Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-03T05:34:43.978Z Has data issue: false hasContentIssue false

Non-linear regulation of cardiac autonomic modulation in obese youths: interpolation of ultra-short time series

Published online by Cambridge University Press:  27 August 2019

David M. Garner
Affiliation:
Cardiorespiratory Research Group, Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK
Franciele M. Vanderlei
Affiliation:
Department of Physiotherapy, Sao Paulo State University, UNESP, Presidente Prudente, Brazil
Vitor E. Valenti
Affiliation:
Autonomic Nervous System Center, Sao Paulo State University, UNESP, Presidente Prudente, Brazil
Luiz Carlos M. Vanderlei*
Affiliation:
Department of Physiotherapy, Sao Paulo State University, UNESP, Presidente Prudente, Brazil
*
Author for correspondence: L. C. M. Vanderlei, Department of Physiotherapy, Sao Paulo State University, UNESP, Rua Roberto Simonsen, 305 – Centro Educacional, Presidente Prudente 19060-900, Brazil. Tel: +55 (18) 3229-5388; Fax: +55 (18) 3229-5389; E-mail: [email protected]

Abstract

Background:

In this study, we applied ultra-short time series of interbeat intervals (RR-intervals) to evaluate heart rate variability through default chaotic global techniques with the purpose of discriminating obese youths from non-obese youth patients.

Method:

Chaotic global analysis of the RR-intervals from the electrocardiogram and pre-processing adjustments was undertaken. The effect of cubic spline interpolations was assessed, while the spectral parameters remained fixed. Exactly, 125 RR-intervals of data were recorded.

Results:

CFP1, CFP3, and CFP6 were the only significant combinations of chaotic globals when the standard conditions were enforced and at the level p<0.01 (or <1%). These significances were acheived via Kruskal–Wallis and Cohen’s ds effects sizes tests of significance after Anderson–Darling and Lilliefors statistical tests indicated non-normal distributions in the majority of cases. Adjustments of the cubic spline interpolation from 1 to 13 Hz were revealed to be inconsequential when measured by Kruskal–Wallis and Cohen’s ds, regarding the outcome between the two datasets.

Conclusion:

Chaotic global analysis was offered as a robust technique to distinguish autonomic dysfunction in obese youths. It can discriminate the two different groups using ultra-short data lengths, and no cubic spline interpolations need be applied.

Type
Original Article
Copyright
© Cambridge University Press 2019 

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

Kleiger, RE, Miller, JP, Bigger, JT, Moss, AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Card 1987; 59: 256262.CrossRefGoogle ScholarPubMed
Campos, LA, Pereira, VL , Jr., Muralikrishna, A, Albarwani, S, Brás, S, Gouveia, S. Mathematical biomarkers for the autonomic regulation of cardiovascular system. Front Physiol 2013; 4: 279.10.3389/fphys.2013.00279CrossRefGoogle ScholarPubMed
Wiertel-Krawczuk, A, Hirschfeld, AS, Huber, J, Wojtysiak, M, Szymankiewicz-Szukała, A. Sympathetic skin response following single and combined sound and electrical stimuli in young healthy subjects. J Med Sc 2016; 85: 106113.CrossRefGoogle Scholar
Baum, P, Petroff, D, Classen, J, Kiess, W, Bluher, S. Dysfunction of autonomic nervous system in childhood obesity: a cross-sectional study. PLoS One 2013; 8: e54546.10.1371/journal.pone.0054546CrossRefGoogle ScholarPubMed
Goldberger, AL. Cardiac chaos. Science 1989; 243: 1419.CrossRefGoogle ScholarPubMed
Goldberger, AL, West, BJ. Chaos and order in the human body. MD Comput 1992; 9:2534.Google ScholarPubMed
Vanderlei, FM, Vanderlei, LCM, Garner, DM. Heart rate dynamics by novel chaotic globals to HRV in obese youths. J Hum Growth Develop 2015; 25: 8288.CrossRefGoogle Scholar
Garner, DM, Vanderlei, FM, Vanderlei, LCM. Complex measurements of heart rate variability in obese youths: distinguishing autonomic dysfunction. J Hum Growth Develop 2018; 28: 298306.CrossRefGoogle Scholar
McKinley, S, Levine, M. Cubic spline interpolation. Coll Red 1998; 45: 10491060.Google Scholar
Mackey, MC, Milton, JG. Dynamical diseases. Ann N Y Acad Sci 1987; 504: 1632.CrossRefGoogle ScholarPubMed
Bélair, J, Glass, L, An der Heiden, U, Milton, J. Dynamical disease: identification, temporal aspects and treatment strategies of human illness. Chaos 1995; 5: 17.CrossRefGoogle ScholarPubMed
Seiver, A, Daane, S, Kim, R. Regular low frequency cardiac output oscillations observed in critically ill surgical patients. Complexity 1997; 2: 5155.3.0.CO;2-S>CrossRefGoogle Scholar
Kawaguchi, M, Takamatsu, I, Kazama, T. Rocuronium dose-dependently suppresses the spectral entropy response to tracheal intubation during propofol anaesthesia. Br J Anaesth 2009; 102: 667672.10.1093/bja/aep040CrossRefGoogle ScholarPubMed
Alvarez, D, Hornero, R, Marcos, J, Del Campo, F, Lopez, M. Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 400403.Google ScholarPubMed
Bokov, P, Fiamma, MN, Chevalier-Bidaud, B, et al. Increased ventilatory variability and complexity in patients with hyperventilation disorder. J Appl Physiol 2016; 120: 11651172.10.1152/japplphysiol.00859.2015CrossRefGoogle ScholarPubMed
Grogono, JC, Butler, C, Izadi, H, Moosavi, SH. Inhaled furosemide for relief of air hunger versus sense of breathing effort: a randomized controlled trial. Respir Res 2018; 19: 181.CrossRefGoogle ScholarPubMed
Garner, DM, Ling, BWK. Measuring and locating zones of chaos and irregularity. J Syst Sci Complex 2014; 27: 494506.CrossRefGoogle Scholar
Ghil, M. The SSA-MTM toolkit: applications to analysis and prediction of time series. Appl Soft Comp 1997; 3165: 216230.CrossRefGoogle Scholar
Garner, DM, De Souza, NM, Vanderlei, LCM. Risk assessment of diabetes mellitus by chaotic globals to heart rate cariability via six power spectra. Rom J Diabetes Nut Met Dis 2017; 24: 227236.Google Scholar
Slepian, S. Prolate spheroidal wave functions, Fourier analysis and uncertainty, V, The discrete case. Bell Syst Tech J 1978; 57: 13711430.CrossRefGoogle Scholar
Shannon, CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27: 379423.CrossRefGoogle Scholar
Peng, CK, Havlin, S, Stanley, HE, Goldberger, AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995; 5: 8287.CrossRefGoogle ScholarPubMed
Wajnsztejn, R, De Carvalho, TD, Garner, DM, et al. Heart rate variability analysis by chaotic global techniques in children with attention deficit hyperactivity disorder. Complexity 2016; 21: 412419.CrossRefGoogle Scholar
Jolliffe, IT. Principal Component Analysis, Springer Series in Statistics, vol. 2nd edn. Springer, New York, 2002.Google Scholar
Manly, BF. Multivariate Statistical Methods: A Primer. CRC Press, Boca Raton, 2004.CrossRefGoogle Scholar
Sullivan, GM, Feinn, R. Using effect size—or why the P value is not enough. J Grad Med Ed 2012; 4: 279282.CrossRefGoogle Scholar
Coe, R. It’s the Effect Size, Stupid: what effect size is and why it is important. British Educational Research Association annual conference. 2002; 1–18. Accessed at: https://www.cem.org/attachments/ebe/ESguide.pdf Google Scholar
Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol 2013; 4: 863.CrossRefGoogle ScholarPubMed
Sawilowsky, SS. New Effect Size Rules of Thumb 2009; 8: 597–599.Google Scholar
Baselli, G, Cerutti, S, Civardi, S, et al. Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. Int J Biom Comp 1987; 20: 5170.CrossRefGoogle ScholarPubMed
Vanderlei, FM, Vanderlei, LC, Garner, DM. Chaotic global parameters correlation with heart rate variability in obese children. J Hum Growth Develop 2014; 24: 2430.CrossRefGoogle Scholar
Souza, NM, Vanderlei, LC, Garner, DM. Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV. Complexity. 2015; 20: 8492.CrossRefGoogle Scholar
Antonio, AMS, Garner, DM, Cardoso, MA, et al. Behaviour of globally chaotic parameters of heart rate variability following a protocol of exercise with flexible pole. Russ J Cardiol 2015; 4: 2428.Google Scholar
Mateo, J, Laguna, P. Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model. IEEE Trans Biomed Eng 2000; 47: 985996.CrossRefGoogle ScholarPubMed
DeBoer, RW, Karemaker, JM, Strackee, J. Comparing spectra of a series of point events particularly for heart rate variability data. IEEE Trans Biomed Eng 1984; 31(4): 384387.CrossRefGoogle ScholarPubMed
Lomb, NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 1976; 39: 447462.CrossRefGoogle Scholar
Kreyszig, E. Advanced Engineering Mathematics. Wiley, Hoboken, 2011.Google Scholar
Camm, AJ, Malik, M, Bigger, JT, et al. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996; 93: 10431065.Google Scholar
Tarvainen, MP, Niskanen, JP, Lipponen, JA, Ranta-Aho, PO, Karjalainen, PA. Kubios HRV–heart rate variability analysis software. Comp Meth Prog Biomed 2014; 113: 210220.CrossRefGoogle ScholarPubMed
Anderson, TW, Darling, DA. A test of goodness of fit. J Am Stat Assoc 1954; 49: 765769.CrossRefGoogle Scholar
Razali, NM, Wah, YB. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J Stat Mod Anal 2011; 2: 2133.Google Scholar
Vanderlei, F, Vanderlei, LCM, de Abreu, LC, Garner, DM. Entropic analysis of HRV in obese children. Int Arch Med 2015; 8(200): 19.Google Scholar
Garner, DM, Souza, NM, Vanderlei, LCM. Heart rate variability analysis: Higuchi and Katz’s fractal dimensions in subjects with type 1 diabetes mellitus. Rom J Diabetes Nut Met Dis 2018; 25: 289.Google Scholar
Sassi, R, Cerutti, S, Lombardi, F, 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. Ep Europace 2015; 17: 13411353.10.1093/europace/euv015CrossRefGoogle Scholar
Barreto, GS, Vanderlei, FM, Vanderlei, LCM, Garner, DM. Risk appraisal by novel chaotic globals to HRV in subjects with malnutrition. J Hum Growth Develop 2014; 24: 243248.CrossRefGoogle Scholar
Van Leeuwen, P, Bettermann, H, An der, HU, Kummell, HC. Circadian aspects of apparent correlation dimension in human heart rate dynamics. Am J Physiol 1995; 269: H130H134.Google ScholarPubMed