetics and Computer Engineering, 2020, 2(200)

CHABANIUK V.S.,1,2 PhD (Phys.&Math.),
Senior Researcher of the Cartography Department,
Institute of Geography, National Academy of Sciences of Ukraine,
Director of “Intelligence Systems-GEO” LLC,
email: chab3@i.ua, chab@isgeo.kiev.ua

KOLIMASOV I.M.,2
Head of Production of “Intelligence systems-GEO” LLC,
email: kolimasov@ukr.net

1 Institute of Geography, National Academy of Sciences of Ukraine
44, Volodymyrsk str., 01030, Kyiv, Ukraine
2“Intelligence systems-GEO” LLC,
6/44, Mikilsko-Slobidska Str., 02002, Kyiv, Ukraine.

ANALYSIS OF THE PRACTICAL USE OF GEOINFORMATION SYSTEMS FOR TERRITORIAL MANAGEMENT AND DETERMINATION OF THEIR CRITICAL PROPERTIES

Introduction. The practical experience of creation, implementation and operation of geoinformation system (GIS) for territory management allows to identify their critically important properties. GIS with critical properties do not fit the conventional definition because they are more advanced systems. Special attention to critical properties helps to reduce the risks involved in the development and implementation of such GIS, as well as to increase the effectiveness of their use for territory management.

The purpose of the paper is to analyze the use of GIS developed by the authors for the management of large territories and to determine their main critical properties. Critical GIS properties are being sought to manage territories that: 1) repeat for all such GIS, 2) differentiate them from conventional GIS, 3) must be taken into account when creating new GIS.

Results. The critical properties that are mandatory also for modern GIS for territory management are as follow: 1) the availability of education-scientific, production and management components, 2) the availability of a relatively independent atlas solution, 3) the obligation to use portals, 4) the need to supplement the territory modeling of their metamodeling.

Conclusions. Abductive inferences after analyzing the practical experience of creation, implemention and operation of GIS for territory management allow to confirm the presence of critical properties of GIS.  Without any such property, we can speak about a corresponding critical shortcoming of the GIS project, and this project is likely to be a failure.

Keywords: geoinformation system, territory management, spatial data, abduction, critical property

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REFERENCES

1. URL: http://ucgis.org/roger-tomlinson (Last accessed: 28.01.2020).

2. Svitlychny O.O., Plotnitsky S.V. Fundamentals of geoinformatics: Textbook. Sumy, 2006, 295 p. (in Ukrainian)

3. Kapralov E.G., Koshkarev A.V., Tikunov V.S. Fundamentals of geoinformatics. In 2 b. Textbook for stud. univ. M. Publishing Center “Academy”, 2004. Book 1. 352 p. Book 2. 400 p. (in Russian)

4. Star J., Estes J. Geographic Information Systems. An Introduction. Prentice Hall, 1990. 303 p.

5. URL: https://ru.wikipedia.org/wiki/Геоинформационная_система (Last accessed: 25.01.2020)

6. URL: https://uk.wikipedia.org/wiki/Геоінформаційна_система (Last accessed: 25.01. 2020)

7. Cowen David J. GIS versus CAD versus DBMS: What Are the Differences? Photo-grammetric Engineering and Remote Sensing. 1998, Vol. 54, No 11, pp. 1551–1555.

8. Rudenko L.G., Chabanyuk V.S. Foundations of the conception of the multigoal GIS of Ukraine. Ukrainian Geographical Journal. 1994, No 3, pp. 22–34. (in Ukrainian)

9. Miller Harvey J. The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science. 2010, Vol. 50, No 1, pp. 181–201.
https://doi.org/10.1111/j.1467-9787.2009.00641.x

10. Sventek Yu.V. Theoretical and practical aspects of modern cartography. Editorial URSS. 1999, 76 p. (in Russian)

11. Rumbaugh James, Jacobson Ivar, Booch Grady. The Unified Modeling Language Reference Manual. Addison-Wesley, 2005, 2nd Ed. 721 p.

12. Karpinsky Yu.O., Lyashchenko A.A. Geographic information: the reference model is the first fundamental national standard, harmonized with international standards of ISO 19100 / Lviv. Modern achievements of geodetic science and production. Collection of scientific works of the Western Geodetic Society. Publishing House of Lviv Polytechnic National University. 2010, Issue I (19), pp. 198–203. (in Ukrainian)

13. Prister B.S., Tabatchnyi L.J., Chabanyuk V.S. Liquidation of the Chernobyl After-Effects and GIS. Proceedings. Canadian GIS Conference (6th – 10th of June, Ottawa). Ottawa, Canada, 1994, pp. 1025–1035.

14. Palko S., Glieca M., Dombrowski A. Geographic information systems for the Chernobyl makers in Ukraine. One decade after Chernobyl: consequences of the accident. International Conference (8th – 12th of Apr., Vienna). Vienna, Austria, 1996. Poster presentations, Vol. 2, pp. 107–113.

15. URL: http://radatlas.isgeo.com.ua (Last accessed: 26.01.2020)

16. Pretzsch G., Lhomme V., Selesnew A., Roloff R., Artmann A., Berberich G. The French-German Initiative for Chornobyl. Programme 1: Safety State of the Sarcophagus. GRS/IRSN – 3. 2005, ISBN 3-931995-83-6, 68 p.

17. Deville-Cavelin G., Biesold H., Brun-Yaba C., Artmann A. The French-German Initiative for Chornobyl. Programme 2: Study of the Radioecological Consequences. Synthesis Report. GRS/IRSN – 4.1, 2007, ISBN 3-931995-95-X. 104 p.

18.  Tirmarche M. The French-German Initiative for Chernobyl. Programme 3: Study of the Health Effects. GRS/IRSN – 5. 2006, ISBN 3-931995-85-2, 64 p.

19. Falkenberg E.D., Lindgreen P., Eds. Information System Concepts: An In-depth Analysis. Amsterdam, 1989. 357 p.

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Issue 2 (200)

DOI:https://doi.org/10.15407/kvt200.02

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TABLE OF CONTENTS:

Informatics and Information Technologies:

Chabaniuk V.S., Kolimasov I.M.
Analysis of the practical use of geoinformation systems for territorial management and determination of their critical properties

Yermakova I.I., Nikolaenko A.Y., Bogatonkova A.I., Hrytsaiuk O.V., Kravchenko P.M.
Information Technology for Prediction of Human State in Extreme Environments

Intelligent Control and Systems:

Gritsenko V.I., Volkov O.Ye., Bogachuk Yu.P., Gospodarchuk O.Yu., Komar M.M., Shepetukha Yu.M., Volosheniuk D.O.
Intellectual Control, Localization and Mapping in Geographic Information Systems Based on Analysis of Visual Data

Medical and Biological Cybernetics:

Kochina M.L., Kozak L.M., Yavorsky O.V., Firsov O.G., Yevtushenko A.S.
Model and Method for Evaluation and Forecast of the Changes of Visual System Functional State in Consequence of Visual Work

Belov V.M., Hontar T.M., Kobzar T.A., Kozlovska V.O.
Health Self-Esteem Information Technology for Rehabilitation of Post-Traumatic Stress Disorder

Issue 1 (199), article 5

DOI:https://doi.org/10.15407/kvt199.01.085

Cybernetics and Computer Engineering, 2020, 1(199)

Azarkhov O.Yu.1, DSc. (Medicine),
Head of the Biomedical Engineering Department
e-mail: alexazarhov@gmail.com

Chernyshova T.A.2, Physician,
e-mail: tetyana.che@gmail.com

1Pryazovsky State Technical University of the Ministry of Education and Science of Ukraine,
7, University st., Mariupol, 87555, Ukraine

2Aviation Medical Center of the National Aviation University,
1, Komarova av., Kyiv, 03058, Ukraine

APPLICATION OF INFORMATION TECHNOLOGY FOR DETERMINATION OF CIRCULATING TUMOR CELLS TO DIAGNOSTICS OF MALIGNANT TUMOR DISEASES

Introduction. The study of the possibility of using the circulating tumor cells (CTC) definition in the patients` blood with different localization of malignant tumors as a diagnostic criterion and the criterion of the effectiveness of specific treatment tactics is one of the topical issues in modern oncology.

The purpose of the paper is to analyze the results of using the developed information technology for identification of circulating tumor cells for the study of blood samples of patients in order to confirm or reject the initial diagnosis of cancer of different localization.

Results. Our information technology is based on the use of an advanced method of isolation of intact circulating cells, the difference of which is to supplement the structure of the basic ISET method (Isolation by Sizе of Tumor Cells) with new modes: 100% sealing chamber with hemolysate and providing it with the necessary and constant pressure during the filtration process by introducing a negative pressure gauge, as well as the mode of three-level filtering of the CTC on consecutive polycarbonate membranes with micropore diameters of 8 μm, 5 μm and 3 μm. To assess the malignancy of selected cells, the information technology used the method of determining the CTC according to the set of criteria, formed databases with created template CTC masks and control templates in the automated mode. Blood samples from patients were tested using IT. Taking into account each step of the technique (using different filters), analysis of the results showed that of the total proportion of samples, which additionally detected the CTC using not only an 8 μm filter, but also filters 5 μm and 3 μm, was 20.66 %.

Conclusions. The use of information technology to identify circulating tumor cells improves the efficiency of detecting these cells by reducing the testing time and expanding the range of research due to the ability to detect cells of small size. Improvement of IT by supplementing the knowledge base (complex of template mask masks and relevant expert findings) makes it possible to apply it in screening of patients’ blood, including at the preclinical stage of the examination.

Keywords: information technology, circulating tumor cells, method of isolation of circulating tumor cells, automated system, screening of patients’ blood.

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REFERENCES

1. Goeminne J.C., Guillaume T., Symann M. Pitfalls in the de- tection of disseminated non-hematological tumor cells. Ann Oncol. 2000, no. 11, pp.785-792.
https://doi.org/10.1023/A:1008398228018

2. Pantel K., Woelfle U. Detection and molecular characterisa- tion of disseminated tumour cells: implications for anticancer therapy. Biochim Biophys Acta. 2005, no. 1756, pp. 53-64.
https://doi.org/10.1016/j.bbcan.2005.07.002

3. Smerage J.B., Hayes D.F. The measurement and therapeutic implications of circulating tumour cells in breast cancer. Br J Cancer. 2006, no. 94, pp. 8-12.
https://doi.org/10.1038/sj.bjc.6602871

4. Grudinskaya T.V., Kovalev A.A., Kovalev K.A., Kuznetsova T.P. Heterogeneity of circulating tumor cells. Oncology. 2012, Vol. 14, no. 2, pp. 126-129. (in Russian)

5. Keith O.I., Novikova I.A., Selyutina O.N., Duritsky M.N., Dontsov V.A., Chernikova E.N., Samaneva N.Yu., Nistratova O.V. Research of the CTC level in epitital tumors of different localizations. International Journal of Applied and Fundamental Research. 2015, no.12 (Part 5), pp. 817-820. (in Russian).

6. Bzhadug O.B. Grivtsova L.Y., Tupitsh N.N., Tyulandin S.A. Circulating tumor cells in the blood of patients with locally distributed and disseminated breast cancer. RONC Newsletter. 2007, Vol. 18, no. 3, pp. 19-22. (in Russian)

7. Balic M., Dandachi N., Hofmann G. Comparison of two methods for enumerating circulating tumor cells in carcinoma patients. Clin Cytom. 2005, no.68, pp. 25-30.
https://doi.org/10.1002/cyto.b.20065

8. Ashworth T. A case of cancer in which cells similar to those in the tumors were seen in the blood after death. Aust Med J. 1869, no. 14, pp. 146-149.

9. Goeminne J.C., Guillaume T., Symann M. Pitfalls in the detection of disseminated non-hematological tumor cells. Ann Oncol. 2000, no. 11, pp. 785-92.
https://doi.org/10.1023/A:1008398228018

10. Christiansen J.J., Rajasekaran A.K. Reassessing epithelial to mesenchymal transition as a prerequisite for carcinoma invasion and metastasis. Cancer Res. 2006, no. 66, pp. 8319-26.
https://doi.org/10.1158/0008-5472.CAN-06-0410

11. Ring I, Smith E, Dowsett M. Circulating tumour cells in breast cancer. Lancet Oncol. 2004, no. 5, pp. 79-88.
https://doi.org/10.1016/S1470-2045(04)01381-6

12. Andreopoulou E., Yang L.-Y., Rangel K., Reuben J., Hsu L., Krishnamurthy S., Valero V. Comparison of assay methods for detection of circulating tumor cells in metastatic breast cancer: AdnaGen AdnaTest BreastCancer Select/Detect™ versus Yeridex CellSearch™ system. Int. journal of cancer. 2012, Vol. 130, no.7, pp. 1590-1597.
https://doi.org/10.1002/ijc.26111

13. Kagan M., Howard D., Bendele Т., Mayes J., Silvia J., Repollet M., Doyle J. A Sample Preparation and Analysis System for Identification of Circulating Tumor Cells. Journal of Clinical Ligand Assay. 2002, Vol. 25, no. 1, pp. 104-110.

14. Nezos A., Pissimisis N., Lembessis P., Sourla A., Dimopoulos P., Dimopoulos Т., Tzelepis K. Detection of circulating tumor cells in bladder cancer patients. Cancer treatment reviews. 2009, Vol. 35, no.3, pp. 272-279.
https://doi.org/10.1016/j.ctrv.2008.11.003

15. Ignatiadis M., Kallergi G., Ntoulia M., Perraki M., Apostolaki S., Kafousi M., Chlouverakis G. Prognostic value of the molecular detection of circulating tumor cells using a multimarker reverse transcription-PCR assay for cytokeratin 19, mammaglobin A, and HER2 in early breast cancer. Clinical cancer research: an official journal of the American Association for Cancer Research. 2008, Vol. 14, no9, pp. 2593-2600.
https://doi.org/10.1158/1078-0432.CCR-07-4758

16. Van der Auwera I., Peeters D., Benoy I. Circulating tumour cell detection: a direct comparison between the CellSearch System, the AdnaTest and CK-19/mammaglobin RT-PCR in patients with metastatic breast cancer. British journal of cancer. 2010, Vol.102, no. 2, pp. 276-284.
https://doi.org/10.1038/sj.bjc.6605472

17. Nagrath S., Sequist L., Maheswaran S., Bell D., Irimia D., Ulkus L., Smith M. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature. 2007, Vol. 450, no.7173, pp. 1235-1239.
https://doi.org/10.1038/nature06385

18. Chen L., Bode A. M., Dong Z. Circulating Tumor Cells: Moving Biological Insights into Detection. Theranostics. 2017.
https://doi.org/10.7150/thno.18588

19. Laget S., Broncy L., Hormigos K., Dhingra D.M., BenMohamed F., Capiod T. Technical Insights into Highly Sensitive Isolation and Molecular Characterization of Fixed and Live Circulating Tumor Cells for Early Detection of Tumor Invasion. PLOS ONE. 2017, 12 (1): e0169427. URL: https://doi.org/10.1371/journal.pone.0169427
https://doi.org/10.1371/journal.pone.0169427

20. Zlepko S.M., Chernyshova T.A., Mayevsky A.Ye., Krivonosov V.Ye., Azarkhov A.Yu. Information technology for the determination of circulating tumor cells in human blood. Kibernetika i vyčislitel’naâ tehnika. 2018, no. 2 (192), pp. 84-98. (in Ukrainian).
https://doi.org/10.15407/kvt192.02.084

21. Vona G., Sabile A., Louha M., Sitruk V., Romana S., Schutze K., Capron F. Isolation by size of epithelial tumor cells: a new method for the immunomorphological and molecular characterization of circulatingtumor cells. The American journal of pathology. 2000, Vol. 156, no. 1, pp. 57-63.
https://doi.org/10.1016/S0002-9440(10)64706-2

22. Paterlini-Bréchot P., Benali-Furet N.L. Circulating tumor cells (CTC) detection : Clinical impact and future directions. Cancer Letter. 2007, no. 253, pp. 180-204.
https://doi.org/10.1016/j.canlet.2006.12.014

23. Ismailova G., Laget S., Paterlini-Brechot P. Diagnosis of circulating tumor cells using ISET technology and their molecular characteristics for fluid biopsy. Clinical medicine of Kazakhstan. 2015, no. 1 (35), pp. 15-20. (in Russian).
https://doi.org/10.1007/978-3-642-27841-9_1182-2

24. Pavlov S.V., Kozhemyako V.P., Burdenyuk I.I., Rami Rebhi Hamdi. Opto-electronic technologies of biomedical image analysis. Vinnitsa, 2011, 166 p. (in Ukrainian).

25. Zlepko S.M., Pavlov S.V., Koval L.G., Timchyk I.S. Fundamentals of biomedical radioelectronic apparatus. Vinnitsa, 2011, 133 p. (in Ukrainian).

26. Chernyshova T.A. Criteria and Method for Detection of Circulating Tumor Cells. Cybernetics and computer engineering. 2019, no.1 (195), pp.85-98.
https://doi.org/10.15407/kvt195.01.082

27. Pat. 127486 UA, MPK C12M 3/06, G01N 33/574, G01N 33/49. Device for detecting circulating tumor cells in the blood / S.M. Zlepko, V.E. Krivonosov, S.V. Timchyk, T.A. Chernyshova, O.S. Zlepko, O.Yu. Azarkhov, V.S. Pavlov, V.V. Krivonosov (Ukraine). 2018 00060; claimed 02.01.2018; publ. 08/10/2018, Bul. № 15. – 7 p. (in Ukrainian).

Recieved 11.12.2019

Issue 1 (199), article 4

DOI:https://doi.org/10.15407/kvt199.01.059

Cybernetics and Computer Engineering, 2020, 1(199)

GRITSENKO V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine
e-mail: vig@irtc.org.ua

FAINZILBERG L.S., DSc. (Engineering), Professor,
Chief Researcher of the Department of Intelligent Automatic Systems
e-mail: fainzilberg@gmail.com

International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of  Ukraine and of Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

CURRENT STATE AND PROSPECTS FOR THE DEVELOPMENT OF DIGITAL MEDICINE

Introduction. According to the definition of the International Society of Digital Medicine, digital medicine is a field of science in which scientists strive to explain previously incomprehensible pathophysiological phenomena in the human body and to explore new medical procedures using modern digital technologies to improve the quality of human life.

The purpose of the paper is to provide brief information about the current state and prospects for the development of digital medicine.

Methods. The analysis of the main directions of digital medicine is done. Basic definitions of the concepts “Intelligent IT signal processing” and “Effective computational procedure” are formulated. The role of intelligent IT in digital medicine is demonstrated on the example of fasegraphy method.

Results. Existing methods and means of digital medicine are used for diagnosis, treatment, rehabilitation, as well as to restore the lost functions of the patient (vision, hearing, movement). Such technologies make it possible not only to free medical workers from solving routine tasks, but also to increase the efficiency of performing surgical operations, radiation therapy and a number of other tasks of practical medicine. Unlike traditional IT, based on procedures for processing numerical data, intelligent IT operate with generalized concepts (images) that provide more complete information about the external environment, and the analysis of such images generates a holistic picture of the phenomena studied. Within the framework of the algorithmic approach, the construction of intelligent IT for solving the problems of digital medicine requires the active participation of a technology developer, who, using his natural intelligence, creates effective procedures for extracting diagnostic information from real data under disturbances.

Conclusions. Intelligent IT with the properties of natural intelligence (adaptation, generalization, learning, etc.) play an important role in expanding the functional capabilities and increasing the effectiveness of digital medicine.

Keywords: digital medicine, intelligent IT, efficient computing procedures.

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REFERENCES

1. Amosov N.M. Modeling complex systems. Kiev: Naukova Dumka, 1968. 87 p. (In Russian)

2. Anokhin P. K. Theory of a functional system. Advances in physiological sciences. 1970. V. 1, no. 1, pp. 19-54. (In Russian)

3. Grodins F.S. Control Theory and Biological Systems. New York: Columbia University Press, 1963. 179 p.

4. Gritsenko V.I. Digital medicine and information technology – cornerstones of healthcare for the future. Bulletin of NAS of Ukraine. 2016, no 5, pp. 41-43. (In Ukrainian)

5. Gritsenko V.I., Fainzilberg L.S. Personalized digital medicine – a step towards health. Bulletin of NAS of Ukraine. 2012, no 8, pp. 62-70. (In Ukrainian).

6. Gritsenko V.I., Fainzilberg L.S. Information technology FASEGRAPH® for the integrated assessment of the state of the cardiovascular system according by the phase portrait of the electrocardiogram. Doctor and information technology. 2013, no. 3, pp. 52-63. (In Russian).

7. Zhang S., Liao R., Alpert J.S., Kong J., Spetzger U., Milia P., et al. Digital Medicine: Emergence, Definition, Scope and Future. Digit Medicine. 2018, no. 4, pp. 1-4. DOI: 10.4103/digm.digm_9_18.
https://doi.org/10.4103/digm.digm_9_18

8. Kwakkel G., Kollen B., Lindeman E.. Understanding the Pattern of Functional Recovery after Stroke: Facts and Theories. Restor Neurol Neurosci. 2004, no. 22, pp. 281-299.

9. Lo A.C., Guarino P.D., Richards L.G., Haselkorn J.K., Wittenberg G.F., Federman D.G. et al. Robot Assisted Therapy for Long Term upper Limb Impairment after Stroke. The New England Journal of Medicine. 2010, Iss. 362, pp. 1772-1783. DOI: 10.1056/NEJMoa0911341.
https://doi.org/10.1056/NEJMoa0911341

10. Vovk M.I. Galyan., E.B., Kutsyak A.A., Lauta A.D. Formation of an individual complex of control actions for the rehabilitation of movements and speech after a stroke. Kibernetika i vyčislitel’naâ tehnika. 2018, no. 3, pp. 43-63. (In Russian). DOI: https://doi.org/10.15407/kvt192.03.043.
https://doi.org/10.15407/kvt192.03.043

11. Milia P., De Salvo F., Caserio M., Cope T., Weber P., Santella C. et al. Neurorehabilitation in Paraplegic Ptients with an Active Powered Exoskeleton (Ekso). Digital Medicine. 2016, no. 2, pp. 163-168. DOI: 10.4103/digm.digm_51_16.
https://doi.org/10.4103/digm.digm_51_16

12. Michaud F., Salter T., Duquette A., Laplante J. Perspectives on Mobile Robots as Tools for Child Development and Pediatric Rehabilitation. Assistive Technology. 2007. Vol. 19, no. 1, pp. 21-36. https://doi.org/10.1080/10400435.2007.10131863
https://doi.org/10.1080/10400435.2007.10131863

13. Sukhoruchkina O.N., Progonny N.V., Voronov M.A. Interpretation and use of measurements of a rangefinder sensor in control tasks of an autonomous mobile robot. Upravlâûŝie sistemy i mašiny. 2017, no. 1, pp. 26-34. (In Russian).
https://doi.org/10.15407/usim.2017.01.026

14. Kraevsky S.V., Rogatkin D.A. Medical robotics: the first steps of medical robots. Technologies of living systems. 2010. Vol. 7, no. 4, pp. 3-14. (In Russian).

15. Okie S. Robots Make the Rounds to Ease Hospitals’ Costs. The Washington Post. 2002, no 3, p. A3.

16. Tarasova L. Da Vinci in tandem with the surgeon. Medical Herald. 2008, no. 8, pp. 435. (In Russian).

17. Avrunin O.G., Semenets V.V. To the question of determining the force characteristics of a field in magnetic stereotaxis systems. Radio engineering. 2001, no. 117, pp.121-124. (In Russian).

18. Wang Y., Butner S.E., Darzi A. The Developing Market for Medical Robotics. Proc. IEEE, Special issue “Medical Robotics”. 2006. Vol. 94, no. 9, pp.1763-1770. DOI: 10.1109/JPROC.2006.880711.
https://doi.org/10.1109/JPROC.2006.880711

19. Donnet A., Valade D., Régis J. Gamma Knife Treatment for Refractory Cluster Headache: Prospective Open Trial. Journal of Neurology, Neurosurgery and Psychiatry. 2005, no. 2, pp. 218-221. DOI: 10.1136/jnnp.2004.041202.
https://doi.org/10.1136/jnnp.2004.041202

20. Simroid: Dentistry in the uncanny valley. – http://pinktentacle.com/2007/11/simroid-dentistry-in-the-uncanny-valley-video.

21. SimMan 3G. Patient Simulator Gets a Serious Upgrade. MedGadget internet journal of emerging medical technologies. (Год, № ????)

22. Fainzilberg L.S., Pomorskaya D.V. Information technology teaching the methods for the organism recovery at home conditions. Upravlâûŝie sistemy i mašiny. 2018, no 1, pp. 87-96. DOI: https://doi.org/10.15407/usim.2018.01.087.
https://doi.org/10.15407/usim.2018.01.087

23. Shaw D. Overview of Telehealth and Its Application to Cardiopulmonary Physical Therapy. Cardiopulmonary Physical Therapy Journal. 2009. Vol. 20, no. 2, pp. 13-18.
https://doi.org/10.1097/01823246-200920020-00003

24. Telemedicine yesterday, today, tomorrow. Electronics: Science, Technology, Business. 2000, no. 2, pp. 62-65.

25. Digital medicine in Ukraine: overview of services – https://ain.ua/ 2018/12/14/cifrovaya- medicina-v-ukraine-obzor-servisov/

26. Fainsilberg L.S. Digital medicine and intellectual information technology. Proc. of the First International Scientific and Practical Conference “Information Systems and Technologies in Medicine” (ISM-2018). Kharkiv: KNURE, Madrid Printing House, 2018, pp. 21-23.

27. Empowering Heroes, Transforming Health. URL: https://www.ibm.com/watson/health/

28. IBM Healthcare Technologies (In Russian). URL: http://cognitive.rbc.ru/health-tech.

29. Ambulatory Cardiac Monitoring: Avoiding Maturity through Technological Advancement. Market Engineering Research. Frost & Sullivan, Meriland. 2008, no. 9, pp. 325.

30. Turing A.M. On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society. 1937. Vol. 2, Iss. 42, pp. 230-265.
https://doi.org/10.1112/plms/s2-42.1.230

31. McCulloch W.S., Pitts W. A Logical Calculus of the Ideas Immanent in Nervous Activity Bulletin of Marhematicak Biophysics. 1943. V. 5, pp. 115-143.
https://doi.org/10.1007/BF02478259

32. Nils Aall Barricelli N.A. Numerical Testing of Evolution Theories. Acta Biotheoretica. 1962. Vol. 16. Iss. 1-2, pp. 69-98.
https://doi.org/10.1007/BF01556771

33. Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science. Edited by Mihail C. Roco and William Sims Bainbridge. Dordrecht: Kluwer Academic Publishers (Springer), 2003. 482 p.

34. Gritsenko V.I. Intellectualization of information technologies. Science and Technology. Kiev: V.M. Glushkov Institute of Cybernetics. NAS of Ukraine, 1992. pp. 4-9. (In Ukrainian).

35. Fainsilberg L.S. Intellectual opportunities and prospects for the development of fasegraphy – the information technology for processing signals of complex shape. Kibernetika i vyčislitel’naâ tehnika. 2016. Iss. 186, pp. 56-77. (In Russian)
https://doi.org/10.15407/kvt186.04.056

36. Fainzilberg L.S. Interactive synthesis of information technologies for signal processing with localized information. Kibernetika i vyčislitel’naâ tehnika. 2017, Iss. 1 (187), pp. 11-29. (In Russian). DOI: doi.org/10.15407/kvt187.01.011
https://doi.org/10.15407/kvt187.01.011

37. Fainsilberg L.S. Computer diagnostics by phase portrait of an electrocardiogram. Kiev: Osvita of Ukraine, 2013. 191 p. (In Russian).

38. Fainsilberg L.S. Basics of fasegraphy. Kiev: Osvita of Ukraine, 2017. 264 p. (In Russian).

39. Dyachuk D.D., Gritsenko V.I., Fainzilberg L.S., et al. The use of the method of fasegraphy in the screening of coronary artery disease. Methodological Recommendations of the Ministry of Health of Ukraine № 163.16 / 13.17. Kyiv: Ukrainian Center for Scientific Medical Information and Patent Licensing, 2017. 32 p. (In Ukrainian).

40. Fainzilberg L.S. Intelligent information technology for signal processing with localized information. Artificial Intelligence. 2018, no. 2, pp. 144-153. (In Russian).

41. Dyachuk D.D., Kravchenko A.N., Fainzilberg L.S., Stanislavskaya S.S., Korchinskaya Z.A., Orikhovskaya K.B., Pasko V.S., Mikhalev K.A. Screening of myocardial ischemia by the method of assessing the phase of repolarization. Ukrainian Cardiology Journal. 2016, no. 6, pp. 82-89. (In Russian).

42. Maydannik V.G., Khaitovich N.V., Fainzilberg L.S., Stepanov V.A., Vladimirova A.A., Misyura L.I. Symmetry of the T wave on the electrocardiogram as a marker of cardiometabolic risk in schoolchildren. International Journal of Pediatrics, Obstetrics and Gynecology. 2013. Vol. 4, no. 3,. pp. 35-39. (In Russian).

43. Morozik A.A. Fainzilberg L.S. The diagnostic significance of the combined analysis of the electrocardiogram on the phase plane and heart rate variability in children with diabetic cardiopathy. International Journal of Pediatrics, Obstetrics and Gynecology. 2015. Vol. 7, no.1, pp. 11-17. (In Russian).

44. Morozik A.O., Maidannik V.G., Fainzilberg L.S. Informative properties of indicators of the phase of repolarization of single-channel electrocardiogram in children with diabetic cardiomyopathy. Endocrinology. 2018. Vol. 23, no. 1, pp. 74-82. (In Ukrainian).

45. Maydannik V.G., Fainzilberg L.S., Dukkart K.B., Morozik A.O., Kondratyuk A.S. Regulatory patterns in assessing the functional state of the body of children and adolescents with type 1 diabetes. Ukrainian magazine of child endocrinologist. 2018, no. 1, pp. 51-60. (In Russian)

46. Maydannik V.G., Fainzilberg L.S., Dukkart K.B., Morozik A.A., Kondratyuk A.S. A new approach to the analysis of the functional state of the body of children and adolescents with type 1 diabetes. Modern pediatrics. 2018, no. 2 (90), pp. 37-46. (In Russian).
https://doi.org/10.15574/SP.2018.90.37

47. Pavlichenko P.P. The influence of game load on the functional state of professional football players. Light Medicine and Biology. 2015, no. 1 (48), pp. 49-54. (In Russian).

48. Fainzilberg L.S. New Opportunities of Phasegraphy in Medical Practice. Science and Innovation. 2017. Vol. 13. Iss. 3, pp. 37-50. DOI: 10.15407/scine13.03.037.
https://doi.org/10.15407/scine13.03.037

49. Fainzilberg L.S. Evaluation of the effectiveness of the application of information technology FASEGRAPH® according to independent studies. Upravlâûŝie sistemy i mašiny. 2014, no. 2, pp. 84-92. (In Russian).

50. Fainzilberg L.S. The technology for constructing a telemedicine system based on a generative model of generating an artificial ECG of a realistic form. Clinical computer science and telemedicine. 2012. Vol.8. Iss. 9, pp. 89-98. (In Russian).

51. Fainzilberg L.S., Soroka T.V. Development of a telemedicine system for remote monitoring of cardiac activity based on the phasegraphic method. East European Journal of Advanced Technology. 2015, no. 6/9 (78), pp. 37-46. (In Russian). DOI: 10.15587/1729-4061.2015.55004.
https://doi.org/10.15587/1729-4061.2015.55004

52. Fainzilberg L.S., Soroka T.V. Mobile applications for the virtual interaction of a doctor and a patient with remote monitoring of cardiac activity. Kibernetika i vyčislitel’naâ tehnika. 2016. Iss. 184, pp. 8-24. (In Russian).
https://doi.org/10.15407/kvt184.02.008

53. Fainzilberg L.S., Potapova T.P. Computer Analysis and Recognition of Cognitive Phase Space Electro-Сardio Graphic Image // Proceeding of the 6th International Conference On Computer analysis of Images and Patterns (CAIP’95). Prague (Czech Republic). 1995. pp. 668-673.
https://doi.org/10.1007/3-540-60268-2_362

54. Fainzilberg L.S., Korchinska Z.A., Semergei M.O. Software-technical complex for research of a new method of biometric identification of a person by a phase portrait of an ECG. Forensic Bulletin. 2015, no. 1 (23), pp. 63-71. (In Ukrainian).

55. Fainzilberg L.S., Orikhovskaya K.B., Vakhovsky I.V. Assessment of the randomness of the fragments shape of a single-channel electrocardiogram. Kibernetika i vyčislitel’naâ tehnika. 2016. Iss. 183, pp. 4-24. (In Russian).
https://doi.org/10.15407/kvt183.01.005

56. Fainzilberg L.S., Orikhovskaya K.B. A new approach for detecting the effect of electrical heart alternation on a single-channel ECG. Bulletin of the National Technical University “KPI”. 2017, no. 21 (1243), pp. 144-151. (In Russian). DOI: 10.20998/2411-0558.2017.21.13.
https://doi.org/10.20998/2411-0558.2017.21.13

57. Gritsenko V.I., Fainzilberg L.S., Kravchenko A.N., Korchinskaya Z.A., Orikhovskaya K.B., Pasko V.S., Stanislavskaya S.S. Cognitive graphical images in the task of assessing the body’s response to a load by phase printing method. Upravlâûŝie sistemy i mašiny. 2016, no. 6, pp. 24-33. (In Russian).

58. Fainzilberg L.S. Generalized Method of Processing Cyclic Signals of Complex Form in Multidimension Space of Patameters. Journal of Automation and Information Sciences. 2015. Vol. 47. Iss. 3, pp. 24-39. DOI: 10.1615/JAutomatInfScien.v47.i3.30
https://doi.org/10.1615/JAutomatInfScien.v47.i3.30

59. Fainzilberg L.S., Matushevich N.A. An effective method for analyzing diagnostic features by a noisy electrocardiogram. Upravlâûŝie sistemy i mašiny. 2016, no. 2, pp.76-84. (In Russian).
https://doi.org/10.15407/usim.2016.02.076

60. Fainzilberg L.S., Dykach Ju.R. Linguistic approach for estimation of electrocardiograms’s subtle changes based on the Levenstein distance. Cybernetics and Computer Engineering. 2019, no. 2 (196), pp. 3-26. DOI: https:// 10.15407/kvt196.02.003.
https://doi.org/10.15407/kvt196.02.003

61. Fainzilberg L.S. Intellectual Information Technologies on Smartphone. Proc. the XII International Conference “Information Technology and Automation – 2019” (Odessa, 17-18th of Oct, 2019). Odessa, 2019, Part 1, pp. 31-33.

Received: 06.12.2019

Issue 1 (199), article 3

DOI:https://doi.org/10.15407/kvt199.01.039

Cybernetics and Computer Engineering, 2020, 1(199)

MISHCHENKO M.D., Student
e-mail: mishenkomihailo@gmail.com
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 37, Peremohy av., 03056, Kyiv, Ukraine

GUBAREV V.F., DSc. (Engineering), Corresponding Member of NAS of Ukraine,
Head of the Dynamic Systems Control Department
e-mail: v.f.gubarev@gmail.com
Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine
40, Acad. Glushkova, 03187, Kyiv, Ukraine

METHODS OF MODEL PREDICTIVE CONTROL FOR DISCRETE MULTI-VARIABLE SYSTEMS WITH INPUT

Introduction. There are a lot of systems which can be conveniently modelled as a discrete linear multi-input multi-variable system. When a control problem for such systems arises, it is usually done with methods derived from the control theory. But these methods have several known drawback. For example, for non-deterministic systems, they are based on assumption about certain convenient statistical properties of noises.

The purpose of the paper is to develop synthesis algorithms based on ideas and approaches of the Model Predictive Control (MPC).

Methods. In contrast to the common approach, in this work we aim to synthesize the best control sequence in terms of some criterion. We use results derived from the Kuhn-Tucker theorem for control synthesis.

Results. A new class of methods capable of leading linear system’s state to zero (or, in case of noisy environment, to its neighbourhood) and stabilization of cognitive map’s functioning was developed. This new methods are capable of controlling not only stable systems, but also unstable and semi-stable ones, even in presence of random perturbations and with constrained control resource. These methods differ in efficiency of control resource utilization and required computational resources. More efficient methods require more computations. That’s why it is necessary to choose an appropriate method in each particular case.

Conclusions. The developed methods can be used to control both technical and any other kinds of systems represented either as controllable linear systems with multiple inputs and outputs or as controllable cognitive maps.

Keywords: variational method, cognitive map, control synthesis, discrete controllable system, moving horizon, linear system, MPC

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REFERENCES

1. Garcia C. E., Prett D.M., Morari M. Model predictive control: Theory and Practice – a survey. Automatica. 1989, no. 25, pp. 335-347.
https://doi.org/10.1016/0005-1098(89)90002-2

2. Rawlings, J. B., Muske K.R. The stability of constrained receding horizon control. IEEE Trans. Automat. Control. 1993, AC-38(10), pp. 1512-1516.
https://doi.org/10.1109/9.241565

3. Mayne D. Q. Optimization in model based control. In Proc. IFAC Symposium Dynamics and Control of Chemical Reactors, Distillation Columns and Batch Proacesses. Helsingor. 1995. pp. 229-242.
https://doi.org/10.1016/B978-0-08-042368-5.50041-1

4. Den Boom V. T. J. J. Model based predictive control: Status and perspective. In Symposium on Control, Optimization and Supervision, CESA’96 IMACS Multiconference. Lille, 1996. pp. 1-12.

5. Rawlings J.B., Mayne D.Q. Model Predictive Control: Theory and Design. Nob Hill Publishing, Madison, WI, 2009, ISBN 978-0-9759377-0-9. 576 p.

6. Richalet, J., Rault A., Testud J.L., Papon J. Model predictive heuristic control: Application to industrial processes. Automatica. 1978, no. 14, pp. 413-428.
https://doi.org/10.1016/0005-1098(78)90001-8

7. Qin S.J., Badgwell T.A. An overview of industrial model predictive control technology. In Kantor Y.C., Garcia C.E. Carnahan (Eds) Chemical Process Control-Assessment and New Directions for Research AIChE Symposium series. Vol. 93, no. 316, pp. 232-256.

8. Gubarev V.F., Mishchenko M.D., Snizhko B.M. (Kondratenko Y., Chikrii A., Gubarev V., Kacprzyk J. (eds)). Model Predictive Control for Discrete MIMO Linear Systems. Advanced Control Techniques in Complex Engineering Systems: Theory and Applications. Studies in Systems, Decision and Control. 2019, Vol. 203.
https://doi.org/10.1007/978-3-030-21927-7_4

9. Gubarev V.F., Shevchenko V.M., Zhykov A.O., Gummel A.V. State estimation for Systems Subjected to Bounded Uncertainty using Mooing Horizon Approach. In Preprints of the 15th IFAC Symposium on System Identification, Saint-Malo, France, July 6-8, 2009, pp. 910-915.
https://doi.org/10.3182/20090706-3-FR-2004.00151

Received 27.11.2019

Issue 1 (199), article 2

DOI:https://doi.org/10.15407/kvt199.01.019

Cybernetics and Computer Engineering, 2020, 1(199)

BILOSHYTSKA O.K., Senior Lecturer,
Department of Biomedical Engineering,
e-mail: o.k.biloshytska@gmail.com

NASTENKO Ie.A., DSc (Biology), Senior Researcher
Head of the Biomedical Cybernetics Department
e-mail: nastenko.e@gmail.com

PAVLOV V.A., PhD (Engineering), Associate Professor
Associate Professor of the Biomedical Cybernetics Department
e-mail: pavlov.vladimir264@gmail.com

National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute”,
37, Peremohy av.,03056, Kyiv, Ukraine

THE USE OF COMPLEXITY AND VARIABILITY CHARACTERISTICS FOR THE ANALYSIS OF COMPLEX DYNAMIC SYSTEMS

Introduction. The normal dynamics of a healthy organism is chaotic and the observed “chaos” is inherent in the very nature of the dynamic processes taking place in the organism and the degree of chaotic of these processes may vary in case of pathology in one direction or another. The electrical activity of the brain is also characterized by signs of deterministic chaos, and changes in parameters of its nonlinear dynamics testify to the characteristic changes in brain functioning. The problem of diagnostics and identification of the moment preceding an epileptic seizure or other periods of brain functioning in epileptic patients is not only a problem of choosing a classification method but also of determining quantitative estimates of dynamics reflecting the complexity and variability of the Electroencephalography (EEG) signal.

The purpose of the paper is to form an effective ensemble of features from the characteristics reflecting the complexity and variability of the EEG sig signal ,to construct the prognostic models for the course of epilepsy and to develop the information technology to support diagnostic decision-making based on them.

Methods. The methods of mathematical statistics for the processing of diagnostic information, the methods of mathematical modeling (stepwise logistic regression) — for the construction of prognostic models for estimating the course of epilepsy were used; methodological bases for the creation of information technology for the diagnosis of epilepsy according to the EEG.

Results. Changes in indicators such as Hurst Index, fractal dimension, logistic mapping, and algorithmic signal complexity have been investigated. The mathematical models include variables that are calculated from the EEG data and are available during patient observation. As a result of the application of step-by-step algorithms, the most informative features are included in the models. The selected features allow for the most accurate identification of individual periods of epilepsy flow from the EEG data. It has been established that the use of a decision support system increases the reliability of determining the periods of an epileptic seizure (conditional norm, before, during and after an attack) by an average of 6.6% for children and 8% for adults.

Conclusions. The proposed prognostic models allow to obtain additional information about the periods of epileptic seizures and to predict their onset in time.

Keywords: information technology, EEG, epileptic seizures, epilepsy, complexity and variability indicators, predictive models, logistic regression.

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REFERENCES

1 Unified clinical protocol for primary, emergency, secondary (specialized) and tertiary (highly specialized) epilepsy in adults. Kyiv, 2014 (In Ukrainian). URL: http://mtd.dec.gov.ua/images/dodatki/2014_276_Epilepsii/2014_276_YKPMD_epilepsiya_dorosli.pdf. (Last accessed: 11.09.2019)

2 Unified clinical protocol for primary, emergency, secondary (specialized) and tertiary (highly specialized) epilepsy in children. Kyiv, 2014 (In Ukrainian). URL: http://mtd.dec.gov.ua /images/dodatki/2014_276_Epilepsii/2014_276_YKPMD_epilepsiya_dity.pdf. (Last accessed: 11.09.2019)

3 Díaz M.H., Córdova F.M., Cañete L., Palominos F., Cifuentes F., Sánchez C., Herrera M. Order and Chaos in the Brain: Fractal Time Series Analysis of the EEG Activity During a Cognitive Problem Solving Task. 3rd International Conference on Information Technology and Quantitative Management, ITQM 2015. Elsevier. Procedia Computer Science. 2015, Vol. 55, pp. 1410-1419.
https://doi.org/10.1016/j.procs.2015.07.135

4 Wang X., Meng J., Tan G., Zou L. Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain. Nonlinear Biomedical Physics. 2010, no. 4, pp. 2-10.
https://doi.org/10.1186/1753-4631-4-2

5 Mayorov O. Yu. Reliability of bioelectric activity (EEG, ECG and HRV) researches of the deterministic chaos by the nonlinear analysis methods. Conference Paper !·! 3rd Chaotic Modeling and Simulation International Conference (Chania Crete, January, 2010), Chania Crete, Greece, 2010, 61 p.

6 Biloshytska O.K. Investigation of the features of electroencephalographic signals using nonlinear dynamics. Development of science in the XXI century: a collection of articles of the scientific-information center “Knowledge” on the materials of the X international correspondence scientific-practical conference, Part 1 (Kharkiv, 15th of Feb., 2016), Kharkiv, 2016, pp. 11-16. (In Russian).

7 Borisova O.S. Investigation and analysis of native electroencephalographic data by nonlinear dynamics methods: PhD thesis: 05.11.17 / Institute of Technology. Taganrog, 2010, 163 p. (In Russian).

8 Hacken G. Synergy. Moscow, 1980, 404 p. (In Russian).

9 Mayorov O.Yu., Fenchenko V.N. Study of brain bioelectrical activity from the standpoint of multi-dimensional linear and nonlinear EEG analysis. Clinical Informatics and Telemedicine. 2008, no. 4, pp. 12-20. (In Russian).

10 Mayorov O.Yu. Fenchenko V.N. About calculation of parameters of deterministic chaos at research of bioelectrical activity of a brain (EEG). Klin. inform. and telemed. 2006, no. 3, pp. 37-46. (In Russian).

11 Biloshytska O.K., Klymenko T.A. Analysis of the parameters of EEG signals with epileptic activity using nonlinear methods. Bulletin of NTU “KhPI”. Series: Mechanical-technological systems and complexes. 2017, no. 19 (1241), pp. 30-34 (In Ukrainian).

12 Biloshytska O.K. Nonlinear Dynamics as Instruments for Prediction of Pathological Changes in the Electroencephalogram. Bulletin of NTU “KhPI”. Series: Mechanical-technological systems and complexes. 2016, no. 50 (12221), pp.79-83. (In Ukrainian).

13 Biloshytska O.K. Classification of pathological EEG signals using machine learning methods. Bulletin of NTU “KhPI”. Series: Mechanical-technological systems and complexes. 2017, no. 44 (1266), pp.35-39 (In Ukrainian).

14 Biloshytska O.K., Nastenko Ie.A. Evaluation of prognostic possibilities of EEG signal behavior complexity indicators in epileptic seizures. Information systems and technologies in medicine (ISM-2018): Collection of scientific papers of the I International scientific-practical conference (Kharkiv, 28-30th of Nov, 2018), Kharkiv, 2018, pp. 95-97 (In Ukrainian).

15 Kannathal N., Rajendra A., Fadhilah A. Nonlinear analysis of EEG signals at different mental states. BioMedical Engineering OnLine. 2004. URL: http://www.biomedical-engineering-online.com/content/3/1/7. (Last accessed: 02.12.2019 )

16 Gayazova N. T., Zaripov R. P. Stochastic estimation of the rate of human pathological tremor using Hurst index. TGPU Bulletin. 2008, no. 15. (In Russian). URL: https://cyberleninka.ru/article/n/stohasticheskaya-otsenka-skorosti-patologicheskogo-tremora-cheloveka-s-pomoschyu-pokazatelya-hersta (Last accessed: 25.11.2019)

17 Kalush Y., Loginov V. Hurst Indicator and its hidden properties. Siberian Journal of Industrial Mathematics. 2013, no. 4, pp. 29-37. (In Russian).

18 Biloshytska O.K. Use of nonlinear dynamics and cellular-automatic modeling methods to study the dynamic features of the brain neural network. Problems of information technologies. 2015, no.1, pp. 173-180. (In Ukrainian).

19 Borowska M., Oczeretko E., Mazurek A. Application of the Lempel-Ziv complexity measure to the analysis of biosignals and medical images. Annales Academiae Medicae Bialostocensis. 2012, no. 50, pp. 29-30.

20 Garg A., Mathur M., Upadhayay M. Application of LZW Technique for ECG Data Compression. International Journal of Advances in Computer Networks and its Security. 2013, no. 58, pp. 374-377.

21 Strogatz S. Non-linear Dynamics and Chaos: With applications to Physics, Biology, Chemistry and Engineering. Perseus Books, 2000, 505 p.

22 Biloshytska O.K. Information technology for estimating the course of epilepsy by indicators of electroencephalogram complexity: PhD thesis: 05.13.09 / International Research and Training Center for Information Technologies and Systems of the National Academy of Science of Ukraine and Ministry of Education and Science of Ukraine, Kyiv, 2019, 183 p. (In Ukrainian).

23 Gniezditsky V.V. Inverse EEG problem and clinical electroencephalography (mapping and localization of brain electrical activity sources). Moscow, 2004, 624 p. (In Russian).

24 Zenkov L.R. Clinical electroencephalography (with epileptology elements). Manual for doctors. Moscow, 2004, 368 p. (In Russian).

25 Zhirmunskaya E.A. Clinical electroencephalography. Moscow, 1991, 77 p. (In Russian).

26 Biloshytska O.K. Analysis and assessment of non-linear characteristics of epileptic EEG signals. Scientists notes from the VI Tavriya National University Vernadsky. Series: Technical Sciences. 2018, no 29 (68), Part. 1, pp. 80-85. (In Ukrainian).

Received 27.12.2019