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1 Department of Kinesiology, University of Waterloo, Ontario N2L 3G1, Canada; 2 Division of Cardiology, Department of Medicine, University of Oulu; and 3 Merikoski Rehabilitation and Research Center, Oulu 90100, Finland
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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tk;1Passive head-up
tilt and exercise result in specific changes in the spectral
characteristics of heart rate (HR) variability as a result of reduced
vagal and enhanced sympathetic outflow. Recently analytic methods based
on nonlinear system theory have been developed to characterize the
nonlinear features in HR dynamics. This study was designed to assess
the changes in the fractal and complexity measures of HR behavior
during the passive head-up tilt and during exercise. Fractal exponent
(
1) and approximate entropy (ApEn), measures of
short-term correlation properties and overall complexity of HR,
respectively, along with spectral components of HR variability were
analyzed during a passive head-up tilt test (n = 10)
and a low-intensity steady-state exercise (n = 20) in
healthy subjects. We observed that 1 increased during the tilt test (from 0.85 ± 0.22 to 1.48 ± 0.20;
P < 0.001) and during the exercise (from 1.00 ± 0.22 to 1.37 ± 0. 14; P < 0.001). ApEn also
increased during the exercise (from 1.04 ± 0.11 to 1. 11 ± 0.08; P < 0.05), but it did not change during the tilt
test. The normalized high-frequency spectral component decreased and the low-frequency component increased similarly during both the exercise and the tilt test (P < 0.001 for all).
Exercise and passive tilt result in an increase of short-term fractal
correlation properties of HR dynamics, which is related to changes in
the balance between the low- and high-frequency oscillations in
controlled situations. Overall complexity of HR dynamics increases
during exercise but not during passive tilt.
variability; approximate entropy; detrended fluctuation analysis
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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TIME AND FREQUENCY DOMAIN ANALYSES of heart rate (HR) variability (HRV) are the most commonly used noninvasive methods to evaluate autonomic regulation of HR in healthy subjects as well as in patients with cardiovascular disorders. Because nonlinear phenomena are involved in the genesis of human HR fluctuations (2, 13, 31, 35), new analysis techniques have been developed to probe features in HR behavior that are not detectable by traditional analysis methods (11, 12, 21, 23, 36). Analysis of fractal scaling exponents by detrended fluctuation analysis (DFA) is one such method that describes the fractal-like correlation properties of R-R interval data (21). Approximate entropy (ApEn) is another nonlinear method that quantifies the amount of complexity in the time-series data (23, 24).
Several studies have described changes in the spectral characteristics of HRV caused by passive head-up tilt and dynamic exercise (1, 3, 5, 19, 20, 25, 32, 33, 35). The HR increases and the high-frequency (HF) power of R-R intervals decreases both during the head-up tilt test and during dynamic exercise as evidence of withdrawal of vagal activity during both conditions (19, 20, 36). The normalized low-frequency (LF) component of HRV also increases both during tilting and exercise, which suggests an increase in sympathetic outflow (3, 19, 20, 25). Despite a large body of data concerning the changes in spectral characteristics of HRV during the passive tilt and exercise, there is little information on the effects of these physiological interventions on nonlinear characteristics of HR behavior. This study was designed to assess the changes in the nonlinear features of HRV caused by the passive head-up tilt test and steady-state low-intensity dynamic exercise. The main purpose was to gain insight into the physiological background for fractal and complexity characteristics of HR dynamics.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects and study protocol. All subjects were healthy male volunteers. The protocols were approved by the ethics committee of the Merikoski Rehabilitation and Research Center, and all subjects gave their written informed consent. A passive head-up tilt test and a low-intensity exercise test were performed in two different populations. An incremental passive head-up tilt test was performed for 10 subjects (age, 29 ± 5 yr; weight, 75 ± 5 kg; height, 178 ± 4 cm). The subjects in the exercise test (n = 20, age, 29 ± 8 yr; weight, 79 ± 10 kg; height, 179 ± 6 cm) were selected from a larger group of subjects (n = 110), who participated in another study aimed at analyzing the effects of age and physical fitness on HR dynamics during exercise (32). The subjects of the exercise group in the present study were selected from this larger group so that they were age-matched with the subjects undergoing tilt-table testing. Subjects were not allowed to eat or drink coffee for 3 h before the tests. Vigorous exercise and alcohol were also forbidden for 48 h before the testing days. The subjects lay in a supine position in a quiet room at least 15 min before data collection and became accustomed to breathing at a constant metronome-guided rate of 0.25 Hz for the duration of the experiments. Blood pressure was measured with an automatic oscillometric blood pressure recorder every 5 min throughout the protocols (Dinamap, Criticon).
The time periods of interventions were designed to be long enough to obtain the R-R interval data in stationary conditions. The HRV analyses were performed from 500 R-R intervals during the period of stable condition when no changes in the average HR were observed during each intervention. The treadmill exercise time was extended to accustom the subjects to walking properly on the treadmill and to have HR recordings that were as steady state as possible. The spectral and nonlinear HRV analyses were always done from the last 500 beats of R-R interval recordings at rest and during different experiments. The tilt protocol included baseline recordings (10 min) and recordings during a passive head-up tilting to 20°, 40°, and 60° for 10 min of each load. After each 10-min tilting period, subjects were moved again to a horizontal position for 10 min (19). Twenty subjects were studied at rest and during a dynamic exercise. After the resting period (30 min) the subjects performed a standardized walking test (30 min) on a treadmill with a speed of 4 km/h. Breathing was spontaneous during exercise.HRV measures. The R-R intervals were recorded with a Polar R-R recorder with a sampling frequency of 1,000 Hz (Polar Electro) (28). A continuous-surface electrocardiogram (ECG) was also monitored (TEC-7100, Nihon Kohden) and recorded (Oxford Medilog 4500, Oxford Instruments) during the experiments to confirm the sinus origin of the beats. All of the R-R intervals were edited manually to exclude all premature beats and noise, which accounted for <1% in every subject.
Frequency-domain analysis. An autoregressive model was used to estimate the power-spectrum densities of HRV (11, 32). The power spectra were quantified by measuring the area under two frequency bands: LF power (0.04-0.15 Hz) and HF power (0.15-0.4 Hz). A logarithmic transformation to the natural base was performed on both spectral components of HRV. The spectral component values are also presented in normalized units (nu) (19).
Fractal and complexity analysis.
DFA quantifies fractal-like correlation properties of the R-R interval
data (12, 21). The root-mean-square fluctuation of the
integrated and detrended data are measured in observation windows of
different sizes and then plotted against the size of the window on a
log-log scale (Fig. 1). The scaling
exponent represents the slope of this line, which relates
(log)fluctuation to (log)window size. In this study the short-term
(4-11 beats) scaling exponent (1) was calculated
based on previous experiments (16).
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Statistics. Standard statistical methods were used for the calculation of means and standard deviations. Normal Gaussian distribution of the data was verified by the Kolmogorov-Smirnov goodness-of-fit test (z value > 1.0). ANOVA for repeated measurements was used to compare the changes in HR, blood pressure, and HRV parameters during the different protocols. The differences between the mean values were tested for significance using paired t-tests. Analysis of covariance (ANCOVA) using the baseline levels of each HRV measure as covariates was performed to analyze the differences in changes of HRV measures between the tilt and exercise. Pearson's correlation analysis was performed among different HRV parameters at rest, during the passive head-up tilt test, during exercise, and across all conditions.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Representative examples of R-R interval time series, power spectra, and fractal characteristics at rest, during the head-up tilt test, and during dynamic exercise are shown in Fig. 1.
Effects of exercise and passive head-up tilt on 1
and ApEn.
The fractal scaling exponent 1 increased both during the
dynamic exercise and during the passive head-up tilt test
(P < 0.001 for both; Fig.
2, A and B). ApEn
also increased during dynamic exercise (P < 0.05; Fig.
2, C and D), but the passive head-up tilt test
did not cause any significant changes in ApEn values.
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Effects of exercise and passive head-up tilt on spectral measures
of HRV.
The changes in the spectral measures of HRV caused by exercise and
tilting are shown in Table 1. HF power,
analyzed in absolute units, decreased during both interventions
(P < 0.001). Absolute LF power decreased during
exercise (P < 0.001) but did not change during the
tilt test. The LF-HF ratio increased both during exercise and tilting
(P < 0.01). The HF spectral component analyzed in normalized units decreased during both the tilt test and exercise. Accordingly the normalized LF spectral component increased during both
interventions (P < 0.001).
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Correlation among different HRV methods.
Table 2 shows the correlation
coefficients between the different HRV measures at baseline, during
exercise, during the passive head-up tilt test, and across all
conditions, respectively. During all of the experiments, scaling
exponent 1 had only a weak correlation with HR and
spectral measures of HRV when analyzed in absolute units. However,
1 had an inverse correlation with the normalized HF
power and a positive correlation with the normalized LF power at
baseline and during the tilt (when the rate of respiration was
controlled). These correlations became weaker during the dynamic exercise. ApEn had no or only weak correlations with the HR and all the
spectral measures of HRV; 1 and ApEn also had only a weak mutual correlation.
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1 were
correlated to changes in normalized HF and LF components, but not with
the change in HR. Changes in ApEn correlated only weakly with the
changes in HR but did not correlate with any of the other HRV measures.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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The main finding of this study is that the short-term fractal
scaling exponent 1 increases both during passive head-up
tilt and during low-intensity dynamic exercise. On the contrary,
overall complexity of HR dynamics, measured by ApEn, increased during the low-intensity dynamic exercise but not during the passive head-up tilt.
Methodological background for analysis of fractal correlation
properties and complexity of HR dynamics.
The DFA technique is a modified root-mean-square analysis of a random
walk, and it quantifies the presence or absence of fractal correlation
properties in the time series. In this method, a fractal-like signal
results in an exponent value of ~1.0, a random signal results in a
value of 0.5, and a strongly correlated signal behavior results in an
exponent value of 1.5 (21). Increased short-term fractal exponent values (1 
1.4) observed during the passive
head-up tilt and during exercise in the present study reveal stronger correlation properties of short-term HR dynamics during these interventions compared with the baseline resting conditions.
Effects of dynamic exercise and passive head-up tilt on HR dynamics. The interplay between the sympathetic and vagal regulation of HR is usually organized in a reciprocal fashion, i.e., increased activity in one system is accompanied by decreased activity in another (18, 19). At the beginning of low-intensity exercise, HR increases due to inhibition of vagal tone. As the workload increases, HR increases due to further vagal withdrawal and concomitant sympathetic activation (20, 33). Reciprocal changes in sympathetic and vagal activity also occur during the incremental passive head-up tilt exercise (3, 19). In the present study, normalized HF power decreased and LF power increased as evidence of withdrawal of vagal activity and enhanced sympathetic outflow during both dynamic exercise and the passive head-up tilt. Similar changes in the normalized spectral components of HRV during exercise and the passive tilt have been observed and reported in numerous previous studies (3, 19, 20). Changes in autonomic regulation caused by both dynamic exercise and the passive tilt also resulted in concordant changes in the short-term fractal properties of HR dynamics.
Dynamic exercise also resulted in an increase in the overall complexity of HR dynamics, but the passive head-up tilt exercise did not cause any notable changes in complexity values. Although dynamic exercise and the passive head tilt cause similar changes in the cardiac sympathetic and vagal outflow, there are obvious differences in cardiovascular regulation between the head tilt and exercise: the passive head-up tilt predominantly increases the diastolic blood pressure (27), but low-intensity exercise mainly increases systolic blood pressure (26). The passive head-up tilt >50° results in a 1.5- to 3-fold increase in circulating norepinephrine levels and only minor or no changes in circulating epinephrine (1, 27, 30). On the contrary, low-intensity dynamic exercise predominantly increases the circulating epinephrine levels (4, 20). It is possible that divergent changes in the circulating catecholamines, or some other factors affecting neurohumoral regulation, explain the observed differences in HR dynamics caused by the passive head tilt and active exercise. These differences were evident only in the analysis of complexity of HR dynamics, but spectral and fractal analysis methods were not able to uncover significant differences in HR behavior during the head tilt and dynamic exercise.Correlation among different HRV measures. Previous studies have shown that fractal and complexity measures of HRV are weakly correlated with the traditional time- and frequency-domain indices when measured during "free-running" conditions from 24-h ambulatory ECG recordings (15, 22). In this study the short-term fractal scaling exponent was correlated to the normalized spectral components at baseline and across all conditions. A weaker correlation observed in previous Holter studies may be due to uncontrolled breathing rate, which may have significant effects on the characteristics of short-term HR dynamics (6). Correlation between the short-term scaling exponent and normalized spectral components became weaker also here during exercise with a spontaneous breathing rate. From the mathematical point of view, spectral measures resemble scaling indices when analyzed as normalized units during strictly controlled external conditions, because both describe relative changes in the characteristics of HR fluctuations over different time scales rather than the magnitude of HRV. These measures are not surrogates in uncontrolled conditions, however. Measurement of scaling exponents by the DFA method provides precise information on the scaling properties of HR fluctuations over a highly segmented time window, whereas conventionally computed spectral measures vaguely describe HR fluctuation ratios in predetermined time windows.
ApEn had only a weak correlation with the1 scaling
exponent across all conditions, and it did not correlate notably with any of the HRV measures, showing that this overall complexity measure
describes different features in HR dynamics than the spectral analysis
methods or DFA analysis.
Study limitation. In the present study we investigated differences in HR dynamics between low-intensity exercise and the passive head-up tilt test only in healthy males. However, HR dynamics may be different between males and females. Previous studies have shown increased complexity (ApEn) and a higher HF component during short-term ECG recordings under controlled breathing in women compared with men (10, 29). Gender differences in fractal HR dynamics have also been previously described (22). Before generalizing the present observation, the same experiments should be performed also in females and subjects with cardiovascular disorders. We decided to start with healthy male subjects because it may be important to understand the determinants of HR behavior first in a homogeneous sample of subjects without evident cardiovascular disorders before performing similar studies in patients with heart disease.
In conclusions by using a fractal analysis method of HRV, we observed that low-intensity dynamic exercise and passive head-up tilt result in a change in the HR dynamics from a "normal" fractal-like dynamics toward an increase in the short-term correlation properties of HR dynamics. By analyzing the complexity of HR dynamics, subtle differences in HR behavior caused by the passive head tilt and dynamic exercise could be observed that were not detected by traditional spectral or fractal analysis methods. These observations provide some novel information on the physiological background for fractal correlation properties and complexity of HR behavior. Further experimental work will be needed to assess the background for reduction in both short-term fractal properties and complexity in HR dynamics, which have been shown to be associated with the occurrence of adverse clinical events (7-9, 14-16, 34).| |
ACKNOWLEDGEMENTS |
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The authors appreciate the technical and financial support received from Polar Electro (Kempele, Finland) and the generous help from Heart Signal (Kempele, Finland).
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FOOTNOTES |
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This research was funded by grants from the Ministry of Education (Helsinki, Finland), the Heart and Stroke Foundation of Ontario (Toronto, Canada), the Finnish Foundation for Cardiovascular Research (Helsinki, Finland), and the Seppo Säynäjäkangas Foundation (Oulu, Finland).
Address for reprint requests and other correspondence: M. P. Tulppo, Merikoski Rehabilitation and Research Center, Nahkatehtaankatu 3, 90100 Oulu, Finland (E-mail: mikko.tulppo{at}tiimi.merikoski.fi).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 17 February 2000; accepted in final form 15 September 2000.
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REFERENCES |
|---|
|
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
|
|---|
1.
Ahmed, MW,
Kadish AH,
Parker MA,
and
Goldberger JJ.
Effects of physiologic and pharmacologic adrenergic stimulation on heart rate variability.
J Am Coll Cardiol
24:
1082-1090,
1994[Abstract].
2.
Braun, C,
Kowallik P,
Freking A,
Hadeler D,
Kniffki K-D,
and
Meesmann M.
Demonstration of nonlinear components in heart rate variability of healthy person.
Am J Physiol Heart Circ Physiol
275:
H1577-H1584,
1998
3.
Butler, GC,
Yamamoto Y,
Xing HC,
Northey DR,
and
Hughson RL.
Heart rate variability and fractal dimension during orthostatic challenges.
J Appl Physiol
75:
2602-2612,
1993
4.
Cooper, DM,
Barstow TJ,
Bergner A,
and
Lee WN.
Blood glucose turnover during high- and low-intensity exercise.
Am J Physiol Endocrinol Metab
257:
E405-E412,
1989
5.
Fei, L,
Anderson MH,
Statters DJ,
Malik M,
and
Camm AJ.
Effects of passive tilt and submaximal exercise on spectral heart rate variability in ventricular fibrillation patients without significant structural heart rate disease.
Am Heart J
129:
285-290,
1995[ISI][Medline].
6.
Hirsch, JA,
and
Bishop B.
Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate.
Am J Physiol Heart Circ Physiol
241:
H620-H629,
1981
7.
Ho, KKL,
Moody GB,
Peng C-K,
Mietus JE,
Larson MG,
Levy D,
and
Goldberger AL.
Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics.
Circulation
96:
842-848,
1997
8.
Hogue, CW, Jr,
Domitrovich PP,
Stein PK,
Despotis GD,
Re L,
Schuessler RB,
Kleiger RE,
and
Rottman JN.
RR interval dynamics before atrial fibrillation in patients after coronary artery bypass graft surgery.
Circulation
98:
429-434,
1998
9.
Huikuri, HV,
Mäkikallio TH,
Peng CK,
Goldberger AL,
Hintze U,
and
Møller M.
Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction.
Circulation
101:
47-53,
2000
10.
Huikuri, HV,
Pikkujämsä SM,
Airaksinen KEJ,
Ikäheimo MJ,
Rantala AO,
Kauma H,
Lilja M,
and
Kesäniemi YA.
Sex-related differences in autonomic modulation of heart rate in middle-aged subjects.
Circulation
94:
122-125,
1996
11.
Huikuri, HV,
Seppänen T,
Koistinen MJ,
Airaksinen KEJ,
Ikäheimo MJ,
Castellanos A,
and
Myerburg RJ.
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
312:
170-177,
1996.
12.
Iyengar, N,
Peng C-K,
Ladin Z,
Wei JY,
Goldberger AL,
and
Lipsitz LAL
Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics.
Am J Physiol Regulatory Integrative Comp Physiol
271:
R1078-R1084,
1996
13.
Kanters, JK,
Hojgaard MV,
Agner E,
and
Holstein-Rathlou NH.
Short- and long-term variations in nonlinear dynamics of heart rate variability.
Cardiovasc Res
31:
400-409,
1996[ISI][Medline].
14.
Mäkikallio, TH,
Hoiber S,
Kober L,
Torp-Pedersen C,
Peng CK,
Goldberger AL,
and
Huikuri HV.
Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after myocardial infarction.
Am J Cardiol
83:
836-839,
1999[ISI][Medline].
15.
Mäkikallio, TH,
Ristimäe T,
Airaksinen KEJ,
Peng C-K,
Goldberger AL,
and
Huikuri HV.
Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures.
Am J Cardiol
81:
27-31,
1998[ISI][Medline].
16.
Mäkikallio, TH,
Seppänen T,
Airaksinen KEJ,
Koistinen J,
Tulppo MP,
Peng C-K,
Goldberger AL,
and
Huikuri HV.
Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction.
Am J Cardiol
80:
779-783,
1997[ISI][Medline].
17.
Mäkikallio, TH,
Seppanen T,
Niemela M,
Airaksinen KEJ,
Tulppo M,
and
Huikuri HV.
Abnormalities in beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction.
J Am Coll Cardiol
28:
1005-1011,
1996[Abstract].
18.
Malliani, A,
Pagani M,
Lombardi F,
and
Cerutti S.
Cardiovascular neural regulation explored in the frequency domain.
Circulation
84:
482-492,
1991
19.
Montano, N,
Ruscone TG,
Porta A,
Lombardi F,
Pagani M,
and
Malliani A.
Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt.
Circulation
90:
1826-1831,
1994
20.
Nakamura, Y,
Yamamoto Y,
and
Muraoka I.
Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability.
J Appl Physiol
74:
875-881,
1993
21.
Peng, C-K,
Havlin S,
Stanley HE,
and
Goldberger AL.
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.
Chaos
5:
82-87,
1995[ISI][Medline].
22.
Pikkujämsä, SM,
Mäkikallio TH,
Skyttä J,
Sourander LB,
Peng C-K,
Goldberger AL,
and
Huikuri HV.
Cardiac interbeat interval dynamics from childhood to senescence: comparison of conventional and new measures based on fractals and chaos theory.
Circulation
100:
393-399,
1999
24.
Pincus, SM,
and
Huang WM.
Approximate entropy: statistical properties and applications.
Commun Stat Theory Meth
21:
3061-3077,
1992.
23.
Pincus, SM.
Approximate entropy as a measure of system complexity.
Proc Natl Acad Sci USA
88:
2297-2301,
1991
25.
Radelli, A,
Bernardi L,
Valle F,
Leuzzi S,
Salvucci F,
Pedrotti L,
Marchesi E,
Finardi G,
and
Sleight P.
Cardiovascular autonomic modulation in essential hypertension: effect of tilting.
Hypertension
24:
556-563,
1994
26.
Robinson, TE,
Sue DY,
Huszczuk A,
Weiler-Ravell D,
and
Hansen JE.
Intra-arterial and cuff blood pressure responses during incremental cycle ergometry.
Med Sci Sports Exerc
20:
142-149,
1988[ISI][Medline].
27.
Rössler, A,
László Z,
Haditsch B,
and
Hinghofer-Szalkay HG.
Orthostatic stimuli rapidly change plasma adrenomedullin in humans.
Hypertension
34:
1147-1151,
1999
28.
Ruha, A,
Sallinen S,
and
Nissilä S.
A real-time microprocessor QRS detector system with a 1 ms timing accuracy for the measurement of ambulatory HRV.
IEEE Trans Biomed Eng
44:
159-167,
1997[ISI][Medline].
29.
Ryan, SM,
Goldberger AL,
Pincus SM,
Mietus J,
and
Lipsitz LA.
Gender- and age-related differences in heart rate dynamics: are women more complex than men?
J Am Coll Cardiol
24:
1700-1707,
1994[Abstract].
30.
Sander-Jensen, K,
Secher NH,
Astrup A,
Christensen NJ,
Giese J,
Schwartz TW,
Warberg J,
and
Bie P.
Hypotension induced by passive head-up tilt: endocrine and circulatory mechanisms.
Am J Physiol Regulatory Integrative Comp Physiol
251:
R742-R748,
1986
31.
Tulppo, MP,
Mäkikallio TH,
Seppänen T,
Airaksinen KEJ,
and
Huikuri HV.
Heart rate dynamics during accentuated sympathovagal interaction.
Am J Physiol Heart Circ Physiol
274:
H810-H816,
1998
32.
Tulppo, MP,
Mäkikallio TH,
Seppänen T,
Laukkanen RJ,
and
Huikuri HV.
Vagal modulation of heart rate during exercise: effects of age and physical fitness.
Am J Physiol Heart Circ Physiol
274:
H424-H429,
1998
33.
Tulppo, MP,
Mäkikallio TH,
Takala TES,
Seppänen T,
and
Huikuri HV.
Quantitative beat-to-beat analysis of heart rate dynamics during exercise.
Am J Physiol Heart Circ Physiol
271:
H244-H252,
1996
34.
Vikman, S,
Mäkikallio TH,
Yli-Mäyry S,
Pikkujämsä S,
Koivisto A-M,
Reinikainen P,
Airaksinen KEJ,
and
Huikuri HV.
Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation.
Circulation
100:
2079-2084,
1999
36.
Yamamoto, Y,
Hughson RL,
Sutton JR,
Houston CS,
Cymerman A,
Fallen EL,
and
Kamath MV.
Operation Everest II: an indication of deterministic chaos in human heart rate variability at simulated extreme altitude.
Biol Cybern
69:
205-212,
1993[ISI][Medline].
35.
Yamamoto, Y,
and
Hughson RL.
Coarse-graining spectral analysis: new method for studying heart rate variability.
J Appl Physiol
71:
1143-1150,
1991
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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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I. Dvir, Y. Adler, D. Freimark, and P. Lavie Evidence for fractal correlation properties in variations of peripheral arterial tone during REM sleep Am J Physiol Heart Circ Physiol, July 1, 2002; 283(1): H434 - H439. [Abstract] [Full Text] [PDF]
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S. C. Malpas Neural influences on cardiovascular variability: possibilities and pitfalls Am J Physiol Heart Circ Physiol, January 1, 2002; 282(1): H6 - H20. [Abstract] [Full Text] [PDF]
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