Advertisement

Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables

  • Rosangela Akemi Hoshi
    Correspondence
    Corresponding author at: FAMERP - São José do Rio Preto Medicine School, PhD Program. Av. Brigadeiro Faria Lima, n 5416, Vila São Pedro. CEP: 15090-000 São José do Rio Preto, São Paulo, Brazil. Tel.: (+5517) 3201 5927.
    Affiliations
    FAMERP - São José do Rio Preto Medicine School, Cardiology and Cardiovascular Surgery Departament, Transdisciplinary Nucleus of Studies on Complexity and Chaos(NUTECC), São José do Rio Preto, São Paulo, Brazil
    Search for articles by this author
  • Carlos Marcelo Pastre
    Affiliations
    UNESP - Univ Estadual Paulista, Physical Therapy Departament, Presidente Prudente Campus, São Paulo, Brazil
    Search for articles by this author
  • Luiz Carlos Marques Vanderlei
    Affiliations
    UNESP - Univ Estadual Paulista, Physical Therapy Departament, Presidente Prudente Campus, São Paulo, Brazil
    Search for articles by this author
  • Moacir Fernandes Godoy
    Affiliations
    FAMERP - São José do Rio Preto Medicine School, Cardiology and Cardiovascular Surgery Departament, Transdisciplinary Nucleus of Studies on Complexity and Chaos(NUTECC), São José do Rio Preto, São Paulo, Brazil
    Search for articles by this author

      Abstract

      The Poincaré plot for heart rate variability analysis is a technique considered geometrical and non-linear, that can be used to assess the dynamics of heart rate variability by a representation of the values of each pair of R–R intervals into a simplified phase space that describes the system's evolution. The aim of the present study was to verify if there is some correlation between SD1, SD2 and SD1/SD2 ratio and heart rate variability nonlinear indexes either in disease or healthy conditions. 114 patients with arterial coronary disease and 65 healthy subjects underwent 30 minute heart rate registration, in supine position and the analyzed indexes were as follows: SD1, SD2, SD1/SD2, Sample Entropy, Lyapunov Exponent, Hurst Exponent, Correlation Dimension, Detrended Fluctuation Analysis, SDNN, RMSSD, LF, HF and LF/HF ratio. Correlation coefficients between SD1, SD2 and SD1/SD2 indexes and the other variables were tested by the Spearman rank correlation test and a regression analysis. We verified high correlation between SD1/SD2 index and HE and DFA (α1) in both groups, suggesting that this ratio can be used as a surrogate variable.

      Jel classification

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Autonomic Neuroscience: Basic and Clinical
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Acharya U.R.
        • Lim C.M.
        • Joseph P.
        Heart rate variability analysis using correlation dimension and detrended fluctuation analysis.
        ITBM-RBM. 2002; 23: 333-339
        • Acharya U.R.
        • Kannathal N.
        • Sing O.W.
        • Ping L.W.
        • Chua T.
        Heart rate analysis in normal subjects of various age groups.
        BioMed. Eng. OnLine. 2004; 3: 24-31
        • Acharya U.R.
        • Joseph K.P.
        • Kannathal N.
        • Lim C.M.
        • Suri J.S.
        Heart rate variability: a review.
        Med. Biol. Eng. Comput. 2006; 44: 1031-1051
        • Bastos F.N.
        • Vanderlei L.C.
        • Nakamura F.Y.
        • Bertollo M.
        • Godoy M.F.
        • Hoshi R.A.
        • Junior J.N.
        • Pastre C.M.
        Effects of cold water immersion and active recovery on post-exercise heart rate variability.
        Int. J. Sports Med. 2012; 33: 873-879
        • Brennan M.
        • Palaniswami M.
        • Kamen P.
        Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?.
        IEEE Trans. Biomed. Eng. 2001; 48: 1342-1347
        • Buchheit M.
        • Laursen P.B.
        • Ahmaidi S.
        Parasympathetic reactivation after repeated sprint exercise.
        Am. J. Physiol. Heart Circ. Physiol. 2007; 293: H133-H141
        • Carvalho T.D.
        • et al.
        Fractal correlation property of heart rate variability in chronic obstructive pulmonary disease.
        Int. J. COPD. 2011; 6: 23-28
        • Chappell D.
        • Panagiotidis T.
        Using the Correlation Dimension to Detect Non-linear Dynamics. Evidence from the Athens Stock Exchange.
        ([Online] Available at:) (Accessed 11 October 2012)
        • Corrêa P.R.
        • Catai A.M.
        • Takakura I.T.
        • Machado M.N.
        • Godoy M.F.
        Heart rate variability and pulmonary infections after myocardial revascularization.
        Arq. Bras. Cardiol. 2010; 95: 448-456
        • DePetrillo P.B.
        • Speers A.
        • Ruttimann U.E.
        Determining the Hurst exponent of fractal series and its application to electrocardiographic analysis.
        Comp. Biol. Med. 1999; 29: 393-406
        • De Vito G.
        • Galloway S.D.
        • Nimmo M.A.
        • Maas P.
        • McMurray J.J.
        Effects of central sympathetic inhibition on heart rate variability during steady-state exercise in healthy humans.
        Clin. Physiol. Funct. Imaging. 2002; 22: 32-38
        • Eckmann J.P.
        • Ruelle D.
        Ergodic theory of chaos and strange attractors.
        Rev. Mod. Phys. 1985; 57 (effect of endurance training. Eur. J. Appl. Physiol. 91, 79–87): 617-656
        • Foss J.M.
        • Apkarian A.V.
        • Chialvo D.R.
        Dynamics of pain: fractal dimension of temporal variability of spontaneous pain differentiates between pain states.
        J. Neurophysiol. 2006; 95: 730-736
        • Guzzetti S.
        • Signorini M.G.
        • Cogliati C.
        • Mezzetti S.
        • Porta A.
        • Cerutti S.
        • Malliani A.
        Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients.
        Cardiovasc. Res. 1996; 31: 441-446
        • Huikuri H.
        • Makikallio T.H.
        • Perkiomaki J.
        Measurement of heart rate variability by methods based on nonlinear dynamics.
        J. Electrocardiol. 2003; 36 (Suppl.): 95-99
        • Javorka M.
        • Zila I.
        • Balhárek T.
        • Javorka M.
        Heart rate recovery after exercise: relations to heart rate variability and complexity.
        Braz. J. Med. Biol. Res. 2002; 32: 991-1000
        • Karmakar C.K.
        • Khandoker A.H.
        • Gubbi J.
        • Palaniswami M.
        Complex correlation measure: a novel descriptor for Poincaré plot.
        BioMed. Eng. OnLine. 2009; 8: 17
        • Karmakar C.K.
        • Gubbi J.
        • Khandoker A.H.
        • Palaniswami M.
        Analyzind temporal Variabililty of standard descriptors of Poincaré plots.
        J. Electrocardiol. 2010; 43: 719-724
        • Kikuchi A.
        • Shimizu T.
        • Hayashi A.
        • Horikoshi T.
        • Unno N.
        • Kozuma S.
        • Taketani Y.
        Nonlinear analyses of heart rate variability in normal and growth-restricted fetuses.
        Early Hum. Dev. 2006; 82: 217-226
        • Kim K.K.
        • Baek H.J.
        • Lim Y.G.
        • Park K.S.
        Effect of missing RR-interval data on nonlinear heart rate variability analysis.
        Comp. Methods Prog. Biomed. 2012; 106: 210-218
        • Krstacic G.
        • Martinis M.
        • Vargovic E.
        • Knezevic A.
        • Krstacic A.
        Non-linear dynamics in patients with stable angina pectoris.
        in: Presented at Computers in Cardiology. 2001: 23-26 (Rotterdam: The Netherlands)
        • Krstacic G.
        • Krstacic A.
        • Smalcelj A.
        • Milicic D.
        • Jembrek-Gostovic M.
        The “Chaos Theory” and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?.
        Ann. Noninvasive Electrocardiol. 2007; 12: 130-136
        • Krstacic G.
        • Parati G.
        • Gamberger D.
        • Castiglioni P.
        • Krstacic A.
        • Steiner R.
        Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy.
        Med. Biol. Eng. Comput. 2012; 50: 1037-1046
        • Lerma C.
        • Infante O.
        • Pérez-Grovas H.
        • José M.V.
        Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients.
        Clin. Physiol. Funct. Imaging. 2003; 23: 72-80
        • Lewis M.J.
        • Short A.L.
        Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise.
        Physiol. Meas. 2007; 28: 731-744
        • Maestri R.
        • et al.
        Nonlinear indices of heart rate variability in chronic heart failure patients: redundancy and comparative clinical value.
        J. Cardiovasc. Electrophysiol. 2007; 18: 425-433
        • Magagnin V.
        • Bassani T.
        • Bari V.
        • Turiel M.
        • Maestri R.
        • Pinna G.D.
        • Porta A.
        Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices.
        Physiol. Meas. 2011; 32: 1775-1786
        • Mäkikallio T.H.
        • Tulppo M.P.
        • Seppänen T.
        • Huikuri H.V.
        Analysis of nonlinear heart rate dynamics in cardiac arrhythmias.
        Herzschr. Elektrophys. 2000; 11: 131-138
        • Mourot L.
        • Bouhaddi M.
        • Perrey S.
        • Cappelle S.
        • Henriet M.T.
        • Wolf J.P.
        • Rouillon J.D.
        • Regnar J.
        Decrease in heart rate variability with overtraining: assessment by the Poincare´ plot analysis.
        Clin. Physiol. Funct. Imaging. 2004; 24: 10-18
        • Mourot L.
        • Bouhaddi M.
        • Perrey S.
        • Rouillon J.D.
        • Regnard J.
        Quantitative Poincaré plot analysis of heart rate variability: effect of endurance training.
        Eur. J. Appl. Physiol. 2004; 91: 79-87
        • Niskanen J.-P.
        • Tarvainen M.P.
        • Ranta-aho P.O.
        • Karjalainen P.A.
        Software for advanced HRV analysis.
        Comput. Methods Prog. Biomed. 2004; 76: 73-81
        • Peng C.K.
        • Havlin S.
        • Stanley H.E.
        • Goldberger A.L.
        Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.
        Chaos. 1995; 5: 82-87
        • Penttilä J.
        • Helminen A.
        • Jartti T.
        • Kuusela T.
        • Huikuri H.V.
        • Tulppo M.P.
        • Scheinin H.
        Effect of cardiac vagal outflow on complexity and fractal correlation properties of heart rate dynamics.
        Auton. Autacoid Pharmacol. 2003; 23: 173-179
        • Porta A.
        • D'Addio G.
        • Guzzetti S.
        • Lucini D.
        • Pagani M.
        Testing the presence of non stationarities in short heart rate variability series.
        Comput. Cardiol. 2004; 31: 645-648
        • Porta A.
        • Gnecchi-Ruscone T.
        • Tobaldini E.
        • Guzzetti S.
        • Furlan R.
        • Montano N.
        Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt.
        J. Appl. Physiol. 2007; 103: 1143-1149
        • RaRao R.K.A.
        • Yeragani V.K.
        Decreased chaos and increased nonlinearity of heart rate time series in patients with panic disorder.
        Auton. Neurosci. 1999; 88: 99-108
        • Richman J.S.
        • Moorman J.R.
        Physiological time-series analysis using approximante entropy and sample entropy.
        Am. J. Physiol. Heart Circ. Physiol. 2000; 278: H2039-H2049
        • Stadnitski T.
        Measuring fractality.
        Front. Physiol. 2012; 3: 1-13
        • Task Force of ESC
        • NASPE
        Heart rate variability, standards of measurement, physiological interpretation, and clinical use.
        Circulation. 1996; 17: 354-381
        • Todder D.
        • Bersudsky Y.
        • Cohen H.
        Nonlinear analysis of RR interval in euthymic bipolar disorder.
        Auton. Neurosci. 2005; 117: 127-131
        • Tulppo M.P.
        • Makikallio T.H.
        • Takala T.E.
        • Seppanen T.
        • Huikuri H.V.
        Quantitative beat-to-beat analysis of heart rate dynamics during exercise.
        Am. J. Physiol. 1996; 271: H244-H252
        • Tulppo M.P.
        • Mäkikallio T.H.
        • Seppänen T.
        • Laukkanen R.T.
        • Huikuri H.V.
        Vagal modulation of heart rate during exercise: effects of age and physical fitness.
        Am. J. Physiol. Heart Circ. Physiol. 1998; 274: H424-H429
        • Vanderlei L.C.M.
        • Pastre C.M.
        • Hoshi R.A.
        • Carvalho T.D.
        • Godoy M.F.
        Noções básicas de variabilidade da frequência cardíaca e sua aplicabilidade clínica.
        Rev. Bras. Cir. Cardiovasc. 2009; 24: 205-217
        • Vuksanovic V.
        • Gal V.
        Nonlinear and chaos characteristics of heart period time series: healthy aging and postural change.
        Auton. Neurosci. 2005; 121: 94-100
        • Wagner C.D.
        • Persson P.B.
        Onlinear chaotic dynamics of arterial blood pressure and renal blood flow.
        Am. J. Physiol. 1985; 268: H621-H627
        • Woo M.A.
        • Stevenson W.G.
        • Moser D.K.
        • Trelease R.B.
        • Harper R.M.
        Patterns of beat-to-beat heart rate variability in advanced heart failure.
        Am. Heart J. 1992; 123: 704-710