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ISSN 2542-1905 (Online)

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Fleishman A. N., Korablina T. V., Smagina E. S., Petrovsky S. A., Iovin D. E., Neretin A. A. Entropy and dfa of heart rate variability in remote ischemic preconditioning, orthostatic test in healthy young subjects and in individuals with changes in autonomic regulation of cardiodynamics. Izvestiya VUZ. Applied Nonlinear Dynamics, 2016, vol. 24, iss. 5, pp. 37-61. DOI: 10.18500/0869-6632-2016-24-5-37-61

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Entropy and dfa of heart rate variability in remote ischemic preconditioning, orthostatic test in healthy young subjects and in individuals with changes in autonomic regulation of cardiodynamics

Fleishman Arnold Naumovich, Institute of Complex Problems of Hygiene and Occupational Diseases of the Siberian Branch of the Russian Academy of Medical Sciences (NII KPGPZ SB RAMS)
Korablina Tatjana Valentinovna, Siberian State Industrial University SibSIU
Smagina Ekaterina Sergeevna, Siberian State Industrial University SibSIU
Petrovsky Stanislav Alfredovich, Institute of Complex Problems of Hygiene and Occupational Diseases of the Siberian Branch of the Russian Academy of Medical Sciences (NII KPGPZ SB RAMS)
Iovin Denis Evgenevich, Siberian State Industrial University SibSIU
Neretin Artem Andreevich, Siberian State Industrial University SibSIU

There was conducted the research of comparative physiological assessment in nonlinear analysis methods of heart rate variability (HRV): the approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE) and detrended fluctuation analysis (DFA) in case of remote ischemic preconditioning (RIPC) and orthostatic test both in healthy young subjects and individuals with autonomic regulation imbalance. The purpose of this work was to raise awareness of the researches as well as disclosing the mechanisms of non-pharmacological methods in heart and brain protection from damage and stress. The main tasks included: 1) a model study of the relations between amplitude and structural HRV features regarding the entropy indexes and DFA; 2) a comparative study in the properties of ApEn, SampEn, MSE of heart rate variability (HRV); 3) the research of many features of entropy and DFA indicators of HRV in the context of RIPC, orthostatic test and autonomic regulation changes. The artificially generated wave signals were used in the model studies. It was revealed that the changes in amplitude and power of the oscillatory processes were not associated with entropy indicators. The results of the model research were extrapolated to the data of HRV and heart rate. During comparative studies of ApEn, SampEn, MSE there was defined the optimum length indicators (m) of the compared sequences in the studied time series. The acceptable deviation of the resulting parameter r for ApEn, SampEn, MSE, a scaling factor for MSE, as well as the advantages and disadvantages of different methods in the analysis of entropy of HRV. Physiological applications of the developed methods were studied in 34 subjects of young age (19–25 years) in the dynamic load tests within RIPC and in 75 individuals with altered autonomic regulation. We evaluated the effects of RIPC to orthostatic test. The indicators of the functional state were represented by ten indexes of HRV spectra, heart rate, blood pressure and nonlinear dynamics (ApEn, SampEn, MSE, DFA). The results were compared to the dynamics of the spectral parameters during SHAM procedure. The significance of differences was assessed based on paired Student’s t-test. In dynamics of the load tests as well as orthostatic test the reactive changes in the structure of cardiodynamics were best reflected in the indicators of SampEn and MSE whereas ApEn didn’t do as well. Moreover, the changes in the indices of entropy and DFA had the opposite direction during orthostatic test. It was established that ApEn, SampEn and MSE can suppress noise. These properties were expressed in a form of increased information value for prognostic criteria in changes of autonomic profile, namely, in cases of isolated decrease in LF and HF and increase in heart rate, which were previously regarded as contradictory criteria of energy deficit states. A marked reduction in entropy of HRV increased the validity of negative prognosis for the patients suffering from diabetes mellitus or polyneuropathy. The absence of a direct dependence of the entropy indicators in the oscillatory processes power of HRV will reduce the information value in indices of entropy regarding the assessment of cardiodynamics in RIPC, because the strengthening in the power of HRV is one of the leading signs for positive dynamics in RIPC. The study results interpretation was based on the capabilities of modern research methods and new theoretical views on the body functioning and transition process from health to disease. 

  1. Fleishman A.N., Suleiman M.S., Shumeyko N.I. Khaliulin I.G., et al. Neurogenic mechanisms of remote ischemic preconditioning in young healthy people // Sbornik nauchnyh trudov VII Vserossijskogo simpoziuma i V Shkoly-seminara s mezhdunarodnym uchastiem. Novokuznetsk, 2015. P. 24 – 40. (In Russian).
  2. Fleishman A.N. et al. Orthostatic tachycardia: diagnostic and prognostic value of Very Low Frequency of heart rate variability //Bjulleten’ sibirskoj mediciny. 2014. Vol. 13, N. 4. (In Russian).
  3. Task Force of the European Society of Cardiology et al. Heart rate variability standards of measurement, physiological interpretation, and clinical use //Eur Heart J. 1996. Vol. 17. P. 354–381.
  4. Sassi R. et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society // Europace. 2015. euv015.
  5. Lipsitz L.A., Goldberger A.L. Loss of’complexity’and aging: potential applications of fractals and chaos theory to senescence //Jama. 1992. Vol. 267, N 13. P. 1806– 1809.
  6. Hardstone R. et al. Detrended fluctuation analysis: A scale-free view on neuronal oscillations //Scale-free Dynamics and Critical Phenomena in Cortical Activity. 2012. P. 75.
  7. Mandelbrot B.B., Wallis J.R. Computer experiments with fractional Gaussian noises: Part 2, rescaled ranges and spectra // Water resources research. 1969. Vol. 5, N 1. P. 242–259.
  8. Peng C.-K., Hausdorff J.M., Goldberger A.L. Fractal mechanisms in neural control: Human heartbeat and gait dynamics in health and disease // In: Self-Organized Biological Dynamics and Nonlinear Control / Ed. J. Walleczek. Cambridge: Cambridge University Press, 2000.
  10. Fang Wang, Qingju Fan, and H. Eugene Stanley. Multiscale multifractal detrended-fluctuation analysis of two-dimensional surfaces // Phys. Rev. E. 2016. Vol. 93. 042213.
  11. Goldberger A.L. et al. Fractal dynamics in physiology: alterations with disease and aging //Proceedings of the National Academy of Sciences. 2002. Vol. 99, suppl 1. P. 2466–2472.
  12. Francis Darrel P., Willson K., Georgiadou P. et al. Physiological basis of fractal complexity properties of heart rate variability in man// J. Physiol. 2002. Vol. 542, N 2. P. 619–629.
  13. Rojo-Alvarez J.L. et al. Analysis of physiological meaning of detrended fluctuation analysis in heart rate variability using a lumped parameter model // Computers in Cardiology. IEEE. 2007. P. 25–28.
  14. West B.J. Fractal physiology and the fractional calculus: a perspective //Frontiers in physiology. 2010. Vol. 1. P. 12.
  15. Ihlen E.A.F. Introduction to multifractal detrended fluctuation analysis in Matlab //Fractal Analyses: Statistical And Methodological Innovations And Best Practices. 2012. P. 97.
  16. Eke A. et al. Fractal characterization of complexity in temporal physiological signals //Physiological measurement. 2002. Vol. 23, N 1. P. R1.
  17. Pincus S. Approximate entropy (ApEn) as a complexity measure //Chaos: An Inter- disciplinary Journal of Nonlinear Science. 1995. Vol. 5, N 1. P. 110–117.
  18. Richman J.S., Moorman J.R. Physiological time-series analysis using approximate entropy and sample entropy //American Journal of Physiology-Heart and Circulatory Physiology. 2000. Vol. 278, N 6. H2039-H2049.
  19. Costa M., Goldberger A.L., Peng C.K. Multiscale entropy analysis of complex physiologic time series //Physical review letters. 2002. Vol. 89, N 6. 068102.
  20. Costa M., Goldberger A. L., Peng C. K. Multiscale entropy analysis of biological signals //Physical review E. 2005. Vol. 71. N 2. P. 021906.
  21. Costa M.D., Goldberger A.L. Generalized multiscale entropy analysis: application to quantifying the complex volatility of human heartbeat time series //Entropy. 2015. Vol. 17, N 3. P. 1197–1203.
  22. Singh B., Bharti N. Software tools for heart rate variability analysis //International Journal of Recent Scientific Research. 2015. Vol. 6, N 4. P. 3501–3506.
  23. Aktaruzzaman M., Sassi R. Parametric estimation of sample entropy in heart rate variability analysis //Biomedical Signal Processing and Control. 2014. Vol. 14. P. 141–147.
  24. Zhang Y.C. Complexity and 1/f noise. A phase space approach //Journal de Physique I. 1991. Vol. 1, N 7. P. 971–977.
  25. Gorban A.N. Basic types of coarse-graining //Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena. Springer Berlin Heidelberg, 2006. P. 117–176.
  26. Gorban A.N. et al. Ehrenfest’s argument extended to a formalism of nonequilibrium thermodynamics //Physical Review E. 2001. Vol. 63, N 6. 066124.
  27. Ehrenfest P., Ehrenfest T. The conceptual foundations of the statistical approach in mechanics. Courier Corporation, 2002.
  28. Ji L. et al. Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method //Biomedical engineering online. 2015. Vol. 14, N 1. P. 1.
  29. Ho Y.L. et al. The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure-a pilot study of multiscale entropy //PloS one. 2011. Vol. 6, N 4. e18699.
  30. Norris P.R., Stein P.K., Morris J.A. Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients //Journal of critical care. 2008. Vol. 23, N 3. P. 399–405.
  31. Tarvainen M.P. et al. Complexity of heart rate variability in type 2 diabetes-effect of hyperglycemia // 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. P. 5558–5561.
  32. Minassian A. et al. Heart rate variability characteristics in a large group of activeduty marines and relationship to posttraumatic stress //Psychosomatic medicine. 2014. Vol. 76, N 4. P. 292.
  33. Cancio L.C. et al. Combat casualties undergoing lifesaving interventions have decreased heart rate complexity at multiple time scales // Journal of critical care. 2013. Vol. 28, N 6. P. 1093–1098.
  34. Young H., Benton D. We should be using nonlinear indices when relating heart-rate dynamics to cognition and mood // Scientific reports. 2015. Vol. 5.
  35. Gonchar I.A., et al. Entropy of heart rate is a predictor of the functional outcome in partial cerebral infarction in blood-pool of carotid arteries in patients with atrial fibrillation//Meditsinskie novosti. 2015. N1. P.41–46 (In Russian).
  36. Wessel N. et al. Entropy measures in heart rate variability data //International Sym- posium on Medical Data Analysis // Springer Berlin Heidelberg. 2000. Pp. 78–87.
  37. Bienertova-Vasku J. et al. Calculating Stress: From Entropy to a Thermodynamic Concept of Health and Disease //PloS one. 2016. Vol. 11, N 1. e0146667.
  38. McEwen B.S. Stress, adaptation, and disease: Allostasis and allostatic load //Annals of the New York Academy of Sciences. 1998. Vol. 840, N 1. P. 33–44.
  39. Ganzel B.L., Morris P.A., Wethington E. Allostasis and the human brain: Integrating models of stress from the social and life sciences //Psychological review. 2010. Vol. 117, N 1. P. 134.
  40. Kupriianov R.V., Zhdanov R.I. Stress and allostasis: Problems, outlooks and relationships // Zhurnal Vysshei Nervnoi Deyatelnosti imeni I.P. Pavlova. 2014. Vol. 64, N 1. P. 21–31. (In Russian).
  41. Sterling P. Allostasis: a model of predictive regulation //Physiology & behavior. 2012. Vol. 106, N 1. P. 5–15.
  42. Fleishman A.N. Heart Rate Variability and Slow Oscillations in Hemodynamics: Nonlinear Phenomena in Clinical Practice. Novosibirsk: Publishing house of SB RAS, 2009 (in Russian).
  43. Fleishman A.N. Slow Oscillations of Hemodynamics. Theory, Practical Application in Clinical Medicine and Prevention. Novosibirsk: Sib. predprijatie RAN, 1999 (in Russian).
  44. Anishchenko V.S. et al. Comparative analysis of methods for classifying the cardio-vascular system’s states under stress //Critical ReviewsTM in Biomedical Engineering. 2001. Vol. 29, N 3.
  45. McEwen B.S., Gianaros P.J. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease //Annals of the New York Academy of Sciences. 2010. Vol. 1186, N 1. P. 190–222.
  46. Doletskiy A.N. The intensity of the relationships of very slow oscillatory processes in the body as an integral characteristic of human adaptation //Bulletin of Volgograd State University. Series 11: Natural sciences. 2014. N 4 (in Russian).
  47. Rebeca Goya Esteban. Heart rate variability characterization using entropy measures. Dissertation submitted to the FEUP, Universidade do Porto in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biomedical Engineering. 2008.
  48. Manilo L.A., Zozulya E.P. Research of the possibility of using approximate entropy to analyze biosignals // Bulletin of the Saint Petersburg State Institute of Technology (Technical University). 2007. P. 3–9. (In Russian).
  49. Silva L.E.V. et al. Multiscale entropy analysis of heart rate variability in heart failure, hypertensive and sinoaortic-denervated rats: Classical and refined approaches// American Journal of Physiology – Regulatory, Integrative and Comparative Physiology. 2016. С. ajpregu. 00076.
  50. Weippert M. et al. Sample entropy and traditional measures of heart rate dynamics reveal different modes of cardiovascular control during low intensity exercise // Entropy. 2014. Vol. 16, N 11. P. 5698–5711.
  51. Al-Angari H.M., Sahakian A.V. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome //IEEE Transactions on Biomedical Engineering. 2007. Vol. 54, N 10. P. 1900–1904.
  52. Thayer J.F., Lane R.D. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration //Neuroscience & Biobehavioral Reviews. 2009. Vol. 33, N 2. P. 81–88.
  53. Vargas B. et al. What Can Biosignal Entropy Tell Us About Health and Disease? Applications in Some Clinical Fields //Nonlinear dynamics, psychology, and life sciences. 2015. Vol. 19, N 4. P. 419–436.
  54.  Ivanitskii G.R. 21st century: What is life from the perspective of physics? // Physics-Usp. 2010. Vol.53. P.327–356.
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