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(Circulation. 1999;100:393-399.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Cardiac Interbeat Interval Dynamics From Childhood to Senescence
Comparison of Conventional and New Measures Based on Fractals and Chaos Theory
From the Division of Cardiology, Department of Internal Medicine, University of Oulu (S.M.P., T.H.M., H.V.H.), the Department of Geriatrics, University of Turku (L.B.S., I.J.R.), the Research and Development Center of the Social Insurance Institution, Turku (P.P.), and the Hospital for Children and Adolescents, Helsinki University Central Hospital (J.S.), Finland; and the Margret and H.A. Rey Laboratory for Nonlinear Dynamics in Medicine, Cardiovascular Division, the Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Mass (C.-K.P., A.L.G.).
Correspondence to Sirkku M. Pikkujämsä, MD, Division of Cardiology, Department of Internal Medicine, University of Oulu, Kajaanintie 50, 90220 Oulu, Finland. E-mail pikkujam{at}cc.oulu.fi
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Abstract |
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Background—New methods of R-R interval variability based on fractal scaling and nonlinear dynamics ("chaos theory") may give new insights into heart rate dynamics. The aims of this study were to (1) systematically characterize and quantify the effects of aging from early childhood to advanced age on 24-hour heart rate dynamics in healthy subjects; (2) compare age-related changes in conventional time- and frequency-domain measures with changes in newly derived measures based on fractal scaling and complexity (chaos) theory; and (3) further test the hypothesis that there is loss of complexity and altered fractal scaling of heart rate dynamics with advanced age.
Methods and Results—The relationship between age and cardiac
interbeat (R-R) interval dynamics from childhood to senescence was
studied in 114 healthy subjects (age range, 1 to 82 years) by
measurement of the slope, ß, of the power-law regression line (log
power-log frequency) of R-R interval variability (10-4 to
10-2 Hz), approximate entropy (ApEn), short-term
(
1) and intermediate-term (2) fractal
scaling exponents obtained by detrended fluctuation analysis,
and traditional time- and frequency-domain measures from 24-hour ECG
recordings. Compared with young adults (<40 years old, n=29),
children (<15 years old, n=27) showed similar complexity (ApEn) and
fractal correlation properties (1, 2,
ß) of R-R interval dynamics despite lower spectral and time-domain
measures. Progressive loss of complexity (decreased ApEn,
r=-0.69, P<0.001) and alterations of
long-term fractal-like heart rate behavior (increased 2,
r=0.63, decreased ß, r=-0.60,
P<0.001 for both) were observed thereafter from middle
age (40 to 60 years, n=29) to old age (>60 years, n=29).
Conclusions—Cardiac interbeat interval dynamics change markedly from childhood to old age in healthy subjects. Children show complexity and fractal correlation properties of R-R interval time series comparable to those of young adults, despite lower overall heart rate variability. Healthy aging is associated with R-R interval dynamics showing higher regularity and altered fractal scaling consistent with a loss of complex variability.
Key Words: aging • heart rate • fractals
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Introduction |
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Cardiac interbeat interval dynamics vary with age in healthy subjects, possibly in relation to changes in the regulatory mechanisms. The maturation of the autonomic nervous system and other control systems during childhood is associated with increased variation of heart rate (HR).1 2 Conversely, increasing age during adult life is associated with a reduction in overall HR variability2 3 4 5 6 7 and also in the complexity of physiological dynamics.8 9 This loss of complexity may be due to both structural factors (eg, loss of sinoatrial pacemaker cells) and functional changes (eg, altered coupling between these components).9 The loss of complexity and alterations of long-range (fractal) organization with aging, which are also apparent in many diseases,10 may be associated with the reduced ability to adapt to physiological stress.
Recently, new dynamic methods of R-R interval variability have been used in conjunction with traditional time- and frequency-domain measures to uncover "hidden" abnormalities and alterations that are not otherwise apparent.9 A number of studies have addressed the effects of age on R-R interval dynamics. Reduced HR variability and loss of HR complexity have been reported with increasing age.2 3 4 5 6 7 8 11 12 However, previous studies have important limitations related to analyses based solely on traditional time- and frequency-domain measures,2 3 4 5 7 on short-term (<3 hours) ECG recordings,6 11 12 or on comparisons between small groups with widely disparate ages but without including children.8 11
The purpose of the present study, therefore, was to systematically investigate the effects of age on R-R interval dynamics from 24-hour ECG recordings in healthy subjects over a wide range of ages (childhood to advanced age). In addition to traditional measures of HR variability, we used recently described methods derived from nonlinear dynamics (chaos theory) and fractal analysis, including scaling exponents derived from the power spectrum13 14 15 and detrended fluctuation analysis (DFA),11 16 and approximate entropy (ApEn),17 18 19 a "complexity" measure.
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Subjects
One hundred fourteen healthy subjects (age range, 1 to 82 years) were included in this cross-sectional study. The subjects were divided into 4 groups: (1) children, <15 years old (mean, 8±5 years); (2) young adults, 15 to 40 years old (mean, 28±6 years); (3) middle-aged, 40 to 60 years old (mean, 50±6 years); and (4) elderly, >60 years old (mean, 71±5 years). There were 15 boys and 12 girls in the group of children and 17 men and 12 women in each other group. The children and young adults were apparently healthy, with no history or symptoms of heart disease, hypertension, or diabetes, and with normal findings on clinical examination. The middle-aged20 and elderly subjects21 were selected from previously described random populations. All middle-aged and elderly subjects underwent a physical examination, a standard 12-lead ECG, a chest radiograph, and laboratory tests. No subjects with a previous history, symptoms, or clinical evidence (including analysis of a 12-lead ECG with the Minnesota code) of ischemic heart disease, diabetes, hypertension, or any other medical disorder were included. The fasting blood glucose was <6.7 mmol/L and the blood pressure <160/90 mm Hg in all subjects. None of the subjects included were taking any medication. All subjects gave informed consent. The study protocol was approved by the Ethics Committee of the University of Oulu.
HR Recordings
A 24-hour ambulatory ECG recording was performed during
usual everyday activities. All subjects had 
18 hours (mean, 23±1
hours) of ECG data, including 90% of normal sinus beats. The ECG
data were digitally sampled (frequency, 256 Hz) and transferred from a
scanner to a microcomputer for the analysis of HR variability.
Premature beats and artifact were carefully eliminated automatically
and manually.22 The measures of R-R interval dynamics were
calculated from the entire 24-hour recording and also
separately for the hours representing nighttime (midnight
to 6 AM) and daytime (9 AM to 6 PM)
hours to study possible diurnal differences.
HR Variability Measures
Time- and Frequency-Domain Measures
The mean and the SD of all R-R intervals (SDNN) were used as
time-domain measures of HR variability. The power spectrum densities
were estimated by the fast Fourier method. Ultralow-frequency power
(ULF, <0.0033 Hz) and very-low-frequency power (VLF, 0.0033 to 0.04
Hz) were calculated from the entire 24-hour segment. Low-frequency
power (LF, 0.04 to 0.15 Hz), high-frequency power (HF, 0.15 to 0.4 Hz),
and the nighttime and daytime VLF powers were calculated from 1-hour
segments of the 24-hour recording. The mean value of these
segments was used.
Fractal Scaling and Complexity Measures: Power-Law
Relationship Analysis
The slope, ß, of the power spectrum was calculated as
described previously13 14 15 by a regression
analysis of log(power) and log(frequency) plots of the smoothed
power spectrum over the frequency range of 10-4
to 10-2 Hz. This range was chosen because of the
typically linear (1/fß) relationship between
log(power) and log(frequency) over this broad frequency
band.13
DFA quantifies fractal-like correlation properties of the time-series
data.11 16 The root-mean-square fluctuation of the
integrated and detrended data are measured in observation windows of
various sizes and then plotted against the size of the window on a
log-log scale. The scaling exponent represents the slope of
this line, which relates (log)fluctuation to (log)window size. In this
study, both 1, the short-term (4 to 11 beats)
and 2, the intermediate-term (>11
beats) scaling exponents were calculated. The scaling exponents
were calculated from segments encompassing 8000 beats of the 24-hour
ECG recording as previously described,11 16 and
the average values of these segments were used.
Approximate entropy, a measure quantifying the regularity of time
series, was calculated from the average values of segments encompassing
8000 beats with fixed input variables m=2 and
r=20% as previously described.17 18 19 In
addition, 1, 2, and
ApEn were calculated from segments encompassing 4000 beats from 3-hour
periods (midnight to 3 AM, 3 to 6 AM, 9 to 12
AM, noon to 3 PM, 3 to 6 PM) to
obtain nighttime (midnight to 6 AM), early sleeping phase
(midnight to 3 AM), and daytime (9 AM to 6
PM) average values.
Statistics
Results are reported as mean±SD. The data were normally
distributed. However, the distributions of the spectral values of HR
variability were highly skewed. Therefore, these data were transformed
by taking the natural logarithms of the absolute values.
Parametric tests were used to compare the groups and to test
the correlations among age and measures of HR dynamics based on the
results of the Kolmogorov-Smirnov test (z<1.0) for a normal
distribution. The comparisons between the 4 study groups were
analyzed by 1-way ANOVA followed by the Bonferroni post hoc
test. Student's t test was used to compare males and
females and nighttime and daytime values. Pearson's correlation
coefficients (r) are given when linear relationships between
2 variables are reported. A value of P<0.05 was
considered statistically significant.
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Results |
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Representative examples of R-R interval time-series, 24-hour power-spectra, power-law, and scaling DFAs of data from a 7-year-old, a 29-year-old, and a 76-year-old healthy male are shown in Figures 1
and 2. Different measures of HR dynamics as a
function of age are plotted in Figures 3 to 5.
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Children Versus Young Adults
Measures of complexity (ApEn) and short-term
(1) and longer-term temporal correlation
properties (2 and ß) of R-R intervals did
not differ between children and young adults
(Table). However, the total
variance and all the power spectral measures were lower in children
than in young adults. A linear increase in all time- and
frequency-domain measures was observed during childhood (r
between 0.66 [HF] and 0.78 [VLF], P<0.001 for all), and
children <6 years old (n=10) had significantly lower values than
children between 6 and 15 years old (n=17) (P<0.01 for
all). No differences were found in ApEn, 1,
and ß between children in the 2 age groups. Children 6 to 15 years
old had significantly lower total variance than young adults, but their
dynamic measures did not differ.
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Comparisons of HR variability measures during daytime (9 AM
to 6 PM) and nighttime (midnight to 6 AM) did
not reveal differences in ApEn or scaling exponents between the
children and young adults. Furthermore, when measures of HR variability
were compared between the groups during the early phase of sleeping
hours (midnight to 3 AM), no differences between the age
groups were observed in ApEn (1.39±0.13 in children versus 1.36±0.19
in young adults, P=NS), 1
(0.88±0.19 versus 1.00±0.21, P=NS), or
2 (0.92±0.11 versus 0.95±0.1.0,
P=NS), despite the lower overall variance in children (SDNN
95±43 ms in children versus 133±40 ms in young adults,
P<0.001). The differences in spectral measures of HR
variability were consistent during the daytime and during
different phases of sleeping hours (Table 1
).
Middle-Aged and Elderly Versus Young Subjects
A linear decrease of ApEn (r=-0.69) and ß
(r=0.60) and an increase in 2
(r=0.63) with age occurred during middle age and old age
(P<0.001 for all) (Figure 5, Table 1).
Middle-aged and elderly subjects had significantly lower values for ß
and ApEn and higher values for 2 than the 2
younger groups (Table 1). The short-term scaling exponent,
1, did not differ among the 3 adult groups. A
decrease of all time- and frequency-domain measures also occurred with
age in adults (Table 1). Differences in various indices between
the age groups were similar during the daytime and the nighttime
(Table 1).
To determine whether a decrease in total HR variability explains the
changes in dynamic measures of R-R interval variability with increasing
age, an ANCOVA was performed using SDNN and age group as explanatory
variables and each of the 4 dynamic measures of R-R interval
variability as a dependent variable. The significant differences
for ApEn, ß, 1, and
2 between the groups still remained after
adjustment for SDNN (P<0.001 for each).
Day-Night Differences
In all age groups, ApEn was higher (P<0.001 in each
group), 1 was lower (P<0.001 in
children and young adults, P<0.05 in middle-aged and
elderly), and all spectral measures were higher (P<0.01 for
all in each group) during sleep times than daytime (Table 1).
Effects of Sex on R-R Interval Dynamics
1 was significantly lower (1.10±0.13
versus 1.18±0.16, P<0.01), ß was slightly steeper
(-1.29±0.21 versus -1.21±0.19, P<0.05), and VLF was
slightly lower (7.14±0.70 versus 7.45±0.78, P<0.05) in
females, whereas no other differences were observed compared with
males. Similar age dependencies of the different measures of R-R
interval dynamics were observed in both males and females.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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The results of this study indicate that R-R interval dynamics change markedly from childhood to old age in healthy subjects. However, there are important age-related differences among various measures. Children show complexity and fractal correlation properties of R-R interval dynamics comparable to those of young, healthy adults despite lower overall HR variability. Progressive loss of complexity (increased regularity and predictability) and a decrease in total variability of R-R intervals occur from middle age to old age. Of particular note, the observed reduction in complexity and the changes in the fractal correlation properties with aging were not accompanied by a reduction in overall HR variability. Thus, dynamic measures of R-R interval variability provide complementary information about HR behavior when used in concert with traditional time- and frequency-domain HR variability measures.
Dynamic Analysis of R-R Intervals
The mathematical basis for new dynamic measures of R-R interval
variability used in this study has been described
elsewhere.11 13 14 15 16 17 18 19 Briefly, ApEn describes the
predictability or complexity of time-series data,17 18 19
the slope of the power-law relationship describes the fractal-like
correlation properties of R-R interval data over long time
periods,13 14 15 and 1 and
2 describe the correlation properties of the
short-term and intermediate-term R-R interval fluctuation,
respectively.11 16
R-R Interval Dynamics During Childhood
In this study, the complexity (ApEn) and temporal correlation
properties of HR behavior (1,
2, and ß) in children were similar to those
of young adults. Both children and young adults showed R-R interval
dynamics with 1/f behavior (1 and
2 
1.0, ß -1.0),
consistent with a system with fractal-like, scale-invariant
correlations. To the best of our knowledge, this is the first study to
analyze these dynamic measures of R-R intervals in children.
Our findings also confirm the reduced frequency-domain measures in
young children versus young adults observed in previous
studies.1 2
R-R Interval Dynamics During Adult Life: Effect of Increasing
Age
A steeper ß (the slope of the power-law relationship), a
decrease of ApEn (complexity), and an increase of
2 (the intermediate-term scaling exponent)
were observed with increasing age, suggesting that the longer-term R-R
interval dynamics change from 1/f behavior toward
1/f2 behavior. These findings are
consistent with lower complexity (higher regularity and
predictability) of R-R interval dynamics with increasing age. All
traditional measures of HR variability also decrease with aging,
evidenced by lower total variance and smaller spectral power at all
frequencies. These observations are consistent with previous
findings showing decreased total variance,3 4 5 6 decreased
spectral power of VLF, LF, and HF,7 steeper slopes of the
power-law relationship,6 and reduced ApEn
values8 12 with old age.
Day-Night Difference of the Different Measures
The findings of increased ApEn, decreased
1, and increased spectral components during
nighttime indicate increased variance and complexity of HR dynamics at
night. The age dependence of different measures of R-R interval
dynamics were similar when analyzed from 24 hours or from
nighttime hours. Thus, differences in physical activity among different
age groups (children versus elderly) during daytime do not explain the
observed age-related changes in R-R interval dynamics.
Effects of Sex on R-R Interval Dynamics
Previous studies using short-term ECG recordings under
controlled breathing and activity conditions have reported increased
complexity (ApEn)12 and lower LF and higher
HF20 in women compared with men. In the present study,
women had significantly lower 1 than men,
whereas other measures did not differ. Thus, in women, short-term R-R
interval dynamics seem to be closer to 1/f behavior than in men.
Interpretation of Age-Related Differences in R-R Interval
Dynamics
It has been suggested that scale invariance may be a central
organizing principle of physiological structure and
function. The breakdown of this scale-invariant, fractal organization
could lead to either totally uncorrelated randomness or highly
predictable (single-scale) behavior, both of which may result in a less
adaptable system.10 Thus, changes from 1/f scale-invariant
behavior toward behavior resembling either random fluctuations (white
noise) or toward 1/f2 behavior with less
complexity might be physiologically
deleterious. These changes seem to occur with
physiological aging. In contrast, children already
show a "mature"-pattern R-R interval dynamics comparable to that of
healthy young adults, with complex, fractal dynamics suggesting a
highly adaptive cardiovascular regulatory system.
The age-related changes in different measures of R-R interval dynamics are probably a marker of the various physiological mechanisms affecting these measures, especially neuroautonomic inputs.13 23 The finding that children showed a similar slope of the power-law relationship of R-R interval dynamics compared with young adults, despite reduced power of various spectral components, indicates that these indexes are differentially regulated and that HR variance and related measures cannot be used as surrogates for complexity measures.
Limitations of the Study
Twenty-four-hour recordings have been recommended for HR
variability testing in various cardiovascular disorders
because of better reproducibility of long-term versus short-term
recordings.24 The purpose of the present study
was to examine the R-R interval dynamics of 24-hour recordings
of healthy subjects during normal "free-running" conditions,
recognizing potential confounding effects of nonstationarities due to
diurnal rhythms, activity, and other factors. Because standardized
conditions (eg, controlled breathing, body posture, and physical
activity) were not used, this study cannot provide an exact
physiological basis for differences in various
measures of R-R interval dynamics between the age groups. New fractal
and complexity-related measures of HR variability can be reliably
analyzed only from relatively long recording periods
(several hours). It is not practicable to standardize external
conditions for such a long period of time, particularly in children.
Therefore, we also analyzed separately the various indices of
HR variability during the early phase of sleeping hours, which should
partly standardize the level of physical activity and the type of
sleep.25
Implications
Newer dynamic measures of fractal-like properties of R-R interval
variability complement traditional time- and frequency-domain measures
of HR variability. These novel methods may uncover hidden abnormalities
or alterations in time-series data.6 For example, the
slope, ß, of the power-law relationship has been reported to be a
stronger predictor of mortality after myocardial
infarction13 and in a general elderly
population15 than conventional spectral measures of HR
variability. Similarly, fractal measures of HR dynamics have prognostic
value as independent predictors of survival in patients with depressed
left ventricular function after acute myocardial
infarction26 and in heart failure,27 of
vulnerability to life-threatening arrhythmia,28
and in distinguishing subjects with coronary artery disease
from healthy control subjects.29 The findings of the
present study may be useful in quantifying and
modeling30 changes in the complex, nonlinear functioning
of the healthy cardiovascular system in relation to
age. Finally, age dependence of different measures of R-R interval
dynamics must be taken into account when normal reference values of
these measures are given for different subsets of subjects.
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Acknowledgments |
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This work was supported by grants from the Finnish Foundation for Cardiovascular Research, Helsinki, Finland; the Ida Montin Foundation, Helsinki, Finland; the National Aeronautics and Space Administration, Washington, DC; the National Institute of Mental Health, Bethesda, Md; the G. Harold and Leila Y. Mathers Charitable Foundation, Mt Kisco, New York; and the Finnish Life and Pension Insurance Companies, Helsinki, Finland. The authors thank Jeff Hausdorff, PhD, for helpful suggestions.
Received February 1, 1999; revision received April 21, 1999; accepted April 30, 1999.
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References |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Finley JP, Nugent ST. Heart rate variability in infants, children and young adults. J Auton Nerv Syst. 1995;51:103–108.[Medline] [Order article via Infotrieve]
2. Korkushko OV, Shatilo VB, Plachinda YuI, Shatilo TV. Autonomic control of cardiac chronotropic function in man as a function of age: assessment by power spectral analysis of heart rate variability. J Auton Nerv Syst. 1991;32:191–198.[Medline] [Order article via Infotrieve]
3.
Hellman JB, Stacy RW. Variation of respiratory sinus
arrhythmia with age. J Appl Physiol. 1976;41:734–738.
4.
O'Brien IAD, O'Hare P, Corrall RJM. Heart rate
variability in healthy subjects: effect of age and the derivation of
normal ranges for tests of autonomic function. Br Heart
J. 1986;55:348–354.
5.
Shannon DC, Carley DW, Benson H. Aging of modulation
of heart rate. Am J Physiol. 1987;253:H874–H877.
6.
Lipsitz LA, Mietus J, Moody GB, Goldberger AL.
Spectral characteristics of heart rate variability before and during
postural tilt: relations to aging and risk of syncope.
Circulation. 1990;81:1803–1810.
7.
Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM,
Schneider WJ, Stein PK. RR variability in healthy, middle-aged persons
compared with patients with chronic coronary heart disease or
recent acute myocardial infarction. Circulation. 1995;91:1936–1943.
8. Kaplan DT, Furman MI, Pincus SM, Ryan SM, Lipsitz LA, Goldberger AL. Aging and the complexity of cardiovascular dynamics. Biophys J. 1991;59:945–949.[Medline] [Order article via Infotrieve]
9.
Lipsitz LA, Goldberger AL. Loss of "complexity"
and aging: potential applications of fractals and chaos theory to
senescence. JAMA. 1992;267:1806–1809.
10. Goldberger AL. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet. 1996;347:1312–1314.[Medline] [Order article via Infotrieve]
11.
Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA.
Age-related alterations in the fractal scaling of cardiac interbeat
interval dynamics. Am J Physiol. 1996;271:R1078–R1084.
12. Ryan SM, Goldberger AL, Pincus SM, Mietus J, Lipsitz LA. Gender- and age-related differences in heart rate dynamics: are women more complex than men? J Am Coll Cardiol. 1994;24:1700–1707.[Abstract]
13.
Bigger JT Jr, Steinman RC, Rolnitzky LM, Fleiss JL,
Albrecht P, Cohen RJ. Power law behavior of RR-interval variability in
healthy middle-aged persons, patients with recent acute myocardial
infarction, and patients with heart transplants.
Circulation. 1996;93:2142–2151.
14. Goldberger AL, West BJ. Fractals in physiology and medicine. Yale J Biol Med. 1987;60:421–435.[Medline] [Order article via Infotrieve]
15.
Huikuri HV, Mäkikallio TH, Airaksinen KEJ,
Seppänen T, Puukka P, Räihä IJ, Sourander LB.
Power-law relationship of heart rate variability as a predictor of
mortality in the elderly. Circulation. 1998;97:2031–2036.
16. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. CHAOS. 1995;5:82–87.[Medline] [Order article via Infotrieve]
17. Pincus SM, Viscarello RR. Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet Gynecol. 1992;79:249–255.[Medline] [Order article via Infotrieve]
18.
Pincus, SM, Goldberger AL.
Physiological time-series analysis: what
does regularity quantify? Am J Physiol. 1994;266:H1643–H1656.
19. Mäkikallio TH, Seppänen T, Niemelä M, Airaksinen KEJ, Tulppo M, Huikuri HV. Abnormalities in beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. J Am Coll Cardiol. 1996;28:1005–1011.[Abstract]
20.
Huikuri HV, Pikkujämsä SM, Airaksinen KEJ,
Ikäheimo MJ, Rantala AO, Kauma H, Lilja M, Kesäniemi YA.
Sex-related differences in autonomic modulation of heart rate in
middle-aged subjects. Circulation. 1996;94:122–125.
21.
Räihä IJ, Piha SJ, Seppänen A, Puukka
P, Sourander LB. Predictive value of continuous ambulatory
electrocardiographic monitoring in elderly people. BMJ. 1994;309:1263–1267.
22.
Huikuri HV, Niemelä MJ, Ojala S, Rantala A,
Ikäheimo MJ, Airaksinen KEJ. Circadian rhythms of frequency
domain measures of heart rate variability in healthy subjects and
patients with coronary artery disease: effects of arousal and
upright posture. Circulation. 1994;90:121–126.
23.
Akselrod S, Gordon D, Madwed JB, Snidman NC, Shannon
DC, Cohen RJ. Hemodynamic regulation:
investigation by spectral analysis. Am J
Physiol. 1985;249:H867–H875.
24.
Task Force of the European Society of
Cardiology and the North American Society of Pacing and
Electrophysiology. Heart rate variability: standards of measurement,
physiological interpretation, and clinical use.
Circulation. 1996;93:1043–1065.
25. Kales A, Kales JD. Sleep disorders: recent findings in the diagnosis and treatment of disturbed sleep. N Engl J Med. 1974;290:487–499.
26. Mäkikallio TH, Hoiber S, Kober L, Torp-Pedersen C, Peng CK, Goldberger AL, 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. 1999;83:836–839.[Medline] [Order article via Infotrieve]
27.
Ho KKL, Moody GB, Peng CK, Mietus JE, Larson MG, Levy
D, 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. 1997;96:842–848.
28. Mäkikallio TH, Seppänen T, Airaksinen KEJ, Koistinen J, Tulppo MS, Peng CK, Goldberger AL, Huikuri HV. Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction. Am J Cardiol. 1997;80:779–783.[Medline] [Order article via Infotrieve]
29. Mäkikallio TH, Ristimäe T, Airaksinen KEJ, Peng CK, Goldberger AL, Huikuri HV. Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures. Am J Cardiol. 1998;81:27–31.[Medline] [Order article via Infotrieve]
30. Ivanov PC, Amaral LAN, Goldberger AL, Stanley HE. Stochastic feedback and the regulation of biological rhythms. Europhys Lett. 1998;43:363–368.Effects of aging from early childhood to advanced age on 24-hour heart rate dynamics were studied in 114 healthy subjects (age range, 1 to 82 years). New methods based on fractal scaling and nonlinear dynamics (chaos theory) were used in addition to traditional measures of heart rate variability. Children show complexity and fractal correlation properties of R-R interval dynamics comparable to those of young adults, despite lower overall heart rate variability. Healthy aging results in R-R interval dynamics with higher regularity and predictability and altered fractal scaling consistent with a loss of complex variability.[Medline] [Order article via Infotrieve]
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 M.-K. Yum, J.-T. Kim, and H.-S. Kim Increased non-stationarity of heart rate during general anaesthesia with sevoflurane or desflurane in children Br. J. Anaesth., June 1, 2008; 100(6): 772 - 779. [Abstract] [Full Text] [PDF]
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 D. T. Schmitt and P. Ch. Ivanov 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 Regulatory Integrative Comp Physiol, November 1, 2007; 293(5): R1923 - R1937. [Abstract] [Full Text] [PDF]
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A. Porta, T. Gnecchi-Ruscone, E. Tobaldini, S. Guzzetti, R. Furlan, and N. Montano Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt J Appl Physiol, October 1, 2007; 103(4): 1143 - 1149. [Abstract] [Full Text] [PDF]
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 M. Varela, J. Churruca, A. Gonzalez, A. Martin, J. Ode, and P. Galdos Temperature Curve Complexity Predicts Survival in Critically Ill Patients Am. J. Respir. Crit. Care Med., August 1, 2006; 174(3): 290 - 298. [Abstract] [Full Text] [PDF]
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 A. J. Hautala, T. Rankinen, A. M. Kiviniemi, T. H. Makikallio, H. V. Huikuri, C. Bouchard, and M. P. Tulppo Heart rate recovery after maximal exercise is associated with acetylcholine receptor M2 (CHRM2) gene polymorphism Am J Physiol Heart Circ Physiol, July 1, 2006; 291(1): H459 - H466. [Abstract] [Full Text] [PDF]
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F. Beckers, B. Verheyden, and A. E. Aubert Aging and nonlinear heart rate control in a healthy population Am J Physiol Heart Circ Physiol, June 1, 2006; 290(6): H2560 - H2570. [Abstract] [Full Text] [PDF]
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 T. T. Laitio, H. V. Huikuri, J. Koskenvuo, J. Jalonen, T. H. Makikallio, H. Helenius, E. S.H. Kentala, J. Hartiala, and H. Scheinin Long-term alterations of heart rate dynamics after coronary artery bypass graft surgery. Anesth. Analg., April 1, 2006; 102(4): 1026 - 1031. [Abstract] [Full Text] [PDF]
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 M. P. Tulppo, A. M. Kiviniemi, A. J. Hautala, M. Kallio, T. Seppanen, T. H. Makikallio, and H. V. Huikuri Physiological Background of the Loss of Fractal Heart Rate Dynamics Circulation, July 19, 2005; 112(3): 314 - 319. [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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 A. M. Makikallio, T. H. Makikallio, J. T. Korpelainen, K. A. Sotaniemi, H. V. Huikuri, and V. V. Myllyla Heart rate dynamics predict poststroke mortality Neurology, May 25, 2004; 62(10): 1822 - 1826. [Abstract] [Full Text] [PDF]
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T. T. Laitio, H. V. Huikuri, T. H. Makikallio, J. Jalonen, E. S. H. Kentala, H. Helenius, O. Pullisaar, J. Hartiala, and H. Scheinin The Breakdown of Fractal Heart Rate Dynamics Predicts Prolonged Postoperative Myocardial Ischemia Anesth. Analg., May 1, 2004; 98(5): 1239 - 1244. [Abstract] [Full Text] [PDF]
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 J.E. Naschitz, I. Rosner, M. Rozenbaum, M. Fields, H. Isseroff, J.P. Babich, E. Zuckerman, N. Elias, D. Yeshurun, S. Naschitz, et al. Patterns of cardiovascular reactivity in disease diagnosis QJM, March 1, 2004; 97(3): 141 - 151. [Abstract] [Full Text] [PDF]
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 A. Schumacher Linear and Nonlinear Approaches to the Analysis of R-R Interval Variability Biol Res Nurs, January 1, 2004; 5(3): 211 - 221. [Abstract] [PDF]
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A. J. Hautala, T. H. Makikallio, A. Kiviniemi, R. T. Laukkanen, S. Nissila, H. V. Huikuri, and M. P. Tulppo Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects Am J Physiol Heart Circ Physiol, October 1, 2003; 285(4): H1747 - H1752. [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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I. Perlstein, N. Sapir, J. Backon, D. Sapoznikov, R. Karasik, S. Havlin, and A. Hoffman Scaling vs. nonscaling methods of assessing autonomic tone in streptozotocin-induced diabetic rats Am J Physiol Heart Circ Physiol, September 1, 2002; 283(3): H1142 - H1149. [Abstract] [Full Text] [PDF]
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 D. P Francis, K. Willson, P. Georgiadou, R. Wensel, L C. Davies, A. Coats, and M. Piepoli Physiological basis of fractal complexity properties of heart rate variability in man J. Physiol., July 15, 2002; 542(2): 619 - 629. [Abstract] [Full Text] [PDF]
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 F. Lombardi, T. H Makikallio, R. J Myerburg, and H. V Huikuri Sudden cardiac death: role of heart rate variability to identify patients at risk Cardiovasc Res, May 1, 2001; 50(2): 210 - 217. [Full Text] [PDF]
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J. J. Goldberger, S. Challapalli, R. Tung, M. A. Parker, and A. H. Kadish Relationship of Heart Rate Variability to Parasympathetic Effect Circulation, April 17, 2001; 103(15): 1977 - 1983. [Abstract] [Full Text] [PDF]
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 T. H. Makikallio, H. V. Huikuri, A. Makikallio, L. B. Sourander, R. D. Mitrani, A. Castellanos, and R. J. Myerburg Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects J. Am. Coll. Cardiol., April 1, 2001; 37(5): 1395 - 1402. [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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S. M. Pikkujamsa, T. H. Makikallio, K. E. J. Airaksinen, and H. V. Huikuri Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects Am J Physiol Heart Circ Physiol, March 1, 2001; 280(3): H1400 - H1406. [Abstract] [Full Text] [PDF]
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