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1 Division of Cardiology, Concomitant sympathetic and vagal activation can
occur in various physiological conditions, but there is limited
information on heart rate (HR) behavior during the accentuated
sympathovagal antagonism. Beat-to-beat HR and blood pressure were
recorded during intravenous infusion of incremental doses of
norepinephrine in 18 healthy male volunteers (mean age 23 ± 5 yr).
HR and blood pressure spectra and two-dimensional Poincaré plots
were generated from the baseline recordings and from the recordings at
different doses of norepinephrine. The mean blood pressure increased
(from 90 ± 7 to 120 ± 9 mmHg,
P < 0.001), HR decreased (from 60 ± 9 to 48 ± 7 beats/min, P < 0.001), and the high-frequency spectral component of HR variability
increased (P < 0.001) during the
norepinephrine infusion as evidence of accentuated sympathovagal
interaction. Abrupt aperiodic changes in sinus intervals that were not
related to respiratory cycles or changes in blood pressure occurred in 14 of 18 subjects during the norepinephrine infusions. These
fluctuations in sinus intervals resulted in a complex or
parabola-shaped structure of the Poincaré plots of successive R-R
intervals and a widening of the high-frequency spectral peak. In four
subjects, the abrupt fluctuations in sinus intervals were followed by a
sudden onset of fixed R-R interval dynamics with a loss of respiratory
modulation of HR, resulting in a torpedo-shaped structure of the
Poincaré plots. These data show that HR behavior becomes
remarkably unstable during accentuated sympathovagal interaction,
resembling stochastic dynamics or deterministic chaotic behavior. These
features of HR dynamics can be better identified by dynamic analysis of
beat-to-beat behavior of R-R intervals than by traditional analysis
techniques of HR variability.
heart rate variability; cardiovascular regulation
POWER SPECTRAL ANALYSIS of heart rate (HR) variability
is a commonly used method in the measurement of sympathovagal
interaction on sinus node (2, 24). Because traditional analysis
techniques are insensitive to abrupt, aperiodic changes in HR dynamics,
beat-to-beat analysis techniques and other dynamic methods have been
developed to uncover stochastic or nonlinear features in HR behavior
(9, 14, 20, 25).
Reciprocal changes in sympathetic and vagal activity have been observed
in some physiological conditions, such as during passive tilt, which
can be detected by typical changes in spectral components of HR
variability (22, 26). In other physiological and pathological states,
concomitant sympathetic and vagal activation can occur, leading to
accentuated sympathovagal antagonism (6, 8, 16, 30). Acetylcholine and
norepinephrine have a complex interaction at the level of the sinus
node, resulting in a typical condition favoring the occurrence of
complex HR dynamics (17, 18). The present research was designed to
study the HR behavior during accentuated sympathovagal interaction by
generating power spectra and Poincaré plots of successive R-R
intervals at the baseline and during infusion of incremental doses of
norepinephrine in young healthy males.
Subjects and study protocol.
HR dynamics were studied in 18 healthy male volunteers (mean age 23 ± 5 yr) at rest in the supine position under quiet baseline conditions and during incremental doses of intravenous norepinephrine. Subjects with atrial or ventricular ectopic beats or those with episodes of nodal rhythm during the experiment were excluded. The
design was approved by the ethics committee of the institution, and all
subjects gave their informed consent. All the tests were performed
between 10:00 AM and 4:00 PM, and vigorous exercise, alcohol intake, or
smoking were forbidden for 48 h before the testing days. The subjects
lay in a supine position in a quiet room for 30 min before the data
collection and became accustomed to breathing at a constant
metronome-guided rate of 0.25 Hz for the duration of the experiment.
The beat-to-beat R-R intervals were recorded with a wireless HR
monitoring system having a sampling frequency of 1,000 Hz (Polar
Electro, Kempele, Finland) (27). A continuous surface electrocardiogram
was also recorded during the experiment to confirm the sinus origin of
the beats. Beat-to-beat arterial blood pressure was measured by the
Finapres finger-cuff method, the respiration was measured with a
disposable screen-type flow transducer, and the data were stored by
means of menu-driven software packages (1, 29). HR, blood pressure, and
respiration signals were fed into an analog-to-digital converter and
stored in a microcomputer for further analysis. The protocol included baseline recordings for 15 min and, during infusion of norepinephrine at constant rates of 50, 100, and 150 ng · kg
ABSTRACT
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix
References
INTRODUCTION
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Abstract
Introduction
Methods
Results
Discussion
Appendix
References
METHODS
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Abstract
Introduction
Methods
Results
Discussion
Appendix
References
1 · min1,
recordings of 15 min at each concentration. The doses of norepinephrine were based on previous studies (3) that have shown these doses to
result in plasma concentrations of norepinephrine observed under
various physiological conditions. If the blood pressure increased
>180/110 mmHg or the subjects complained of any uncomfortable symptoms during the norepinephrine infusion, the infusion was stopped
(6 subjects, see Table 1).
Table 1.
Effects of norepinephrine on HR, blood pressure, and HR variability
Analysis of R-R interval dynamics. Data analyses were performed as described in detail previously (13, 29). An autoregressive model was used to estimate the power spectrum densities of HR and blood pressure variabilities (see APPENDIX). The computer program automatically calculates the autoregressive coefficients to define the power spectrum density. The power spectra were quantified by measuring the area under two frequency bands: low-frequency power from 0.04 to 0.15 Hz, and high-frequency power from 0.15 to 0.4 Hz.
Two-dimensional return maps or Poincaré plots were generated by plotting each R-R interval as a function of its previous R-R interval and each systolic blood pressure value as a function of its previous systolic blood pressure value, respectively, obtained at the baseline and with different levels of norepinephrine infusion. Two-dimensional vector analysis was used to quantify the shape of the plots as described previously (13, 29). In this quantitative method, short-term (SD1) and long-term R-R interval variability (SD2) and the ellipse area of the plot are separately quantified. The shapes of Poincaré plots were classified as 1) a normal, comet-shaped plot, in which increasing beat-to-beat R-R interval dispersion is observed with increasing R-R intervals (SD1/SD2 >0.15); 2) a torpedo-shaped plot with small overall beat-to-beat dispersion (SD1) and without increasing dispersion at longer R-R intervals (SD1/SD2 <0.15); or 3) a complex or parabola-like plot, in which two or more distinctive limbs are separated from the main body of the plot, with at least three points included in each limb.Statistical methods. Analysis of variance for repeated measurements was used to compare the changes in HR, blood pressure, and normally distributed HR variability measures during the norepinephrine infusion. Normal Gaussian distribution of the data was verified by the Kolmogorov-Smirnov goodness-of-fit test. Whenever the data were not normally distributed (z value > 1.0 for all spectral components of HR variability), Friedman's randomized block analysis of variance followed by post hoc analysis (Wilcoxon test) was used. Differences in baseline data between the subjects with different R-R interval dynamics during the norepinephrine infusion were analyzed by a Mann-Whitney U test.
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RESULTS |
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Top
Abstract
Introduction
Methods
Results
Discussion
Appendix
References
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The mean blood pressure increased and mean HR decreased progressively
with increasing doses of norepinephrine (Table 1). The high-frequency
spectral component of HR variability increased during the initial dose
of 50 ng · kg1 · min1,
but no additional increase occurred with higher doses. No significant changes were observed in the low-frequency component of HR variability during norepinephrine infusion.
Recordings of electrocardiogram, blood pressure, and respiration and
corresponding R-R interval tachograms for one subject with typical HR
dynamics at the baseline and with incremental doses of norepinephrine
are presented in Fig. 1. Respiratory
modulation of R-R intervals and blood pressure were observed under the
baseline conditions with lengthening of R-R intervals and an increase
in blood pressure during expiration and shortening of the R-R intervals and a decrease of blood pressure during inspiration, resulting in a
discrete high-frequency spectral peak at 0.25 Hz. During the small dose
of norepinephrine (50 ng · kg1 · min1),
a pattern with abrupt lengthening of the R-R intervals followed by
gradual shortening to the baseline level (Fig.
1B) was observed without any
concomitant abrupt changes in blood pressure. These sudden changes in
HR occurred aperiodically and were not related to the frequency or
depth of respiration. At a medium dose (100 ng · kg1 · min1),
abrupt shortenings of R-R intervals were observed that again occurred
aperiodically and were not related to the respiration cycles (Fig.
1C). At a high dose of
norepinephrine (150 ng · kg1 · min1),
a periodic respiratory modulation of the R-R intervals reappeared at a
slower HR without any abrupt changes in R-R intervals.
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Abrupt changes in R-R intervals resulted in a significant widening of
the high-frequency spectral peak toward a very high frequency area (see
Fig. 3). The two-dimensional return maps showed a typical comet-shaped
plot for the baseline R-R intervals (Fig. 2A).
Abrupt episodes of HR slowing during norepinephrine infusion resulted
in a complex or an inverted parabola-like shape for the Poincaré
plot (Fig. 2B). The aperiodic
shortenings of the R-R intervals typically resulted in a horseshoe- or
parabola-like structure of the Poincaré plot (Fig.
2C). A comet-shaped or a circlelike
Poincaré plot of R-R intervals reappeared during the high dose
(150 ng · kg1 · min1)
of norepinephrine. The shape of the Poincaré plots of the blood pressure remained similar during the different doses of norepinephrine infusion (Fig. 2).
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A comet-shaped Poincaré plot of R-R intervals was observed in 14 of the 18 subjects at the baseline, whereas 4 subjects already had a parabola-like structure before the norepinephrine infusion. This latter group had slower mean HR (51 ± 9 vs. 63 ± 9 beats/min, P < 0.05) and higher high-frequency spectral component (4,892 ± 1,209 vs. 1,277 ± 838 ms2, P < 0.01) than subjects with a comet-shaped plot at the baseline. During low or medium doses of norepinephrine infusion, a parabola-like plot was observed in 12 subjects, and in 2 subjects the parabola-like structure occurred during a high dose of norepinephrine. Only four subjects had a normal comet-shaped plot at the baseline and during all phases of norepinephrine infusion. When the baseline HR and blood pressure data were compared between those subjects with a parabola-like plot and those with a comet-shaped plot during the norepinephrine infusion, no significant differences were observed in any of these data (Table 2), nor did changes in mean HR or blood pressure differ between these subjects during the norepinephrine infusion.
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In four subjects, the abrupt changes in R-R interval dynamics were followed by a sudden change into fixed R-R interval dynamics, resulting in a torpedo-shaped Poincaré plot (Fig. 3). No respiratory modulation of R-R intervals was observed during the fixed dynamics.
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Reproducibility. When HR and blood pressure dynamics were assessed twice under similar baseline conditions in four subjects with a parabola-like Poincaré plot at the baseline, all of them showed similar R-R interval dynamics during the second recording. However, in one subject who underwent four recording sessions at 1-wk intervals under similar external conditions, the parabola-like structure was not repeated during the last experiment, when completely different HR dynamics were observed (Fig. 4).
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DISCUSSION |
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Top
Abstract
Introduction
Methods
Results
Discussion
Appendix
References
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HR dynamics during accentuated sympathovagal interaction. The observations of this study show that atypical, abrupt changes occur in HR dynamics during norepinephrine infusion in young healthy males. These findings suggest that the HR may become remarkably unstable during stressful situations that result in accentuated sympathovagal antagonism.
Unstable behavior of HR may be explained by complex interaction of acetylcholine and norepinephrine at the presynaptic and postsynaptic level of sinus node (17, 18). Norepinephrine infusion causes baroreceptor-mediated vagal activation, resulting in accentuated sympathovagal interaction, as evidenced here by an increase in blood pressure, a decrease in HR, and an increase in high-frequency spectral component of HR variability. Norepinephrine and acetylcholine have different temporal influences on the basic R-R interval length; vagal effects on R-R intervals occur more rapidly than sympathetic influences (17, 18), and the beat-to-beat fluctuations in R-R intervals depend on summation and timing of the opposing effects of norepinephrine and acetylcholine on the sinus node. Abrupt changes in R-R intervals are most likely a result of sudden vagal bursts or withdrawals, respectively, during high sympathetic influences on sinus node firing. The physiological background for onset of fixed R-R interval dynamics and abrupt disappearance of respiratory modulation of HR may be explained by the saturation of the respiratory vagal modulation of sinus node during a very high tonic vagal activity (19, 21). Present observations also show that incremental doses of norepinephrine infusion seldom result in a linear slowing of HR but that the HR behavior can be described as stochastic increases or decreases in R-R intervals followed by return to control. There are also some features of deterministic chaos in HR dynamics during this experimental condition. A parabola-like or ringlike structure rather than a random distribution of the successive R-R intervals was observed in the Poincaré plots during unstable HR behavior. This type of specific structure in the return maps of successive data points has been considered to provide evidence for deterministic chaos in the experimental animal models (5, 7, 10, 12, 15, 28). Also, the occurrence of fixed beat-to-beat R-R interval dynamics after abrupt fluctuations on R-R intervals represents another feature of deterministic chaos, i.e., abrupt temporal changes as cascades to fixed dynamics (7, 11, 15). Finally, completely different R-R interval dynamics were observed in the same subject in the similar external conditions when the baseline HR dynamics were different. Dependence of system dynamics on initial conditions is also one of the typical features of deterministic chaos (7, 15). The present analysis of data may not provide definite evidence of whether the observed HR dynamics can be better described as stochastic or as having characteristics of deterministic chaos, but it nevertheless emphasizes the need for analysis of HR behavior with dynamic methods in addition to methods based on moment statistics.Analysis methods of HR dynamics during sympathovagal interaction. Spectral analysis techniques have been most commonly used in assessment of the effects of sympathovagal balance on sinus node (2, 23, 24, 26). These analysis methods are based on the assumption that reciprocal changes occur in sympathetic and vagal activity under various physiological conditions (23, 24). Present findings demonstrate that traditional measures of HR variability are not specific for measurement of accentuated sympathovagal interaction. Abrupt changes in R-R intervals resulted in a widening of the high-frequency spectral peak without consistent changes in any numerical measure of spectral components. These changes in HR dynamics could be accurately described not by quantitative two-dimensional analysis of the Poincaré plots but only by visual inspection of the plots, suggesting that the visual interpretation of the shape of the Poincaré plot is more reliable than numerical methods in revealing the atypical HR behavior during accentuated autonomic interaction. These findings support the concept that beat-to-beat dynamic analysis methods may give important physiological information on HR behavior that cannot be detected by traditional methods of HR variability based on moment statistics. From a methodological point of view, it is also important to note that abrupt changes in sinus intervals can occur in various physiological and pathological states (4, 13, 30). These changes in R-R intervals may become deleted as artifacts or ectopic beats in automatic and visual editing of R-R interval tachograms and in analysis of HR variability by some geometric methods (22).
In conclusion, the results of this study show that unstable stochastic dynamics or deterministic chaos is involved in the genesis of HR variability during accentuated sympathovagal interaction. These features of HR behavior can be better observed by dynamic beat-to-beat analysis of R-R intervals than by traditional nonspectral and spectral HR variability methods. Dynamic analysis methods may importantly increase our understanding of the physiological background for the complex behavior of HR in various conditions.| |
APPENDIX |
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Top
Abstract
Introduction
Methods
Results
Discussion
Appendix
References
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Spectrum estimation with autoregressive modeling of time series. The most popular of the time-series modeling approaches to spectral estimation is the autoregressive (AR) spectral estimation (15a). Other names by which the AR spectral estimator is known are the maximum entropy spectral estimator and the linear prediction spectral estimator.
In AR(p) modeling of time series, it is assumed that a time series can be predicted with a linear combination of past p samples
![<IT>x</IT>′[<IT>n</IT>] = − <LIM><OP>∑</OP><LL><IT>k</IT> = 1</LL><UL><IT>p</IT></UL></LIM> <IT>a</IT>[<IT>k</IT>]<IT>x</IT>[<IT>n</IT> − <IT>k</IT>]](/content/vol274/issue3/fulltext/H810/img001.gif)
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(A1) |
k] is the data sample at
n k, and
a[k]
is a coefficient to be estimated from the data. The
a[k]
coefficients are estimated from time series by solving a set of linear
equations, the so-called Yule-Walker equations. Usually a prediction at
time instant n produces a small
prediction error
u[n],
and
x[n] = x'[n] + u[n].
Many methods have been developed for solving for the Yule-Walker
equations, e.g., autocorrelation, covariance, and modified covariance
methods. We apply the Burg method, which estimates reflection
coefficients first and then uses the Levinson recursion to obtain the
AR parameter estimates. The reflection coefficients are estimated by
minimizing the average of the estimates of the forward and backward
prediction error powers. The Burg estimate is the only one of a large
class of estimates that maintains the minimum-phase property. A
drawback is that line splitting in spectrum may occur if too large a
model order p is used in the
AR(p) model. It is therefore
recommended that model order p should
not exceed one-third of the data size. However,
p should be at least twice the number
of distinct frequency components in the time series.
After the data series is modeled, power spectral density can be
computed straightforwardly from the model
![<IT>P</IT>( <IT>f</IT> ) = <FR><NU>&sfgr;<SUP>2</SUP></NU><DE><FENCE>1 + <LIM><OP>∑</OP><LL><IT>k</IT> = 1</LL><UL><IT>p</IT></UL></LIM> <IT>a</IT>[<IT>k</IT>]<IT>z</IT><SUP>−<IT>k</IT></SUP>[<IT>n</IT> − <IT>k</IT>]</FENCE><SUP>2</SUP></DE></FR>](/content/vol274/issue3/fulltext/H810/img002.gif)
|
(A2) |
2 is the variance of the
driving noise of the model
u[n],
and z = exp( j2
f ),
where j = 
.
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ACKNOWLEDGEMENTS |
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This work is supported by grants from the Foundation for Cardiovascular Research (Helsinki, Finland) and from the Instrumentarium Research Foundation (Helsinki, Finland).
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FOOTNOTES |
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Address for reprint requests: H. V. Huikuri, Div. of Cardiology, Dept. of Medicine, Oulu Univ. Central Hospital, Kajaanintie 50, 90220 Oulu, Finland.
Received 9 September 1997; accepted in final form 6 November 1997.
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Appendix
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 S. Guzzetti, E. Borroni, P. E. Garbelli, E. Ceriani, P. D. Bella, N. Montano, C. Cogliati, V. K. Somers, A. Mallani, and A. Porta Symbolic Dynamics of Heart Rate Variability: A Probe to Investigate Cardiac Autonomic Modulation Circulation, July 26, 2005; 112(4): 465 - 470. [Abstract] [Full Text] [PDF]
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M. P. Tulppo, H. V. Huikuri, E. Tutungi, D. S. Kimmerly, A. W. Gelb, R. L. Hughson, T. H. Makikallio, and J. Kevin Shoemaker Feedback effects of circulating norepinephrine on sympathetic outflow in healthy subjects Am J Physiol Heart Circ Physiol, February 1, 2005; 288(2): H710 - H715. [Abstract] [Full Text] [PDF]
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A. M. Kiviniemi, A. J. Hautala, T. Seppanen, T. H. Makikallio, H. V. Huikuri, and M. P. Tulppo Saturation of high-frequency oscillations of R-R intervals in healthy subjects and patients after acute myocardial infarction during ambulatory conditions Am J Physiol Heart Circ Physiol, November 1, 2004; 287(5): H1921 - H1927. [Abstract] [Full Text] [PDF]
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 M. P. Tulppo, A. J. Hautala, T. H. Makikallio, R. T. Laukkanen, S. Nissila, R. L. Hughson, and H. V. Huikuri Effects of aerobic training on heart rate dynamics in sedentary subjects J Appl Physiol, July 1, 2003; 95(1): 364 - 372. [Abstract] [Full Text] [PDF]
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M. P. Tulppo, R. L. Hughson, T. H. Makikallio, K. E. J. Airaksinen, T. Seppanen, and H. V. Huikuri Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics Am J Physiol Heart Circ Physiol, March 1, 2001; 280(3): H1081 - H1087. [Abstract] [Full Text] [PDF]
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H. V. Huikuri, T. H. Makikallio, C.-K. Peng, A. L. Goldberger, U. Hintze, and M. Moller Fractal Correlation Properties of R-R Interval Dynamics and Mortality in Patients With Depressed Left Ventricular Function After an Acute Myocardial Infarction Circulation, January 4, 2000; 101(1): 47 - 53. [Abstract] [Full Text] [PDF]
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H. V. Huikuri, A.-M. Poutiainen, T. H. Makikallio, M. J. Koistinen, K. E. J. Airaksinen, R. D. Mitrani, R. J. Myerburg, and A. Castellanos Dynamic Behavior and Autonomic Regulation of Ectopic Atrial Pacemakers Circulation, September 28, 1999; 100(13): 1416 - 1422. [Abstract] [Full Text] [PDF]
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C. Braun, P. Kowallik, A. Freking, D. Hadeler, K.-D. Kniffki, and M. Meesmann Demonstration of nonlinear components in heart rate variability of healthy persons Am J Physiol Heart Circ Physiol, November 1, 1998; 275(5): H1577 - H1584. [Abstract] [Full Text] [PDF]
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D. P. Holschneider, O. U. Scremin, D. R. Chialvo, K. Chen, and J. C. Shih Heart rate dynamics in monoamine oxidase-A- and -B-deficient mice Am J Physiol Heart Circ Physiol, May 1, 2002; 282(5): H1751 - H1759. [Abstract] [Full Text] [PDF]
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