Issue 4 (194), article 5


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Vovk M.I., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department

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,
Glushkov ave., 40, Kiev, 03680 GSP, Ukraine


Introduction. Movement training is one of the main factors to mobilize person’s reserves for movement restoration

The purpose of the article is to consider the theoretical and technological bases of the evolution of synthesis of biotechnical systems for motion control, to show the role of new information technologies and means of digital medicine in the synthesis of systems for personal control of movements for the restoration of motor and speech functions that are affected by pathology.

Results. The evolution of the synthesis of technologies of bioelectric control of human movements is given in the analysis of several generations of programmed muscle electrostimulators such as MIOTON, MIOSTIMUL and the new class of digital medicine devices TRENAR®. The main feature of these devices is the use of specially processed electromyographic (EMG) signals as programs to control signals of electrical stimulation and feedback. The principles, criteria, methods, programs, on the basis of which the innovative technology of personal training / restoration of movements TRENAR® is synthesized are considered. The computer program-apparatus complex “PROMOVА-1” is presented, that implements new technology of personal reconstruction of oral speech after a stroke based on the original techniques of fine motor hand training. Prospective studies are aimed at the synthesis of mobile informational and consulting assistance to the doctor in diagnosing the deficit of motor and speech functions and the formation of individual rehabilitation plans; at the synthesis of technologies to control muscle activity coordination during the performance of coordinated movements and rehabilitation treatment of posture defects.

Conclusion. Current researches are aimed at the further development of such priority areas in medicine as an individual approach to treatment, digital medicine, mobile health based on new information technologies.

Keywords: bioelectric control, movement, speech, coordination, posture, personal rehabilitation, methods, programs, myelectrostimulation, digital medicine.

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1. Inventor’s certificate 190525 USSR. The method of motor control / L. Aleev, S. Bunimovich. No 1019769/31-16; claimed 26.06.65; published 29.12.66, Bull. No 2. (in Russian).

2. Aleev L.S. Bioelectrical system “Mioton” and motor functions of a person. Bull. of AS of USSR. 1969. Iss. 4. P. 70–80 (in Russian).

3. Aleev L.S., Vovk M.I., Gorbanev V., Shevchenko A. «Mioton» in motor control. Kiev: Nauk. dumka, 1980. 142 p. (in Russian).

4. Inventor’s certificate 321 245 USSR. The method of motor control of a person / L. Aleev, S. Bunimovich, M. Vovk, V. Gorbanev, A. Shevchenko. No1455753/31-16; claimed 22.06.1970; registered 03.09.1971. (in Russian).

5. Inventor’s certificate 929 054 USSR. Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko. No 2428608/28-13; claimed 13.12.76; published 23.05.82, Bull. № 19 (in Russian).

6. Inventor’s certificate 976 952 USSR Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko. No 2436412/28-13; claimed 03.01.77; published 30.11.82, Bull. №44. (in Russian).

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Received 14.09.2018

Issue 4 (194), article 4


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Kozak L.M., DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department

Kovalenko A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department

Krivova O.A., Researcher of the Medical Information Systems Department

Romanyuk O.A., Junior Researcher of Medical Information Systems Department

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,
Glushkov ave., 40, Kiev, 03187, Ukraine


Introduction. According to the Concept of Ukraine`s Digital Economy and Society Development in 2018-2020, the key components of “digitalization” are the development of digital infrastructure — broadband Internet throughout Ukraine, and the promotion of digital transformations in various sectors of the economy and society, including medicine.

The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine.

Results. A generated model of digital transformation in medicine is presented and several main stages of this transformation are highlighted: І — digital transformation of primary medical information; ІІ — development of support systems for the diagnostic and treatment process; ІІІ — development of technologies and systems for supporting the physicians` activities with digital information; IV — mobile medicine; V — the digital medicine globalization. The method of determining the markers of the functional state of the cardiovascular system based on mathematical models of forecasting and classification with the use of Data Mining is proposed. The method allows detecting and determining the prognostic values of ECG parameters of the CVS functional state for different groups of patients. The developed IT for supporting the processes of receiving, transmitting and storing digital medical images is aimed at ensuring the effective operation of a physician with digital information from various sources: functional diagnostic complexes, digital medical data storage and images using Picture Archiving and Communication Systems (PACS) and cloud technologies . The proposed telemedicine systems theory including the formulated principles of organizing these systems, criteria and methods for analyzing digital medical data has been implemented for elaborating and functioning the Telemedicine Centre. It enables to cover the population in more than 20 Ukraine`s regions with qualified medical assistance.

Conclusions. The digital transformation in medicine like any new process takes place with a gradual complication of tasks, methods and means of their implementation: from formalization of primary medical information to improvement of methods of its analysis, transfer and storage to improve the quality of medical care for patients at any point of the world.

Keywords: digital transformation in medicine, formalized medical records, Data Mining, IT for assessing human state and physiological systems` state, telemedicine, m-medicine.

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Received 29.08.2018

Issue 4 (194), article 3


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Stepashko V.S., DSc (Engineering), Professor,
Head of Dep. for Information Technologies of Inductive Modeling

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,
Acad. Glushkov av., 40, Kyiv, 03187, Ukraine


Introduction. Effective solution of control and decision-making tasks in complex systems should use the results of mathematical modeling. To construct adequate predictive models, many modern methods and tools are available which may be generally based on two principal approaches: theory-driven (deductive) and data-driven (inductive) ones. The data-driven methods are basic for solving typical tacks of data mining; they implement an inductive process of transition from particular data to models generalizing the data. Among all such methods, very notable are those being developed within the area of GMDH-based inductive modeling founded several decades ago by academician O.H. Ivakhnenko.

The purpose of this paper is analysing the background of the GMDH invention by Ivakhnenko and the evolution of model self-organization ideas, methods and tools during the half-century historical period of successful development of the inductive modeling methodology.

Results. Professor Ivakhnenko acquired broad knowledge in the areas of automatic control, engineering cybernetics and emerging neuroscience initiated by the idea of percep-tron. These were those prerequisites which helped Ivakhnenko to synthesize his original self-organizing approach to solving tasks of constructing models of objects and processes on the basis of experimental data. The paper tracks evolution of scientific ideas and views of Ivakhnenko and main achievements in development of GMDH during the period 1968-1997. Contributions of researchers from different countries to the GMDH modification and application are characterized. Results of further developments of inductive modeling meth-ods and tools in the ITIM department are presented and the most promising prospects of in-vestigations in this field are indicated.

Conclusions. Main prerequisites facilitating the creation of the GMDH by O.H. Ivakhnenko were analysed, basic fundamental, technological and applied achievements of the half-century development of inductive modeling both in Ukraine and abroad were characterized, as well as the most prospective ways of further research were formulated.

Keywords: mathematical modeling, data-driven modeling, model self-organization, GMDH, inductive modeling, noise-immune modeling, information technology, case study.

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Received 20.09.2018

Issue 4 (194), article 2


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Yermakova I.I., Professor, DSc (Biology),
Leading Researcher Dept. of Complex Research
of Information Technologies
e-mail: :

Nikolaienko A.Y., Researcher,
Dept. of Complex Research of Information Technologies

Solopchuk Y.M., Researcher,
Dept. of Complex Research of Information Technologies

Hrytsaiuk O.V., 1st category software engineer,
Dept. of Complex Research of Information Technologies

Tadeieva J.P., Ph.D. (Engineering), Senior Researcher,
Dept. of Complex Research of Information Technologies

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,
Acad. Glushkova av., 40, Kiev, 03187, Ukraine


Introduction. Nowadays it can’t be imagined the development of personalized e-medicine without smartphone use. It involves the integration of information platforms, mobile applications and portable medical devices via cloud technologies into a single system. Predictive systems that can assess and prevent risk factors of human health in extreme environmental conditions is still absent in contemporary mobile personalized medicine. Combining the unique features of the service platform with the recent development of m-health allows to develop a unique information smartphone system for assessing the risk factors of human health in various environmental conditions. The system allows to collect personal data, integrate the data with another mobile app and gadget data and thus provide predictions of the human state.

The purpose of the article is to develop an intelligent information system
using smartphone technologies based on a multi-functional service platform for predicting a human functional state under extreme environmental conditions.

Results. A client-server architecture was used to build the intelligent information smartphone system, which allows a user to access the service platform (a key feature of the system) and a centralized database via the smartphone application.
The “client” is the smartphone application that uses network protocols to exchange data with the server. Data input, primary control and data transfer to the server, as well as receiv-ing and displaying the prediction results on the smartphone screen are the main functions of the app. The server software provides data management (receiving, processing, transferring and storing data in the databases), automatically controls the integrity and consistency of the information received and stored, manages multi-user access and confidentiality of databases of different users, logs system events, etc.
A unique distinctive feature of the developed system is the service platform for process-ing the entered conditions data and giving the prediction of human functional state. The pre-diction results are analyzed and, based on the results of the analysis, the system identifies probable health risk factors. The automatic analysis and decision making allow to classify the developed system as an intelligent information technology.

Conclusions. The smartphone-health system has been developed. The system has a client-server architecture that provides multi-user access to its resources and features.
The “client” is a smartphone application that allows a user to input, control and transfer the data to the server, and then receive and display the results on the screen. The server con-sists of a data flow manager, the service platform, prediction result database. The multifunc-tional service platform provides a user with the prediction of his functional state under chosen environmental conditions and physical activity.

Keywords: smartphone, e-health, human state prediction, mobile health, extreme environmental conditions.

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Received 31.08.2018

Issue 4 (194), article 1


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Grytsenko 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

Rachkovskij D.A., DSc (Engineering), Leading Researcher
Dept. of Neural Information Processing Technologies

Revunova E.G., PhD (Engineering), Senior Researcher
Dept. of Neural Information Processing Technologies

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,
Acad. Glushkov av., 40, Kiev, 03187, Ukraine


Introduction. Distributed representation (DR) of data is a form of a vector representation, where each object is represented by a set of vector components, and each vector component can belong to representations of many objects. In ordinary vector representations, the meaning of each component is defined, which cannot be said about DR. However, the similarity of RP vectors reflects the similarity of the objects they represent.
DR is a neural network approach based on modeling the representation of information in the brain, resulted from ideas about a “distributed” or “holographic” representations. DRs have a large information capacity, allow the use of a rich arsenal of methods developed for vector data, scale well for processing large amounts of data, and have a number of other advantages. Methods for data transformation to DRs have been developed for data of vari-ous types – from scalar and vector to graphs.

The purpose of the article is to provide an overview of part of the work of the Department of Neural Information Processing Technologies (International Center) in the field of neural network distributed representations. The approach is a development of the ideas of Nikolai Amosov and his scientific school of modeling the structure and functions of the brain.

Scope. The formation of distributed representations from the original vector representations of objects using random projection is considered. With the help of the DR, it is possible to efficiently estimate the similarity of the original objects represented by numerical vectors. The use of DR allows developing regularization methods for obtaining a stable solution of discrete ill-posed inverse problems, increasing the computational efficiency and accuracy of their solution, analyzing analytically the accuracy of the solution. Thus DRs allow for in-creasing the efficiency of information technologies applying them.

Conclusions. DRs of various data types can be used to improve the efficiency and intelligence level of information technologies. DRs have been developed for both weakly structured data, such as vectors, and for complex structured representations of objects, such as sequences, graphs of knowledge-base situations (episodes), etc. Transformation of different types of data into the DR vector format allows unifying the basic information technologies of their processing and achieving good scalability with an increase in the amount of data processed.
In future, distributed representations will naturally combine information on structure and semantics to create computationally efficient and qualitatively new information technologies in which the processing of relational structures from knowledge bases is performed by the similarity of their DRs. The neurobiological relevance of distributed representations opens up the possibility of creating intelligent information technologies based on them that func-tion similarly to the human brain.

Keywords: distributed data representation, random projection, vector similarity estimation, discrete ill-posed problem, regularization.

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Received 22.08.2018