350 rub
Journal Biomedical Radioelectronics №1 for 2017 г.
Article in number:
Identification and evaluation of non-linear relationships between the electrographic signals heart muscle and patient time series
Keywords: oscillator expand and collapse the oscillation phase the analytical signal power and communications orientation assessment of statistical significance
Authors:
N.T. Abdullaev - Ph.D. (Eng.), Associate Professor, Department Biomedical Technics, Azerbaijan Technical University E-mail: a.namik46@mail.ru O.A. Dyshin - Ph.D. (Phys.-Math.), Senior Research Scientist, Azerbaijan State University of Oil and Industry, Sceintific Research Institute - Geotechnological Problems of Ois, Gas and Chemistry - D. A. Dadasheva - Assistant, Department Engineering Device Designing, Azerbaijan State University of Oil and Industry
Abstract:
The most important role in the relations between the various organs and the entire life of the organism play rhythms of physiological functions, discoordination dyssynchrony and which are the first signs of possible pathological changes. In electrophysiology mainly investigates potential fluctuations representing oscillatory processes. The success of the vital activity of living systems is determined by the consistency and stability of frequency and phase relationships between the functional changes in some of its parts and that all or almost all of the regulation of processes in biological objects built as oscillators. In the analysis of oscillatory processes of the whole organism established regulatory role of resonance mechanisms in changing the functional state of the organism. Thus, the possibility of use for diagnostic purposes ratios of parameters of oscillatory processes such as information communications physiological functions. The principle of information links between elements of the functional systems of the whole organism determines the future path of development of both information systems. The paper considers the problem of determining the nature of the relationship between the stochastic oscillators, which generate respectively ECG and EMG signal.
Pages: 23-34
References

 

  1. Pikovskijj A.S., Rozenbljum M.G., Kurts JU. Sinkhroni­zacija. Fundamentalnoe nelinejjnoe javlenie. M.: Tekhnosfera. 2003.
  2. Lehnertz K. et al. Chaos in Brain - Proc. of the Workshop, Bonn, Germany, 10-12 March 1999 (Singapore: World Scientific, 2000).
  3. Anishhenko V.S., Vadivasova T.E., Astakhov V.V. Nelinejjnaja dinamika khaoticheskikh i stokhasticheskikh sistem. Saratov: Izd-vo Saratovskogo un-ta. 1999.
  4. Box E.P., Jenkins G.M. Time Series Analysis. Fore casting and Control (San Francisco: Holden-day, 1970) [Boks Dzh., Dzhenkins G. Analiz vremennykh rjadov. Prognoz i upravlenie. M.: Mir. 1974].
  5. Schreiber T. Measuring Information Transfer // Physical Review Letters. 2000. V. 85. P. 461.
  6. Hlavackova-Schindler K. et al // Phys, Rep. 2007. V. 441. № 1. R. 1-46.
  7. Arnhold J., grassberger P., Lehnertz K., Elger C.E. A robust method for detecting interdependences: application to intracranially recorded EEG // Physica D.1999. V. 134.  № 4, R. 419-430.
  8. Cenys A., Lasiene G., Pyragas K. Estimation of interrelation between chaotic observables // Physica D Nonlinear Phenomena. 1991. V. 51. № 2-3, P. 332-337.
  9. Rulkov N.F., Sushchik M. M., Tsimring A., Abarbanel H.D. Generalized synchronization of chaos in directionally coupled chaotic systems // Physical Review.1995. V. 51.  № 2. P 980-984.
  10. Schiff S.J., So P., Chang T., Burke R. E., Sauer T. // Physical Review. 1998. V. 54. № 6. P. 6708-6724.
  11. Bezruchko B.P., Smirnov D.A. Matematicheskoe mode­lirovanie i khaoticheskie vremennye rjady. Saratov: Izd-vo Gos. UNC «Kolledzh». 2005. 319 s.
  12. Bezruchko B.P., Ponomarenko V.I., Prokhorov M.D., Smirnov D.A., Tass P.A. Modelirovanie i diagnostika vzaimodejjstvija nelinejjnykh kolebatelnykh sistem po khaoticheskim vremennym rjadam (prilozhenija v nejjrofiziologii) // Uspekhi fizicheskikh nauk. 2008. T. 178. № 3. S. 323-329.
  13. Rosenblum M.G., Pikovsky A.S. Detecting direction of coupling in interacting oscillators // Physical Review. 2001. V. 64. № 4. P. 045202.
  14. Smirnov D.A., Bezruchko B.P. Estimation of interaction strength and direction from short and noisy time series // Physical Review. E. 2003. V. 68. P. 046209.
  15. Smirnov D.A., Bezruchko B.P. Detection of couplings in ensembles of stochastic oscillators // Physical Review. E. 2009. V. 79. 046204.
  16. Kori H., Kuramoto Y. Slow switching in globally coupled oscillators: robustness and occurrence trough delayed coupling // Physical Review. E. 2001. V. 63. P. 046214.
  17. Smirnov D.A. Vyjavlenie nelinejjnykh svjazejj mezhdu stokhasticheskimi oscilljatorami po vremennym rjadam // Izv. vuzov. Ser. Prikladnaja nelinejjnaja dinamika. 2010. T. 18. № 2. S. 16-38.
  18. Kuramoto Y. Chemical Oscillations. Waves and Turbulence. Berlin: Springer-Verlag. 1984.
  19. Gabor D. Theory of communication // J. Inst. Elect. Eng. (London). 1946. V. 93. P. 429.
  20. Rosenblum M.G., Pikovsky A.S., Kurths J., Schaefer C., Tass P.A. Phase synchronization: from theory to data analysis // Neuro-informatics. Handbook of Biological Physics. Edited by F. Moss and S. Gielen. New York: Elsever Science. 2001. P. 279.
  21. Lachaux J.P., Rodriguez E., Le Van Quyen M. et al. Studing single-trials of phase synchronous activity in the brain // Int. J. Bif. Chaos. 2000. V.10. P. 2429-2455.
  22. Smolencev N.K. Osnovy teorii vejjvletov. Vejjvlety v MATLAB. M.: DMK Press. 2008. 448 s.
  23. Kendall M., Stjuart A. Statisticheskie vyvody i svjazi: Per. s angl. M.: Nauka. 1973. 899 s.