Short-term complexity indexes of heart period and systolic arterial pressure variabilities provide complementary information.

It is unclear whether the complexity of the variability of the systolic arterial pressure (SAP) provides complementary information to that of the heart period (HP). The complexity of HP and SAP variabilities was assessed from short beat-to-beat recordings (i.e., 256 cardiac beats). The evaluation was made during a pharmacological protocol that induced vagal blockade with atropine or a sympathetic blockade (beta-adrenergic blockade with propranolol or central sympathetic blockade with clonidine) alone or in combination, during a graded head-up tilt, and in patients with Parkinson's disease (PD) without orthostatic hypotension undergoing orthostatic challenge. Complexity was quantified according to the mean square prediction error (MSPE) derived from univariate autoregressive (AR) and multivariate AR (MAR) models. We found that: 1) MSPE(MAR) did not provide additional information to that of MSPE(AR); 2) SAP variability was less complex than that of HP; 3) because HP complexity was reduced by either vagal blockade or vagal withdrawal induced by head-up tilt and was unaffected by beta-adrenergic blockade, HP was under vagal control; 4) because SAP complexity was increased by central sympathetic blockade and was unmodified by either vagal blockade or vagal withdrawal induced by head-up tilt, SAP was under sympathetic control; 5) SAP complexity was increased in patients with PD; and 6) during orthostatic challenge, the complexity of both HP and SAP variabilities in patients with PD remained high, thus indicating both vagal and sympathetic impairments. Complexity indexes derived from short HP and SAP beat-to-beat series provide complementary information and are helpful in detecting early autonomic dysfunction in patients with PD well before circulatory symptoms become noticeable.

[1]  Niels Wessel,et al.  Addressing the complexity of cardiovascular regulation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  H. P. Koepchen,et al.  Physiology of Rhythms and Control Systems: An Integrative Approach , 1991 .

[3]  Fumitoshi Niwa,et al.  Circadian rhythm of rest activity and autonomic nervous system activity at different stages in Parkinson's disease , 2011, Autonomic Neuroscience.

[4]  R. Hughson,et al.  On the fractal nature of heart rate variability in humans: effects of data length and beta-adrenergic blockade. , 1994, The American journal of physiology.

[5]  C. Peng,et al.  Cardiac interbeat interval dynamics from childhood to senescence : comparison of conventional and new measures based on fractals and chaos theory. , 1999, Circulation.

[6]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[7]  M. Turiel,et al.  Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as a Marker of Sympatho‐Vagal Interaction in Man and Conscious Dog , 1986, Circulation research.

[8]  H. Akaike.  A new look at the statistical model identification , 1974 .

[9]  L. Metman,et al.  Cardiac Sympathetic Denervation in Parkinson Disease , 2000, Annals of Internal Medicine.

[10]  Dirk Hoyer,et al.  Altered diurnal autonomic variation and reduced vagal information flow in acute schizophrenia , 2006, Clinical Neurophysiology.

[11]  A. Porta,et al.  Early Abnormalities of Vascular and Cardiac Autonomic Control in Parkinson’s Disease Without Orthostatic Hypotension , 2007, Hypertension.

[12]  N. Wessel,et al.  Short-term forecasting of life-threatening cardiac arrhythmias based on symbolic dynamics and finite-time growth rates. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  Alberto Porta,et al.  Short-Term Complexity of Cardiac Autonomic Control during Sleep: REM as a Potential Risk Factor for Cardiovascular System in Aging , 2011, PloS one.

[14]  G G Haddad,et al.  Heart rate control in normal and aborted-SIDS infants. , 1993, The American journal of physiology.

[15]  A. Malliani,et al.  Cardiovascular Neural Regulation Explored in the Frequency Domain , 1991, Circulation.

[16]  R. Hughson,et al.  Spontaneous cardiac baroreflex in humans. Comparison with drug-induced responses. , 1995, Hypertension.

[17]  H. Reichmann,et al.  Cardiovascular autonomic dysfunction in Parkinson's disease , 2010, Journal of the Neurological Sciences.

[18]  A. Porta,et al.  Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. , 2007, Journal of applied physiology.

[19]  N. Montano,et al.  Complexity and Nonlinearity in Short-Term Heart Period Variability: Comparison of Methods Based on Local Nonlinear Prediction , 2007, IEEE Transactions on Biomedical Engineering.

[20]  T Seppänen,et al.  Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics. , 2001, American journal of physiology. Heart and circulatory physiology.

[21]  A. Porta,et al.  Causal relationships between heart period and systolic arterial pressure during graded head-up tilt. , 2011, American journal of physiology. Regulatory, integrative and comparative physiology.

[22]  D L Eckberg,et al.  Temporal response patterns of the human sinus node to brief carotid baroreceptor stimuli. , 1976, The Journal of physiology.

[23]  J. Paton,et al.  The yin and yang of cardiac autonomic control: Vago-sympathetic interactions revisited , 2005, Brain Research Reviews.

[24]  H. Huikuri,et al.  Ambulatory ECG and analysis of heart rate variability in Parkinson's disease , 2001, Journal of neurology, neurosurgery, and psychiatry.

[25]  G Baselli,et al.  Assessing baroreflex gain from spontaneous variability in conscious dogs: role of causality and respiration. , 2000, American journal of physiology. Heart and circulatory physiology.

[26]  Can Ozan Tan,et al.  Fractal properties of human heart period variability: physiological and methodological implications , 2009, The Journal of physiology.

[27]  A. Goldberger.  Fractal Variability Versus Pathologic Periodicity: Complexity Loss and Stereotypy in Disease , 1997, Perspectives in biology and medicine.

[28]  A. Porta,et al.  Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. , 1994, Circulation.

[29]  Alberto Porta,et al.  Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. , 2007, American journal of physiology. Heart and circulatory physiology.

[30]  G. Moody,et al.  Clinical Validation of the ECG-Derived Respiration (EDR) Technique , 2008 .

[31]  G. Breithardt,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. , 1996 .

[32]  A Voss,et al.  Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders. , 2006, Bipolar disorders.

[33]  Luca Faes,et al.  Mutual nonlinear prediction as a tool to evaluate coupling strength and directionality in bivariate time series: comparison among different strategies based on k nearest neighbors. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  RaffaelloFurlan,et al.  Oscillatory Patterns in Sympathetic Neural Discharge and Cardiovascular Variables During Orthostatic Stimulus , 2000 .

[35]  O. Rascol,et al.  Orthostatic hypotension in Parkinson's disease , 2013 .

[36]  P. Berlit,et al.  Sympathetic cardiovascular dysfunction in long-standing idiopathic Parkinson's disease , 1997, Clinical Autonomic Research.

[37]  D L Eckberg,et al.  Human responses to upright tilt: a window on central autonomic integration , 1999, The Journal of physiology.

[38]  D. T. Kaplan,et al.  Aging and the complexity of cardiovascular dynamics. , 1991, Biophysical journal.

[39]  J. Taylor,et al.  Short‐term cardiovascular oscillations in man: measuring and modelling the physiologies , 2002, The Journal of physiology.

[40]  Giuseppe Baselli,et al.  Coupling arterial windkessel with peripheral vasomotion: modeling the effects on low-frequency oscillations , 2006, IEEE Transactions on Biomedical Engineering.

[41]  C. Peng,et al.  Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. , 1996, The American journal of physiology.

[42]  S M Pincus,et al.  Greater signal regularity may indicate increased system isolation. , 1994, Mathematical biosciences.

[43]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[44]  J. Kurths,et al.  The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. , 1996, Cardiovascular research.

[45]  K Kato,et al.  On the fractal nature of heart rate variability in humans: effects of vagal blockade. , 1995, The American journal of physiology.

[46]  Pere Caminal,et al.  Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects , 2009, IEEE Transactions on Biomedical Engineering.

[47]  K. Bär,et al.  The altered complexity of cardiovascular regulation in depressed patients , 2010, Physiological measurement.

[48]  G. Baselli,et al.  Spectral decomposition in multichannel recordings based on multivariate parametric identification , 1997, IEEE Transactions on Biomedical Engineering.

[49]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[50]  M. Javorka,et al.  Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus , 2008, Clinical Neurophysiology.

[51]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[52]  T Penzel,et al.  Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[53]  U. Tolonen,et al.  Heart rate variability in patients with untreated Parkinson’s disease , 2000, European journal of neurology.

[54]  M. Javorka,et al.  The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure. , 2011, Physiological measurement.

[55]  T. Seppänen,et al.  Physiological Background of the Loss of Fractal Heart Rate Dynamics , 2005, Circulation.

[56]  R. Hughson,et al.  Fractal nature of short-term systolic BP and HR variability during lower body negative pressure. , 1994, The American journal of physiology.

[57]  D. Goldstein,et al.  Dysautonomia in Parkinson's disease: neurocardiological abnormalities , 2003, The Lancet Neurology.

[58]  L. Quintin,et al.  Cardioinhibitory actions of clonidine assessed by cardiac vagal motoneuron recordings , 2008, Journal of hypertension.

[59]  Alberto Porta,et al.  Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis , 2012, Internal and Emergency Medicine.

[60]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[61]  T Seppänen,et al.  Effects of pharmacological adrenergic and vagal modulation on fractal heart rate dynamics. , 2001, Clinical physiology.

[62]  D. Goldstein,et al.  Orthostatic hypotension from sympathetic denervation in Parkinson’s disease , 2002, Neurology.

[63]  Robert A. Hauser,et al.  Autonomic nervous system dysfunction in Parkinson’s disease , 2003, Current treatment options in neurology.

[64]  Giuseppe Baselli,et al.  Prediction of short cardiovascular variability signals based on conditional distribution , 2000, IEEE Transactions on Biomedical Engineering.

[65]  Giuseppe Baselli,et al.  Modelling and disentangling physiological mechanisms: linear and nonlinear identification techniques for analysis of cardiovascular regulation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[66]  A. Malliani,et al.  Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences , 1994, Medical and Biological Engineering and Computing.

[67]  M. Masè,et al.  An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans. , 2007, Chaos.

[68]  A. Porta,et al.  RT variability unrelated to heart period and respiration progressively increases during graded head-up tilt. , 2010, American journal of physiology. Heart and circulatory physiology.

[69]  G. Parati,et al.  Scale exponents of blood pressure and heart rate during autonomic blockade as assessed by detrended fluctuation analysis , 2011, The Journal of physiology.