Abstract
From the point of view of Complexity Sciences, health can be considered as the state of dynamical balance between robustness and adaptability to the changes in the environment. We consider that any human disease can be found in physiological time series by deviations from this point that reflects the loss of this balance. Thus, it is possible to find biomarkers based on non-invasive physiological parameters that characterize the critical healthy state, and could help as early warnings auxiliary for clinical diagnoses of different diseases. In this work, we present a time-domain analysis using the distribution moments, autocorrelation function, Poincaré diagrams, and the spectral analysis of interbeat intervals and blood pressure time series for control subjects of different age and gender, and diabetic patients. As a preliminary result, a statistical significant difference was found between health and disease in the statistical moments of blood pressure and heart rate variability that can be proposed as biomarkers.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Porta A, Bari V, Ranuzzi G et al (2017) Assessing multiscale complexity of short heart rate variability series through a model-based linear approach. Chaos Interdiscip. J Nonlinear Sci 27:093901
Valenza G, Citi L, Garcia RG et al (2017) Complexity variability assessment of nonlinear time-varying cardiovascular control. Sci Rep 7:42779. https://doi.org/10.1038/srep42779
Alberga D, Mangiatordi GF (2016) Understanding complexity of physiology by combined molecular simulations and experiments: anion channels as a proof of concept. J Physiol 594:2777–2778. https://doi.org/10.1113/JP272001
Raoufy MR, Ghafari T, Mani AR (2017) Complexity analysis of respiratory dynamics. Am J Respir Crit Care Med 196:247–248. https://doi.org/10.1164/rccm.201701-0026LE
Calvani R, Picca A, Cesari M et al (2017) Biomarkers for sarcopenia: reductionism vs. complexity. Curr Protein Pept Sci. https://doi.org/10.2174/1389203718666170516115422
Tippett LJ, Waldvogel HJ, Snell RG et al (2017) The complexity of clinical Huntington’s disease: developments in molecular genetics, neuropathology and neuroimaging biomarkers. In: Neurodegenerative diseases. Springer, Cham, pp 129–161
de la Torre-Luque A, Bornas X, Balle M, Fiol-Veny A (2016) Complexity and nonlinear biomarkers in emotional disorders: a meta-analytic study. Neurosci Biobehav Rev 68:410–422. https://doi.org/10.1016/j.neubiorev.2016.05.023
Dias LM, Thodima V, Friedman J et al (2016) Cross-platform assessment of genomic imbalance confirms the clinical relevance of genomic complexity and reveals loci with potential pathogenic roles in diffuse large B-cell lymphoma. Leuk Lymphoma 57:899–908. https://doi.org/10.3109/10428194.2015.1080364
Rivera AL, Estañol B, Fossion R et al (2016) Loss of breathing modulation of heart rate variability in patients with recent and long standing diabetes mellitus type II. PLoS One 11:e0165904. https://doi.org/10.1371/journal.pone.0165904
Medenwald D, Swenne CA, Loppnow H et al (2017) Prognostic relevance of the interaction between short-term, metronome-paced heart rate variability, and inflammation: results from the population-based CARLA cohort study. Europace 19:110–118. https://doi.org/10.1093/europace/euv333
Berntson GG, Thomas Bigger J, Eckberg DL et al (1997) Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34:623–648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x
Malik M (1996) Heart rate variability. Ann Noninvasive Electrocardiol 1:151–181. https://doi.org/10.1111/j.1542-474X.1996.tb00275.x
Whittle J (2017) Blood pressure variability predicts clinical outcomes: now what? Hypertension 69:584–586. https://doi.org/10.1161/HYPERTENSIONAHA.116.08806
Dolan E, O’Brien E (2010) Blood pressure variability: clarity for clinical practice. Hypertension 56:179–181. https://doi.org/10.1161/HYPERTENSIONAHA.110.154708
Levy MN (1971) Sympathetic-Parasympathetic interactions in the heart. Circ Res 29:437–445. https://doi.org/10.1161/01.RES.29.5.437
Rivera AL, Estañol B, Sentíes-Madrid H et al (2016) Heart rate and systolic blood pressure variability in the time domain in patients with recent and long-standing diabetes mellitus. PLoS One 11:e0148378. https://doi.org/10.1371/journal.pone.0148378
Parati G, Di Rienzo M, Mancia G (2000) How to measure baroreflex sensitivity: from the cardiovascular laboratory to daily life. J Hypertens 18:7–19
Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology (1996) Heart rate variability. Circulation 93:1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
Pagani M, Lombardi F, Guzzetti S et al (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res 59:178–193
Akselrod S, Gordon D, Ubel FA et al (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213:220–222
Acharya UR, Joseph KP, Kannathal N et al (2006) Heart rate variability: a review. Med Biol Eng Comput 44:1031–1051. https://doi.org/10.1007/s11517-006-0119-0
Kamen PW, Tonkin AM (1995) Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure. Aust NZ J Med 25:18–26. https://doi.org/10.1111/j.1445-5994.1995.tb00573.x
Brennan M, Palaniswami M, Kamen P (2001) Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng 48:1342–1347. https://doi.org/10.1109/10.959330
Ewing DJ, Martyn CN, Young RJ, Clarke BF (1985) The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care 8:491–498. https://doi.org/10.2337/diacare.8.5.491
Saul JP, Albrecht P, Berger RD, Cohen RJ (1987) Analysis of long term heart rate variability: methods, 1/f scaling and implications. Comput Cardiol 14:419–422
Kay SM, Marple SL (1981) Spectrum analysis—a modern perspective. Proc IEEE 69:1380–1419
Saykrs BM (1973) Analysis of heart rate variability. Ergonomics 16:17–32. https://doi.org/10.1080/00140137308924479
Malliani A, Pagani M, Lombardi F, Cerutti S (1991) Cardiovascular neural regulation explored in the frequency domain. Circulation 84:482–492
Pomeranz B, Macaulay RJ, Caudill MA et al (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol-Heart Circ Physiol 248:H151–H153
Kobayashi M, Musha T (1982) 1/f fluctuation of heartbeat period. IEEE Trans Biomed Eng 29:456–457
Goldberger AL, West BJ (1987) Fractals in physiology and medicine. Yale J Biol Med 60:421
Iyengar N, Peng CK, Morin R et al (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol Regul Integr Comp Physiol 271:R1078–R1084
Huikuri HV, Seppänen T, Koistinen MJ et al (1996) Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction. Circulation 93:1836–1844
Ciccone AB, Siedlik JA, Wecht JM et al (2017) Reminder: RMSSD and SD1 are identical heart rate variability metrics. Muscle Nerve 56:674–678. https://doi.org/10.1002/mus.25573
Pincus SM, Viscarello RR (1992) Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet Gynecol 79:249–255
Pincus SM, Goldberger AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol-Heart Circ Physiol 266:H1643–H1656
Mäkikallio TH, Seppänen T, Niemelä M et al (1996) Abnormalities in beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. J Am Coll Cardiol 28:1005–1011
Goldberger AL (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 347:1312–1314
Goldberger AL, Amaral LAN, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101:e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215
Bigger JT, Steinman RC, Rolnitzky LM et al (1996) Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. Circulation 93:2142–2151
Huikuri HV, Mäkikallio TH, Airaksinen KJ et al (1998) Power-law relationship of heart rate variability as a predictor of mortality in the elderly. Circulation 97:2031–2036
Yamamoto Y, Hughson RL (1991) Coarse-graining spectral analysis: new method for studying heart rate variability. J Appl Physiol 71:1143–1150
Peng C-K, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82–87
Hausdorff JM, Peng CK, Ladin ZVI et al (1995) Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J Appl Physiol 78:349–358
Shafqat K, Pal SK, Kumari S, Kyriacou PA (2009) Empirical mode decomposition (EMD) analysis of HRV data from locally anesthetized patients. In: 2009 annual international conference of the IEEE engineering in medicine and biology society, pp 2244–2247
Iatsenko D, McClintock PV, Stefanovska A (2015) Nonlinear mode decomposition: a noise-robust, adaptive decomposition method. Phys Rev E 92:032916
Morales IO, Landa E, Angeles CC et al (2015) Behavior of early warnings near the critical temperature in the two-dimensional Ising model. PLoS One 10:e0130751
Acebrón JA, Bonilla LL, Pérez Vicente CJ et al (2005) The Kuramoto model: a simple paradigm for synchronization phenomena. Rev Mod Phys 77:137–185. https://doi.org/10.1103/RevModPhys.77.137
Cumin D, Unsworth CP (2007) Generalising the Kuramoto model for the study of neuronal synchronisation in the brain. Phys Nonlinear Phenom 226:181–196. https://doi.org/10.1016/j.physd.2006.12.004
Malik M, Camm AJ (1993) Components of heart rate variability—what they really mean and what we really measure. Am J Cardiol 72:821–822
O’Brien IA, O’Hare P, Corrall RJ (1986) Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. Heart 55:348–354. https://doi.org/10.1136/hrt.55.4.348
Levy MN (1984) Cardiac sympathetic-parasympathetic interactions. Fed Proc 43:2598–2602
Horn EH, Lee ST (1965) Electronic evaluations of the fetal heart rate patterns preceding fetal death: further observation. Am J Obster Gynecol 87:824–826
Kleiger RE, Miller JP, Bigger JT, Moss AJ (1987) Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 59:256–262
Malik M, Farrell T, Cripps T, Camm AJ (1989) Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J 10:1060–1074
Thayer JF, Yamamoto SS, Brosschot JF (2010) The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol 141:122–131. https://doi.org/10.1016/j.ijcard.2009.09.543
Hellman JB, Stacy RW (1976) Variation of respiratory sinus arrhythmia with age. J Appl Physiol 41:734–738
Pikkujämsä SM, Mäkikallio TH, Sourander LB et al (1999) Cardiac interbeat interval dynamics from childhood to senescence. Circulation 100:393–399
Antelmi I, Paula RSD, Shinzato AR et al (2004) Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol 93:381–385. https://doi.org/10.1016/j.amjcard.2003.09.065
Koenig J, Thayer JF (2016) Sex differences in healthy human heart rate variability: a meta-analysis. Neurosci Biobehav Rev 64:288–310. https://doi.org/10.1016/j.neubiorev.2016.03.007
Malpas SC, Maling TJB (1990) Heart-rate variability and cardiac autonomic function in diabetes. Diabetes 39:1177–1181. https://doi.org/10.2337/diab.39.10.1177
Schmitt DT, Ivanov PC (2007) Fractal scale-invariant and nonlinear properties of cardiac dynamics remain stable with advanced age: a new mechanistic picture of cardiac control in healthy elderly. Am J Physiol Regul Integr Comp Physiol 293:R1923–R1937. https://doi.org/10.1152/ajpregu.00372.2007
Kovatchev BP, Farhy LS, Cao H et al (2003) Sample asymmetry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome. Pediatr Res 54:892–898. https://doi.org/10.1203/01.PDR.0000088074.97781.4F
Bauer A, Kantelhardt JW, Barthel P et al (2006) Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet 367:1674–1681
Robles-Cabrera A, Michel-Chavez A, Callejas-Rojas RC et al (2014) The cardiovagal, cardiosympathetic and vasosympathetic arterial baroreflexes and the neural control of short-term blood pressure. Rev Neurol 59:508–516
Vaschillo EG, Vaschillo B, Lehrer PM (2006) Characteristics of resonance in heart rate variability stimulated by biofeedback. Appl Psychophysiol Biofeedback 31:129–142
Floras JS (2013) Blood pressure variability: a novel and important risk factor. Can J Cardiol 29:557–563
Parati G, Ochoa JE, Salvi P et al (2013) Prognostic value of blood pressure variability and average blood pressure levels in patients with hypertension and diabetes. Diabetes Care 36:S312–S324
Acknowledgements
Thanks to J. A. López-Rivera for grammatical review. Financial funding for this work was supplied by Dirección General de Asuntos del Personal Académico from Universidad Nacional Autónoma de México (UNAM) grants PAPIIT IN106215, IV100116 and IA105017; grants 2015-02-1093, 2016-01-2277 and CB-2011-01-167441 from Consejo Nacional de Ciencia y Tecnología (CONACyT), and the Newton Advanced Fellowship awarded to RF by the Academy of Medical Sciences through the UK Government’s Newton Fund program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors do not have any conflict of interest.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Rivera, A.L., Estañol, B., Robles-Cabrera, A., Toledo-Roy, J.C., Fossion, R., Frank, A. (2018). Looking for Biomarkers in Physiological Time Series. In: Olivares-Quiroz, L., Resendis-Antonio, O. (eds) Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Springer, Cham. https://doi.org/10.1007/978-3-319-73975-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-73975-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73974-8
Online ISBN: 978-3-319-73975-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)