Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-05T05:08:36.622Z Has data issue: false hasContentIssue false
  • English
  • Français

HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children

Part of: Basic Science

Published online by Cambridge University Press:  17 June 2021

David M. Garner ,
Gláucia S. Barreto ,
Vitor E. Valenti Open the ORCID record for Vitor E. Valenti [Opens in a new window] ,
Franciele M. Vanderlei Open the ORCID record for Franciele M. Vanderlei [Opens in a new window] ,
Andrey A. Porto Open the ORCID record for Andrey A. Porto [Opens in a new window]  and
Luiz Carlos M. Vanderlei
David M. Garner
Affiliation:
Cardiorespiratory Research Group, Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Headington Campus, Gipsy Lane, Oxford, OX3 0BP, UK
Gláucia S. Barreto
Affiliation:
Faculdade de Tecnologia Intensiva. FATECI – Fortaleza, Ceará, Sao Paulo, Brazil
Vitor E. Valenti*
Affiliation:
Autonomic Nervous System Center, Sao Paulo State University, UNESP, Marília, Sao Paulo, Brazil
Franciele M. Vanderlei
Affiliation:
Department of Physiotherapy, Sao Paulo State University, UNESP, Presidente Prudente, Sao Paulo, Brazil
Andrey A. Porto
Affiliation:
Autonomic Nervous System Center, Sao Paulo State University, UNESP, Marília, Sao Paulo, Brazil
Luiz Carlos M. Vanderlei
Affiliation:
Department of Physiotherapy, Sao Paulo State University, UNESP, Presidente Prudente, Sao Paulo, Brazil
*
Author for correspondence: Vitor E. Valenti, Autonomic Nervous System Center, Sao Paulo State University, UNESP, Av. Hygino Muzzi Filho, 737. Mirante, 17.525-900 – Marilia, Sao Paulo, Brazil. Tel: +55 14 3402–1300. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction:

Approximate Entropy is an extensively enforced metric to evaluate chaotic responses and irregularities of RR intervals sourced from an eletrocardiogram. However, to estimate their responses, it has one major problem – the accurate determination of tolerances and embedding dimensions. So, we aimed to overt this potential hazard by calculating numerous alternatives to detect their optimality in malnourished children.

Materials and methods:

We evaluated 70 subjects split equally: malnourished children and controls. To estimate autonomic modulation, the heart rate was measured lacking any physical, sensory or pharmacologic stimuli. In the time series attained, Approximate Entropy was computed for tolerance (0.1→0.5 in intervals of 0.1) and embedding dimension (1→5 in intervals of 1) and the statistical significances between the groups by their Cohen’s ds and Hedges’s gs were totalled.

Results:

The uppermost value of statistical significance accomplished for the effect sizes for any of the combinations was −0.2897 (Cohen’s ds) and −0.2865 (Hedges’s gs). This was achieved with embedding dimension = 5 and tolerance = 0.3.

Conclusions:

Approximate Entropy was able to identify a reduction in chaotic response via malnourished children. The best values of embedding dimension and tolerance of the Approximate Entropy to identify malnourished children were, respectively, embedding dimension = 5 and embedding tolerance = 0.3. Nevertheless, Approximate Entropy is still an unreliable mathematical marker to regulate this.

Keywords

Child nutrition disordersnonlinear dynamicsautonomic nervous systemtoleranceembedding dimension
Type
Original Article
Information
Cardiology in the Young , Volume 32 , Issue 3 , March 2022 , pp. 425 - 430
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Goldberger, AL. Cardiac chaos. Science 1989; 243: 1419.CrossRefGoogle ScholarPubMed
Ho, M-W. The Rainbow and the Worm: The Physics of Organisms. World Scientific, Singapore, 2008.CrossRefGoogle Scholar
Prigogine, I. Non-equilibrium statistical mechanics. Interscience, New York, 1962.Google Scholar
Mackey, MC, Milton, JG. Dynamical diseases. Ann N Y Acad Sci 1987; 504: 16–32.CrossRefGoogle ScholarPubMed
Chang, S. Physiological rhythms, dynamical diseases and acupuncture. Chin J Physiol 2010; 53: 77–90.CrossRefGoogle ScholarPubMed
Vanderlei, LC, Silva, RA, Pastre, CM, et al. Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains. Braz J Med Biol Res 2008; 41: 854–859.CrossRefGoogle ScholarPubMed
Rachow, T, Berger, S, Boettger, MK, et al. Nonlinear relationship between electrodermal activity and heart rate variability in patients with acute schizophrenia. Psychophysiology 2011; 48: 1323–1332.CrossRefGoogle ScholarPubMed
Wiertel-Krawczuk, A, Hirschfeld, AS, Huber, J, et al. Sympathetic skin response following single and combined sound and electrical stimuli in young healthy subjects. J Med Sci 2016; 85: 106–113.CrossRefGoogle Scholar
Baum, P, Petroff, D, Classen, J, et al. Dysfunction of autonomic nervous system in childhood obesity: a cross-sectional study. PLoS One 2013; 8: e54546.CrossRefGoogle ScholarPubMed
De Souza, NM, Vanderlei, LCM, Garner, DM. Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV. Complexity 2015; 20: 84–92.CrossRefGoogle Scholar
Bernardo, AF, Vanderlei, LC, Garner, DM. HRV analysis: a clinical and diagnostic tool in chronic obstructive pulmonary disease. Int Sch Res Notices 2014; 673232: 2014.CrossRefGoogle Scholar
Pincus, SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 1991; 88: 2297–2301.CrossRefGoogle ScholarPubMed
Garner, DM, de Souza, NM, Vanderlei, LCM. Unreliability of approximate entropy to locate optimal complexity in diabetes mellitus via heart rate variability. Series Endo Diab Met 2020; 2: 32–40.CrossRefGoogle Scholar
Vanderlei, F, Vanderlei, LCM, de Abreu, LC, et al. Entropic analysis of HRV in obese children. Int Arch Med 2015; 8.CrossRefGoogle Scholar
Garner, DM, Bernardo, AFB, Vanderlei, LCM. HRV analysis: unpredictability of approximate entropy in chronic obstructive pulmonary disease. Series Cardiol Res 2021; 3: 1–10.CrossRefGoogle Scholar
Organization W H and Unicef. WHO Child Growth Standards and the Identification of Severe Acute Malnutrition in Infants and Children: A Joint Statement. United Nations Children’s Fund, Washington, DC, 2009.Google Scholar
Lohman, TG, Roche, AF, Martorell, R. Anthropometric standardization reference manual, vol. 177. Human Kinetics Books Champaign, IL, USA, 1988.Google Scholar
Barbosa, MP, da Silva, NT, de Azevedo, FM, et al. Comparison of Polar(R) RS800G3 heart rate monitor with Polar(R) S810i and electrocardiogram to obtain the series of RR intervals and analysis of heart rate variability at rest. Clin Physiol Funct Imaging 2016; 36: 112–117.CrossRefGoogle Scholar
Gamelin, FX, Berthoin, S, Bosquet, L. Validity of the polar S810 heart rate monitor to measure R-R intervals at rest. Med Sci Sports Exerc 2006; 38: 887–893.CrossRefGoogle Scholar
Vanderlei, LCM, Silva, RA, Pastre, CM, et al. Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains. Braz J Med Biol Res 2008; 41: 854–859.CrossRefGoogle ScholarPubMed
Gamelin, FX, Baquet, G, Berthoin, S, et al. Validity of the polar S810 to measure R-R intervals in children. Int J Sports Med 2008; 29: 134–138.CrossRefGoogle Scholar
Godoy, MF, Takakura, IT, Correa, PR. Relevância da análise do comportamento dinâmico não linear (Teoria do Caos) como elemento prognóstico de morbidade e mortalidade em pacientes submetidos à cirurgia de revascularização miocárdica. Arq Ciênc Saúde 2005; 12: 167–171.Google Scholar
Pimentel, RMM, Ferreira, C, Valenti, V, et al. Complexity measures of heart-rate variability in amyotrophic lateral sclerosis with alternative pulmonary capacities. Entropy 2021; 23: 159.CrossRefGoogle ScholarPubMed
Pincus, S. Approximate entropy (ApEn) as a complexity measure. Chaos 1995; 5: 110–117.CrossRefGoogle Scholar
Richman, JS, Moorman, JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000; 278: H2039–H2049.CrossRefGoogle Scholar
Tarvainen, MP, Niskanen, J-P, Lipponen, JA, et al. Kubios HRV–heart rate variability analysis software. Comput Methods Programs Biomed 2014; 113: 210–220.CrossRefGoogle ScholarPubMed
Anderson, TW, Darling, DA. A test of goodness of fit. J Am Stat Assoc 1954; 49: 765–769.CrossRefGoogle Scholar
Ryan, TA, Joiner, BL. Normal probability plots and tests for normality. In: Minitab Statistical Software: Technical Reports. The Pennsylvania State University, State College, PA. Available from MINITAB: Inc, 1976.Google Scholar
Razali, NM, Wah, YB. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-darling tests. J Stat Model Anal 2011; 2: 21–33.Google Scholar
Blanco-Topping, R. The impact of Maryland all-payer model on patient satisfaction of care: a one-way analysis of variance (ANOVA). Int J Healthcare Manage 2020; 1–8.CrossRefGoogle Scholar
McKight, PE, Najab, J. Kruskal-Wallis test. In: Weiner, IB, Craighead, WE (eds). The Corsini Encyclopedia of Psychology. Wiley, Hoboken, NJ, 2010: 1–1.Google Scholar
Kazis, LE, Anderson, JJ, Meenan, RF. Effect sizes for interpreting changes in health status. Med Care 1989; 27(Suppl 3): S178–S189.CrossRefGoogle ScholarPubMed
Grissom, RJ, Kim, JJ. Effect Sizes for Research: A Broad Practical Approach. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, 2005.Google Scholar
Thompson, B. Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol Sch 2007; 44: 423–432.CrossRefGoogle Scholar
Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Routledge, Oxfordshire, England, UK, 2013.CrossRefGoogle Scholar
Hedges, LV, Olkin, I. Statistical Methods for Meta-Analysis. Academic Press, Cambridge, MA, USA, 2014.Google Scholar
Sawilowsky, SS. New effect size rules of thumb. J Mod Appl Stat Methods 2009; 8: 26.CrossRefGoogle Scholar
Barreto, GS, Vanderlei, FM, Vanderlei, LCM, et al. Risk appraisal by novel chaotic globals to HRV in subjects with malnutrition. J Hum Growth Dev 2014; 24: 243–248.CrossRefGoogle Scholar
Garner, DM, De Souza, NM, Vanderlei, LCM. Risk assessment of diabetes mellitus by Chaotic globals to heart rate variability via six power spectra. Rom J Diabetes Nutr Metab Dis 2017; 24: 227–236.Google Scholar
Garner, DM, Vanderlei, FM, Valenti, VE, et al. Non-linear regulation of cardiac autonomic modulation in obese youths: interpolation of ultra-short time series. Cardiol Young 2019; 29(9): 1196–1201.CrossRefGoogle ScholarPubMed
Vanderlei, FM, Vanderlei, LC, Garner, DM. Chaotic global parameters correlation with heart rate variability in obese children. J Hum Growth Dev 2014; 24: 24–30.CrossRefGoogle Scholar
Vanderlei, FM, Vanderlei, LCM, Garner, DM. Heart rate dynamics by novel chaotic globals to HRV in obese youths. J Hum Growth Dev 2015; 25: 82–88.CrossRefGoogle Scholar