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MULTISCALE ENTROPY AND MULTISCALE TIME IRREVERSIBILITY ANALYSIS OF RR TIME SERIES DEPENDING ON AMBIENT TEMPERATURE

ORIOL ABELLÁN-AYNÉS

Faculty of Sport, University San Antonio of Murcia (UCAM), Avenida Jeronimos s/n, 30007 Murcia, Spain

E-mail Address: oabellan@ucam.edu

Corresponding author.

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JOSE NARANJO-ORELLANA

Department of Sport and Computing, Pablo de Olavide University, Carretera de Utrera, Km. 1, 41013 Seville, Spain

E-mail Address: jnarore@upo.es

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PEDRO MANONELLES

International Chair of Sports Medicine, Faculty of Medicine, University San Antonio of Murcia (UCAM), Avenida Jeronimos s/n, 30007 Murcia, Spain

E-mail Address: pmanonelles@ucam.edu

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FERNANDO ALACID

Department of Education Health Research Centre, University of Almeria, Carretera Sacramento s/n, 04120 La Cañada de San Urbano, Almeria, Spain

E-mail Address: falacid@ual.es

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https://doi.org/10.1142/S0219519420500293Cited by:1 (Source: Crossref)

Abstract

Purpose: The main aim of this paper is to study the influence of temperature on multiscale entropy (MSE) and multiscale time irreversibility (MTI) through the use of short-term measurements. Methods: A total of 12 physically active, healthy, and nonsmoker individuals ( $25.6 \pm 3.9$ $25.6 \pm 3.9$ years old; $174.2 \pm 7.5$ $174.2 \pm 7.5$ cm of height; and $68.6 \pm 11.1$ $68.6 \pm 11.1$ kg of body mass) voluntarily participated in this study. Two beat-to-beat recordings of 15min length were performed on every participant, one under hot conditions ( $3 5^{\circ}$ $3 5^{\circ}$ C) and the other assessment under cool conditions ( $1 9^{\circ}$ $1 9^{\circ}$ C). The order of these two assessments was randomly assigned. Multiscale sample entropy and MTI were assessed in every measurement through 10 scales. Results: Entropy was significantly higher under hot conditions ( $p < 0.05$ $p < 0.05$ ) from the fifth scale compared to cool conditions. On the contrary, MTI values were significantly lower under hotter conditions ( $p < 0.05$ $p < 0.05$ ). Conclusions: The study of MSE and time irreversibility of short RR measurements presents consistent and reliable data. Moreover, exposures to hot conditions provoke an increment of interbeat complexity throughout larger scales and a decrease in the MTI in a healthy population.

Keywords:

References

1. Costa MD, Peng C-K, Goldberger AL , Multiscale analysis of heart rate dynamics: Entropy and time irreversibility measures, Cardiovasc Eng 8 :88–93, 2008. Crossref, Google Scholar
2. Goldberger AL , Non-linear dynamics for clinicians: Chaos theory, fractals, and complexity at the bedside, Lancet 347 :1312–1314, 1996. Crossref, Web of Science, Google Scholar
3. Goldberger AL, Peng C-K, Lipsitz LA , What is physiologic complexity and how does it change with aging and disease?, Neurobiol Aging 23 :23–26, 2002. Crossref, Web of Science, Google Scholar
4. Kaplan DT, Furman MI, Pincus SM, Ryan SM, Lipsitz LA, Goldberger AL , Aging and the complexity of cardiovascular dynamics, Biophys J 59 :945, 1991. Crossref, Web of Science, Google Scholar
5. Al-Angari HM, Sahakian AV , Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome, IEEE Trans Biomed Eng 54 :1900–1904, 2007. Crossref, Web of Science, Google Scholar
6. Pincus SM , Approximate entropy as a measure of system complexity, Proc Natl Acad Sci 88 :2297–2301, 1991. Crossref, Web of Science, Google Scholar
7. Ho KKL, Moody GB, Peng C-K, Mietus JE, Larson MG, Levy D et al., Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics, Circulation 96 :842–848, 1997. Crossref, Web of Science, Google Scholar
8. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P , Methods derived from nonlinear dynamics for analysing heart rate variability, Philos Trans R Soc Lond. A, Math Phys Eng Sci 367 :277–296, 2009. Crossref, Web of Science, Google Scholar
9. Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N , The appropriate use of approximate entropy and sample entropy with short data sets, Ann Biomed Eng 41 :349–365, 2013. Crossref, Web of Science, Google Scholar
10. Xiao M-X, Wei H-C, Xu Y-J, Wu H-T, Sun C-K , Combination of RR interval and crest time in assessing complexity using multiscale cross-approximate entropy in normal and diabetic subjects, Entropy 20 :497, 2018. Crossref, Web of Science, Google Scholar
11. Costa M, Goldberger A, Peng C , Multiscale entropy analysis of complex physiologic time series, Phys Rev Lett 89 :68102, 2002. Crossref, Web of Science, Google Scholar
12. Hu B, Wei S, Wei D, Zhao L, Zhu G, Liu C , Multiple time scales analysis for identifying congestive heart failure based on heart rate variability, IEEE Access, 2019. Web of Science, Google Scholar
13. Costa M, Cygankiewicz I, Zareba W, De Luna AB, Goldberger AL, Lobodzinski S , Multiscale complexity analysis of heart rate dynamics in heart failure: Preliminary findings from the MUSIC study, 2006 Comput Cardiol (IEEE, 2006), pp. 101–103. Google Scholar
14. Awan I, Aziz W, Shah IH, Habib N, Alowibdi JS, Saeed S et al., Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis, PLoS One 13 :e0196823, 2018. Crossref, Web of Science, Google Scholar
15. Javorka M, Trunkvalterova Z, Tonhajzerova I, Javorkova J, Javorka K, Baumert M , Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus, Clin Neurophysiol 119 :1071–1081, 2008. Crossref, Web of Science, Google Scholar
16. Angelini L, Maestri R, Marinazzo D, Nitti L, Pellicoro M, Pinna GD et al., Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series, Artif Intell Med 41 :237–250, 2007. Crossref, Web of Science, Google Scholar
17. Turianikova Z, Javorka K, Baumert M, Calkovska A, Javorka M , The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure, Physiol Meas 32 :1425, 2011. Crossref, Web of Science, Google Scholar
18. Costa M, Goldberger AL, Peng C-K , Broken asymmetry of the human heartbeat: Loss of time irreversibility in aging and disease, Phys Rev Lett 95 :198102, 2005. Crossref, Web of Science, Google Scholar
19. Torres BDLC, Orellana JN , Multiscale time irreversibility of heartbeat at rest and during aerobic exercise, Cardiovasc Eng 10 :1–4, 2010. Crossref, Google Scholar
20. Liu W, Lian Z, Liu Y , Heart rate variability at different thermal comfort levels, Eur J Appl Physiol 103 :361–366, 2008. Crossref, Web of Science, Google Scholar
21. Costa M, Goldberger AL , Peng C-K, Multiscale entropy analysis of biological signals, Phys Rev E 71 :21906, 2005. Crossref, Web of Science, Google Scholar
22. Millar PJ, Rakobowchuk M, McCartney N, MacDonald MJ , Heart rate variability and nonlinear analysis of heart rate dynamics following single and multiple Wingate bouts, Appl Physiol Nutr Metab 34 :875–883, 2009. Crossref, Web of Science, Google Scholar
23. Lee D-Y, Choi Y-S , Multiscale distribution entropy analysis of short-term heart rate variability, Entropy 20 :952, 2018. Crossref, Web of Science, Google Scholar
24. Yao W, Yao W, Wang J , Equal heartbeat intervals and their effects on the nonlinearity of permutation-based time irreversibility in heart rate, Phys Lett A 383 :1764–1771, 2019. Crossref, Web of Science, Google Scholar
25. Wen-po Y, Jun W, Equal values and their effects on the nonlinearity of time irreversibility in heartbeats, 2019. Google Scholar
26. Patel VN, Pierce BR, Bodapati RK, Brown DL, Ives DG, Stein PK , Association of holter-derived heart rate variability parameters with the development of congestive heart failure in the cardiovascular health study, JACC Heart Fail 5 :423–431, 2017. Crossref, Web of Science, Google Scholar
27. Liu C, Zhao L, Cai Z, Liu F, Li Y, Wei S et al., Effect of ectopic beats on heart rate variability indices in heart failure patients, World Congr Med Phys Biomed Eng 2018 (Springer, Singapore, 2019), pp. 361–365. Crossref, Google Scholar
28. Gambassi BB, Rodrigues B, Feriani DJ, Almeida F, de JF, Sauaia BA, Schwingel PA et al., Effects of resistance training of moderate intensity on heart rate variability, body composition, and muscle strength in healthy elderly women, Sport Sci Health 12 :389–395, 2016. Crossref, Google Scholar
29. Siciliano R, Agapito G, Siciliano S, Fimognari FL , Aging changes complexity of heart rate dynamics assessed by entropy and Lyapunov exponent analysis, Geriatr Care 5 :18–22, 2019. Crossref, Google Scholar
30. Yao W, Wang J, Equalities-involved permutation relative entropy in quantifying time irreversibility of heartbeats, 2018. Google Scholar
31. Chladekova L, Czippelova B, Turianikova Z, Tonhajzerova I, Calkovska A, Baumert M et al., Multiscale time irreversibility of heart rate and blood pressure variability during orthostasis, Physiol Meas 33 :1747, 2012. Crossref, Web of Science, Google Scholar
32. Visnovcova Z, Mestanik M, Javorka M, Mokra D, Gala M, Jurko A et al., Complexity and time asymmetry of heart rate variability are altered in acute mental stress, Physiol Meas 35 :1319, 2014. Crossref, Web of Science, Google Scholar
33. Brenner IKM, Thomas S, Shephard RJ , Spectral analysis of heart rate variability during heat exposure and repeated exercise, Eur J Appl Physiol Occup Physiol 76 :145–156, 1997. Crossref, Web of Science, Google Scholar
34. Yamamoto S, Iwamoto M, Inoue M, Harada N , Evaluation of the effect of heat exposure on the autonomic nervous system by heart rate variability and urinary catecholamines, J Occup Health 49 :199–204, 2007. Crossref, Web of Science, Google Scholar
35. Bruce-Low SS, Cotterrell D, Jones GE , Heart rate variability during high ambient heat exposure, Aviat Space Environ Med 77 :915–920, 2006. Google Scholar