Study of the stability of the cardiovascular system from the data of bioelectric modeling and high resolution electrocardiography
https://doi.org/10.29235/1814-6023-2019-16-3-271-282
Abstract
The information technology has been developed for detecting unstable states of the cardiovascular system based on dispersive bioelectric models and 4th generation electrocardiography. New equipment and software for assessing predictors of life-threatening arrhythmias have been created and certified. The reserves of cardiac activity adaptation from elite athletes to patients with myocardial infarction have been studied. A risk stratification model has been developed for patients with chronic heart failure, the forecast correctness was 94.7 %.
About the Author
A. V. Frolov
Republican Scientific and Practical Center “Cardiology”
Belarus
Belarus
Alexander V. Frolov – D. Sc. (Biol.), Professor, Head of the Laboratory
110B, R. Luxemburg Str., 220036, Minsk
References
1. Wellens H., Shwartz P., Lindemans F., Buxton A. E., Goldberger J. J., Hohnloser S. H. [et al.]. Risk-stratification for sudden cardiac death: current status and challenges for future. European Heart Journal, 2014, vol. 35, no. 25, pp. 1642–1651. https://doi.org/10.1093/eurheartj/ehu176
2. Hodgkin A. L., Huxly A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 1952, vol. 117, no. 4, pp. 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764
3. Clancy C., Rudy Y. Linking a genetic defect to its cellular phenotype in a cardiac arrhythmias. Nature, 1999, vol. 400, no. 6744, pp. 566–569. https://doi.org/10.1038/23034
4. Karma A., Gilmour R. F. Nonlinear dynamics of heart rhythm. Physics Today, 2007, vol. 60, pp. 51–57. https://doi.org/10.1063/1.2718757
5. Novoselov V. S. To the mathematical model of pacemaker. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informatika. Protsessy upravleniya = Vestnik of Saint Petersburg University. Applied mathematics. Computer science. Control processes, 2012, iss. 4, pp. 58–64 (in Russian).
6. Surovyatkina E. D. Multistability Phenomenon in Dynamics of Electrical Activity of Human Cardiac Cell. Metody nelineinogo analiza v kardiologii i onkologii. Fizicheskie podkhody i klinicheskaya praktika. Vypusk 2 [Nonlinear analysis methods in cardiology and oncology. Physical approaches and clinical practice. Issue 2]. Moscow, 2010, pp. 9–20 (in Russian).
7. Bauer S., Röder G., Bär M. Alternans and the influence of ionic channel modifications: Cardiac three-dimensional simulations and one-dimensional numerical bifurcation analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2007, vol. 17, no. 1, p. 015104. https://doi.org/10.1063/1.2715668
8. Frolov A. V., Mrochek, A. G., Vaikhanskaya T. G., Vorob’ev A. P., Mel’nikova, O. P. Electrical instability of the myocardium: mechanisms of development, diagnosis, clinical significance. Minsk, Belaruskaya navuka Publ., 2014. 228 p. (in Russian).
9. Armoundas A. A., Tomaselli G. F., Esperer H. D. Pathophysiological basis and clinical application of T-wave alternans. Journal of the American College of Cardiology, 2002, vol. 40, no. 2, pp. 207–217. https://doi.org/10.1016/s0735-1097(02)01960-5
10. Makarycheva O. V., Vasil’eva E. Yu., Radzevich A. E., Shpektor A.V. Dispersion dynamics of the QT interval in acute myocardial infarction and its prognostic value. Kardiologiya [Cardiology], 1998, no. 7, pp. 43–46 (in Russian).
11. Bauer A., Malik M., Schmidt G., Barthel P., Bonnemeier H., Cygankiewicz I. [et al.]. Heart rate turbulence: standards, physiological interpretation, and clinical. Journal of the American College of Cardiology, 2008, vol. 52, no. 17, pp. 1353–1365. https://doi.org/10.1016/j.jacc.2008.07.041
12. Bauer A., Kantelhardt J. W., Barthel P., Schneider R., Mäkikallio T., Ulm K. [et al.]. Deceleration capacity of heart rate as a predictor of mortality after infarction: cohort study. Lancet, 2016, vol. 367, no. 9523, pp. 1674–1681. https://doi.org/10.1016/s0140-6736(06)68735-7
13. Malik M., Bigger J. T., Camm A. J., Kleiger R. E., Malliani A., Moss A. J., Schwartz P. J. Heart rate variability: standards of measurements, physiological interpretation, and clinical use. European Heart Journal, 1996, vol. 17, no. 3, pp. 354–381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868
14. The “Intecard” electrocardiological and interpreting complex TS BY1050381.001-2002]. Minsk, 2002. 35 p. (in Russian).
15. Frolov A. V., Vorob’ev A. P., Mel’nikova O. P. The “Intecard 7” software for the detection of predictors of life-threatening arrhythmias. No. 00248, 18.10.2012. Minsk, 2012. 37 p. (in Russian).
16. Frolov A. V., Radchuk V. J., Mel’nikova O. P. [et al.]. The “Breez M” software for the analysis of heart rate variability. No. 00059, 11.01.2002. Minsk, 2012. 44 p. (in Russian).
17. Piatrasik G., Zaręba W. QRS fragmentation: diagnostic and prognostic significance. Cardiology Journal, 2012, vol. 19, no. 2, pp. 114–121. https://doi.org/10.5603/CJ.2012.0022
18. Simonenko V. B., Shirokov E. A., Frolov V. M. Clinical crysology in cardionevrology. Moscow, KVORUM Publ., 2013. 291 p. (in Russian).
19. Frolov A. V., Vaikhanskaya T. G., Melnikova O. P., Vorobiev A. P., Guel L. M. Risk stratification personalized model for prediction of life-threatening ventricular tachyarrhythmias in patients with chronic heart failure. Kardiologia Polska, 2017, vol. 75, no. 7, pp. 682–688. https://doi.org/10.5603/KP.a2017.0060
Review
For citations:
Frolov A.V. Study of the stability of the cardiovascular system from the data of bioelectric modeling and high resolution electrocardiography. Proceedings of the National Academy of Sciences of Belarus, Medical series. 2019;16(3):271-282. (In Russ.) https://doi.org/10.29235/1814-6023-2019-16-3-271-282
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