Issue 186, article 6


KVT, 2016, Issue 186, pp.56-78

UDC 681.3.06.14


Fainzilberg L.S.

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, Kiev, Ukraine

Introduction. Recently, a new class of information technologies (IT) — intelligent IT is widespread which, unlike traditional, operate generalized concepts — images, and provide more complete information about the external environment. One of the tasks requiring the involvement of intelligent IT — analysis and interpretation of complex form signals with a locally-focused information.

The purpose of the article is — to formulate the basic properties of intelligent IT complex form signal processing, demonstrate the ability to implement these features on the example of the innovative method fasegraphy and outline prospects for further development of this technology.

Methods. Fasegraphy is a high IT which is processing different complex form signals of physical nature, which, basing on a chain of intelligent computational procedures, enables the transition from the observed signal with a locally-focused features (raw material for technology) to information which is focused on a particular user (technology product). The main task of the scientific method fasegraphy aims to detect general laws of indicated signals to identify and use in practice the most effective computational procedures that can ensure this transition.

Results. Basic properties of intelligent IT — adaptation, learning, generalization, invariance, forecasting, understanding, flexibility, interoperability, accessibility have been formulated. Analysis of computational procedures chain in fasegraphy method, that provide a transition from the actually observed signals to technology product, shows that the method has all of the above properties, and therefore fasegraphy can be referred to intelligent ITs. New results have been presented from fasegraphy usage in pediatric cardiology and outlined prospects for the development of this method in two ways — by increasing the reliability of decision making in single-channel ECG and realization of intelligent processing tasks of other signals with locally-focused features.

Conclusions. Fasegraphy intelligent capabilities are far from exhausted and can be used to solve actual scientific and applied problems not only in cardiology but also in other applications.

Keywords: fasegraphy, information technology, complex shape signals, electrocardiogram.

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  1. Omelchenko V.O. Orthogonal expansions of random signals and fields. Кiev: UMKVO, 1991, 142 p. (in Ukrainian).
  2. Murashko V.V., Strutynsky A.V. Electrocardiography. Moscow, 1991, 288 p. (in Russian).
  3. Primin M.A., Nedajjvoda I.V., Vasilev V.E. New algorithms for magnitokardiosignal processing. Control systems and machines, 1998, № 2, pp.48–62. (in Russian).
  4. Ronkin М.А., Ivanov L.B. Rheography in clinical practice. Moscow: SMC MBN, 1997, 250 p. (in Russian).
  5. Piloian G.О. Introduction to Thermal Analysis. Moscow, 1964, 231 p. (in Russian).
  6. Gritsenko V.I., Fainzilberg L.S. FASEGRAPH® — information technology for the integrated assessment of the cardiovascular system state of the electrocardiogram phase portrait. Information technologies for the Physician, 2013, № 3, pp.52–63. (in Russian).
  7. Fainzilberg L.S. Computer diagnostics by phase portrait of electrocardiogram. Kiev, 2013, 191 p. (in Russian).
  8. Katerinchuk I.P., Borisenko N.B. Heart rate variability and autonomic dysfunction in patients with metabolic syndrome. Arrhythmology, 2012, № 3(3), pp. 6–13. (in Ukrainian).
  9. Pavlichencko P.P. The influence of the playing load on the functional status of professional football players. World of medicine and biology, 2015, № 1(48), pp. 49–54. (in Russian).
  10. Ozhegov vocabulary. Available at: URL:
  11. Fainzilberg L.S. Information technology for signal processing of complex shape. Theory and practice. Kiev, 2008, 333 p. (in Russian).
  12. Gritsenko V.I. Intellectualization of Information Technologies. Science & Technology. Kiev: V.M. Glushkov Institute of Cybernetics of NAS of Ukraine, 1992, pp. 4–9. (in Ukrainian).
  13. Hawkins D., Blakeslee S. On intelligence. Moscow, St. Petersburg, Kiev, 2007, 128 p. (in Russian).
  14. Preston J., Bishop M. Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford, 2002, 410 p.
  15. Fainzilberg L.S. Simulation models of generating artificial cardiograms in terms of internal and external disturbances. Journal of Qafgaz University Mаthеmаtics and Computer Science, 2012, № 34, pp. 92–104. (in Russian).
  16. Fainzilberg L.S., Glushauskene G.A. Narrow-band Rejection Filter for Suppression of Harmonic Concentrated Interference on the Basis of Discrete Fourier Transform. Journal of Automation and Information Sciences, 2009, Vol. 41, Iss. 8. pp. 55–70.
  17. Fainzilberg L.S. Adaptive smoothing of noise in information technology processing of physiological signals. Mathematical Machines and Systems, 2002, № 3, pp. 96–104. (in Russian).
  18. Zywienz C., Borovsky D., Goettsch G.& others Methodology of ECG Interpretation in the Hanover Program. Meth. Inf. Med, 1990, № 29, pp. 375.
  19. Gritsenko V.I., Fainzilberg L.S. Computer diagnostics using complex-form signals under conditions of internal and external disturbances. Reports of the National Academy of Sciences of Ukraine, 2013, № 12, pp. 36–44. (in Russian).
  20. L.L., Shtarck M.B. About ECG phase portrait. Avtometriya, 1993, № 2, pp. 51–54. (in Russian).
  21. Fainzilberg L.S. A method of personalized assessment of functional state of human cardiovascular system. UA Patent112325. 2016. Bul. 16. (in Ukrainian).
  22. Fainzilberg L.S. The Performance Evaluation of Information Technology FASEGRAPH® According to the Independent Studies. Control systems and machines, 2014, № 2, pp. 84–92. (in Russian).
  23. Maydannik V.G., Khaitovych N.V., Fainzilberg L.S. & others The 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, № 3, pp. 35–39. (in Russian).
  24. Morozik А.А., Fainzilberg L.S. Diagnostic value of electrocardiosignal combined analysis on phase plane and heart rate variability in children with diabetic cardiomyopathy. International Journal of Pediatrics, Obstetrics and Gynecology, 2015, Iss. 7, № 1, pp. 11– 17. (in Russian).
  25. Kondratiuk A.S., Garkaviy S.I., Korshun M.M. & others.Evaluation of primary school pupil’s functional state in physical education classes and swimming lessons dynamics. Hygiene of settlements, 2014, № 64, pp. 302–308. (in Ukrainian).
  26. Fainzilberg L.S., Orikhovska K.B., Vakhovskyi I.V. Assessment of chaotic fragments shape of the single-channel electrocardiogram. Kibernetika i vyčislitelʹnaâ tehnika, 2016, № 183, pp. 4–24. (in Russian).
  27. Fainzilberg L.S. Method of evaluating the adequacy of reaction to load UA Patent 103229. 2015. Bul. 23. (in Ukrainian).
  28. Fainzilberg L.S., Soroka Т.V. Development of telemedicine system for remote monitoring of heart activity based on fasegraphy method. East Europe Journal of Enterprise Technologies, 2015, № 6/9(78), pp. 37–46.
  29. Fainzilberg L.S. Method for person identification by electrocardiogram UA Patent 105273. 2014. Bul. 8. (in Ukrainian).

Received 27.09.16