Complex measurements of heart rate variability in obese youths: distinguishing autonomic dysfunction
Introduction: Heart rate variability (HRV) can be assessed from RR-intervals. These are derived from an electrocardiographic PQRST-signature and can deviate in a chaotic or irregular manner. In the past, techniques from statistical physics have allowed researchers to study such systems.
Objective: This study planned to assess the heart rate dynamics in young obese subjects by nonlinear metrics to heart rate variability.
Methods: 86 subjects were split equally according to status. Heart rate was recorded with the subjects resting in a dorsal (prone) position for 30 minutes. The complexity of the RR-intervals was assessed by five Entropies, Detrended Fluctuation Analysis, Higuchi and Katz’s fractal dimensions Following inconclusive tests of normality we calculated the One-Way Analysis of Variance, Kruskal-Wallis, and the Effect Sizes by Cohen’s d significances.
Results: It was established that Shannon, Renyi and Tsallis Entropies and the Higuchi and Katz・s fractal dimensions could significantly discriminate the two groups. The three entropies were higher in obese youths, suggesting less predictable sets of RR intervals (p<0.0001; d≈1.0). Whilst the Higuchi (p<0.003; d≈0.76) and Katz・s (p≈0.02; d≈0.57) fractal dimensions were lower in obese youths.
Conclusion: As with chaotic globals an increase in response was detected by three measures of entropy in young obese. This is counter to the decreasing response detected by fractal dimensions. Chaotic globals and entropies are more dependable than fractal dimensions when assessing the responses to obesity.
2. Goldberger AL, West BJ. Chaos and order in the human body. MD Comput. 1992;9(1):25-34.
3. Prigogine I. Non-equilibrium statistical mechanics.: New York: Interscience; 1962.
4. Prigogine I, Lefever R, Goldbeter A, Herschkowitz-Kaufman M. Symmetry breaking instabilities in biological systems. Nature. 1969;223(5209):913-6.
5. Reichl LE, Prigogine I. A modern course in statistical physics: University of Texas press Austin; 1980.
6. Vanderlei LCM, Pastre CM, Hoshi RA, Carvalho TDd, Godoy MFd. Basic notions of heart rate variability and its clinical applicability. Rev Bras Cirurgia Cardiovasc. 2009;24(2):205-17.
7. Abreu LC. Heart rate variability as a functional marker of development. J Hum Growth Dev. 2012;22(3):279-82.
8. Souza NM, Vanderlei LCM, Garner DM. Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV. Complexity. 2015;20(3):84-92.
9. Garner DM, Souza NM, Vanderlei LCM. Risk Assessment of Diabetes Mellitus by Chaotic Globals to Heart Rate Variability via Six Power Spectra. Romanian J Diabetes Nutr Metabolic Diseases. 2017;24(3):227-36. DOI: https://doi.org/10.1515/rjdnmd-2017-0028
10. Bernardo AF, Vanderlei LC, Garner DM. HRV Analysis: A Clinical and Diagnostic Tool in Chronic Obstructive Pulmonary Disease. Int Sch Res Notices. 2014;2014:673232. DOI: https://doi.org/10.1155/2014/673232
11. Ponnusamy A, Marques JL, Reuber M. Comparison of heart rate variability parameters during complex partial seizures and psychogenic nonepileptic seizures. Epilepsia. 2012;53(8):1314-21. DOI: https://doi.org/10.1111/j.1528-1167.2012.03518.x
12. Ponnusamy A, Marques JL, Reuber M. Heart rate variability measures as biomarkers in patients with psychogenic nonepileptic seizures: potential and limitations. Epilepsy Behav. 2011;22(4):685-91. DOI: https://doi.org/10.1016/j.yebeh.2011.08.020
13. Mackey MC, Milton JG. Dynamical diseases. Ann N Y Acad Sci. 1987;504(1):16-32.
14. Vanderlei FM, Vanderlei LCM, Garner DM. Heart rate dynamics by novel chaotic globals to HRV in obese youths. J Hum Growth Dev. 2015;25(1):82-8. DOI: https://doi.org/10.7322/jhgd.96772
15. Welch P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics. 1967;15(2):70-3.
16. Ghil M. The SSA-MTM Toolkit: Applications to analysis and prediction of time series. Applications of Soft Computing. 1997;3165:216-30.
17. Garner DM, Ling BWK. Measuring and locating zones of chaos and irregularity. J Syst Sci Complex. 2014;27(3):494-506. DOI: https://doi.org/10.1007/s11424-014-2197-7
18. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A. 1991;88(6):2297-301.
19. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278(6):H2039-49. DOI: https://doi.org/10.1152/ajpheart.2000.278.6.H2039
20. Lake DE, Richman JS, Griffin MP, Moorman JR. Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol. 2002;283(3):R789-97. DOI: https://doi.org/10.1152/ajpregu.00069.2002
21. Shannon CE. A mathematical theory of communication. Bell System Technical J. 1948;27(3):379-423. DOI: https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
22. Lenzi E, Mendes R, Silva L. Statistical mechanics based on Renyi entropy. Physica A. 2000;280(3):337-45.
23. Zyczkowski K. Renyi extrapolation of Shannon entropy. Open Syst Inf Dyn. 2003;3(10):297-310.
24. Mariz AM. On the irreversible nature of the Tsallis and Renyi entropies. Physics Letters A. 1992;165(5-6):409-11. DOI: https://doi.org/10.1016/0375-9601(92)90339-N
25. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82-7. DOI: https://doi.org/10.1063/1.166141
26. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena. 1988;31(2):277-83. DOI: https://doi.org/10.1016/0167-2789(88)90081-4
27. Katz MJ. Fractals and the analysis of waveforms. Computers Biol Med. 1988;18(3):145-56. DOI: https://doi.org/10.1016/0010-4825(88)90041-8
28. Taylor AE. L’Hospital’s rule. Am Mathemat Monthly. 1952;59(1):20-4. DOI: https://doi.org/10.2307/2307183
29. Pinelis I. L’Hospital Type rules for monotonicity, with applications. J Ineq Pure Appl Math. 2002;3(1).
30. Santos RJV. Generalization of Shannon’s theorem for Tsallis entropy. J Mathemat Phys. 1997;38(8):4104. DOI: https://doi.org/10.1063/1.532107
31. Plastino AR, Plastino A. Stellar polytropes and Tsallis’ entropy. Phys Letters A. 1993;174(5-6):384-6. DOI: https://doi.org/10.1016/0375-9601(93)90195-6
32. Donaldson GC, Seemungal TA, Hurst JR, Wedzicha JA. Detrended fluctuation analysis of peak expiratory flow and exacerbation frequency in COPD. Eur Respir J. 2012;40(5):1123-9. DOI: https://doi.org/10.1183/09031936.00180811
33. Castiglioni P, Quintin L, Civijian A, Parati G, Di Rienzo M. Local-scale analysis of cardiovascular signals by detrended fluctuations analysis: effects of posture and exercise. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:5035-8. DOI: https://doi.org/10.1109/IEMBS.2007.4353471
34. Karasik R, Sapir N, Ashkenazy Y, Ch P, Ivanov PC, Dvir I, et al. Correlation differences in heartbeat fluctuations during rest and exercise. Phys Rev E. 2002;66(6):062902. DOI: https://doi.org/10.1103/PhysRevE.66.062902
35. Leon-Lomeli R, Murguia J, Chouvarda I, Mendez M, Gonzalez-Galvan E, Alba A, et al. Relation between heart beat fluctuations and cyclic alternating pattern during sleep in insomnia patients. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2249-52. DOI: https://doi.org/10.1109/EMBC.2014.6944067
36. Ahmad S, Ramsay T, Huebsch L, Flanagan S, McDiarmid S, Batkin I, et al. Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One. 2009;4(8):e6642. DOI: https://doi.org/10.1371/journal.pone.0006642
37. Liao CM, Hsieh NH, Chio CP. Fluctuation analysis-based risk assessment for respiratory virus activity and air pollution associated asthma incidence. Science Total Environment. 2011;409(18):3325-33. DOI: https://doi.org/10.1016/j.scitotenv.2011.04.056
38. Rossi RC, Vanderlei FM, Bernardo AF, Souza NM, Goncalves AC, Ramos EM, et al. Effect of pursed-lip breathing in patients with COPD: Linear and nonlinear analysis of cardiac autonomic modulation. COPD.
2014;11(1):39-45. DOI: https://doi.org/10.3109/15412555.2013.825593
39. Annegarn J, Spruit MA, Savelberg HHCM, Willems PJB, Wouters EFM, Schols AMWJ, et al. Stride time fluctuations during the six minute walk test in COPD patients. Rehabilitation: Mobility, Exercise, and Sports. 2010;26:149-51. DOI: https://doi.org/10.3233/978-1-60750-080-3-149
40. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV–heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210-20. DOI: https://doi.org/10.1016/j.cmpb.2013.07.024
41. Khoa TQD, Ha VQ, Toi VV. Higuchi fractal properties of onset epilepsy electroencephalogram. Comput Math Methods Med. 2012;2012:461426. DOI: https://doi.org/10.1155/2012/461426
42. Kreyszig E. Advanced engineering mathematics: Wiley; 2011.
43. Anderson TW, Darling DA. A test of goodness of fit . J Am Statistical Assoc.1954;49(268):765-9.
44. Ryan Jr TA, Joiner BL. Normal probability plots and tests for normality. The Pennsylvania State University, State College, PA. 1976.
45. Hsu JC. Multiple Comparisons: Theory and Methods. Boca Raton, Florida: CRC Press, 1996.
46. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Statistical Assoc. 1952;260(47):583-621. DOI: https://doi.org/10.2307/2280779
47. Kazis LE, Anderson JJ, Meenan RF. Effect sizes for interpreting changes in health status. Med Care. 1989;(3 Suppl):S178-89.
48. Vanderlei F, Vanderlei LCM, Abreu LC, Garner D. Entropic Analysis of HRV in Obese Children. Int Arch Med. 2015;8. DOI: http://dx.doi.org/10.3823/1799
49. Vanderlei FM, Vanderlei LCM, Garner DM. Chaotic global parameters correlation with heart rate variability in obese children. J Hum Growth Dev. 2014;24(1):24-30. DOI: https://doi.org/10.7322/jhgd.72041
50. Barreto GS, Vanderlei FM, Vanderlei LCM, Garner DM. Risk appraisal by novel chaotic globals to HRV in subjects with malnutrition. J Hum Growth Dev. 2014;24(3):243-8. DOI: https://doi.org/10.7322/jhgd.88900
CODE OF CONDUCT FOR JOURNAL PUBLISHERS
Publishers who are Committee on Publication Ethics members and who support COPE membership for journal editors should:
- Follow this code, and encourage the editors they work with to follow the COPE Code of Conduct for Journal Edi- tors (http://publicationethics.org/files/u2/New_Code.pdf)
- Ensure the editors and journals they work with are aware of what their membership of COPE provides and en- tails
- Provide reasonable practical support to editors so that they can follow the COPE Code of Conduct for Journal Editors (http://publicationethics.org/files/u2/New_Code.pdf_)
- Define the relationship between publisher, editor and other parties in a contract
- Respect privacy (for example, for research participants, for authors, for peer reviewers)
- Protect intellectual property and copyright
- Foster editorial independence
Publishers should work with journal editors to:
- Set journal policies appropriately and aim to meet those policies, particularly with respect to:
– Editorial independence
– Research ethics, including confidentiality, consent, and the special requirements for human and animal research
– Transparency and integrity (for example, conflicts of interest, research funding, reporting standards
– Peer review and the role of the editorial team beyond that of the journal editor
– Appeals and complaints
- Communicate journal policies (for example, to authors, readers, peer reviewers)
- Review journal policies periodically, particularly with respect to new recommendations from the COPE
- Code of Conduct for Editors and the COPE Best Practice Guidelines
- Maintain the integrity of the academic record
- Assist the parties (for example, institutions, grant funders, governing bodies) responsible for the investigation of suspected research and publication misconduct and, where possible, facilitate in the resolution of these cases
- Publish corrections, clarifications, and retractions
- Publish content on a timely basis