Vol. 277, Issue 1, H261-H267, July 1999
Even slight movements disturb analysis of cardiovascular
dynamics
Jacques-Olivier
Fortrat,
Cédric
Formet,
Jean
Frutoso, and
Claude
Gharib
Laboratoire de Physiologie de l'Environnement, Faculté de
Médecine Lyon Grange-Blanche, 69373 Lyon Cedex 08, France
 |
ABSTRACT |
We hypothesized
that spontaneous movements (postural adjustments and ideomotion)
disturb analysis of heart rate and blood pressure variability and could
explain the discrepancy between studies. We measured R-R intervals and
systolic blood pressure in nine healthy sitting subjects during three
protocols: 1) no movement allowed,
2) movements allowed but not
standing, 3) movements and standing
allowed. Heart rate and blood pressure were not altered by movements.
Movements with or without standing produced a twofold or greater
increase of the overall variability of R-R intervals and of the
low-frequency components of spectral analysis of heart rate
variability. The spectral exponent 
of heart rate variability (1.123 at rest) was changed by movements (1.364), and the percentage of
fractal noise (79% at rest) was increased by standing (91%, coarse-graining spectral analysis). Spontaneous movements
could induce a plateau in the correlation dimensions of heart rate
variability, but they changed its nonlinear predictability. We suggest
that future studies on short-term cardiovascular variability should control spontaneous movements.
heart rate variability; blood pressure variability; ideomotion; fractal noise; nonlinear dynamics
| |
INTRODUCTION |
FOURIER ANALYSIS and its derivatives have provided an
apparently simple and easily implemented tool for cardiovascular
physiology and clinical studies. Despite a large number of experiments
that focus on the mechanisms underlying cardiovascular fluctuations, the meaning of the spectral parameters of heart rate variability (HRV)
and blood pressure variability (BPV) is still under debate. The many
different measures of spectral analysis led the European Society of
Cardiology and the North American Society of Pacing and
Electrophysiology to constitute a Task Force charged with the
responsibility of developing appropriate standards (16). The meaning of
low-frequency and very low-frequency (LF and VLF, respectively)
spectral power seems to be discussed more (7, 8) than that of the high
frequencies (HF) that are related to respiratory influences (16). The
LF of HRV could result from the slow regulatory mechanisms, but
physical and ordinary activity clearly influence them (2), and the VLF
are clearly influenced by one-way transitions from one physiological
state to another (14). Nonlinear phenomena are certainly involved in
the genesis of cardiovascular variability. Despite the lack of
long-term linear autocorrelations, the current values of a series can
be correlated with both immediate and long-term past values of the
series through complex dynamics (6). The fractal pattern of HRV that
implies long-term correlation within the beat-by-beat variability is
known as the 1/f component in the
frequency domain; it largely influences the LF of cardiovascular
variability. This fractal component could be a potential source of
discrepancy in the interpretation of LF. Moreover, the recommendations
of the Task Force are based on human recordings and cannot take into
account the specificity of recordings of unrestrained animals.
Movements are not controlled during such recordings, and they are not
always controlled during human recordings (Holter). Fractal noise and
the noise induced by movements are a potential source of discrepancy in
interpretation of LF spectral markers. We hypothesized that spontaneous
slight movements such as postural adjustments and ideomotor movements (i.e., unconscious movements when attention is withdrawn) are a source
of noise in LF, VLF, and long-term autocorrelations of HRV and BPV and
could bias the analysis and interpretation of HRV and BPV.
| |
METHODS |
Subjects.
Nine healthy volunteers (6 women and 3 men) with a mean age of 23 yr
(range 19-28 yr) took part in this study. The subjects had a mean
weight of 57 kg (range 44-66 kg) and a mean height of 1.68 m
(range 1.58-1.81 m). They had no history of cardiopulmonary disease, and none was taking any medication. One of them was a light
smoker (2 cigarettes/day). They had a resting heart rate of 72 beats/min (range 57-84 beats/min) and a resting systolic blood
pressure of 127 mmHg (range 103-135 mmHg). Each subject received a
complete description of the procedures and potential risks involved and
signed a consent form. The data collection procedure was approved by
the Comité Consultatif pour la Protection des Personnes dans la
Recherche Biomédicale Midi Pyrénée Toulouse I.
Experimental protocols and procedures.
Surface electrocardiogram and Finapres blood pressure were recorded for
each subject during three different protocols: relative steady state
(SM), free recording (ST), and strictly steady state (SS). All tests
were done in the sitting position. During SM, the subject was only
asked to remain quietly sitting without instruction about slight
movements. During ST, the subject was asked to stand at least four
times during the recording. SM and ST were always performed first (in a
random order) to avoid the subjects receiving any suggestion of
controlling slight spontaneous movements. The SS period consisted of a
strictly motionless recording. The subjects were instructed to avoid
any postural adjustment or any ideomotor movement, even very small
movements. Respiratory rate was not controlled. Subjects were made
familiar with the equipment and with the experimental room before the
first data collection session. They were asked to breathe quietly and
to be as relaxed as possible, but not to sleep, and to keep their eyes
open. The finger blood pressure cuff (Finapres 2300, Ohmeda, Englewood,
CO) was carefully placed on the nondominant arm. The subject carried
this arm in a sling to keep the cuff at heart level and to avoid
hydrostatic pressure effects. Each recording was performed in the
morning in a very quiet, light-attenuated room with light ambient
classical music. The subject was alone with the experimenter, who was
as quiet as possible so as not to disturb the subject. The recordings were extended long enough to obtain at least 2,048 cardiac beats (30-40 min). The Finapres was active only after the first 10 min and was allowed to stabilize before the servo-reset mechanism was
disabled to permit continuous collection of at least 1,024 cardiac beats.
Data acquisition and pretreatment.
A peak detection circuit was used to discriminate the R wave from the
electrocardiogram. The impulse train was processed in real time on a
personal computer via an analog-to-digital converter (DAS-16G,
Keithley-Metrabyte, Taunton, MA) at a sampling frequency of 1,000 Hz
and a resolution of 12 bits. Beat-by-beat R-R intervals and systolic
blood pressures were stored for later analysis. Each series was
searched for abnormal values before analysis. Very few abnormal values
were identified (0-0.5%); they were defined as values 25% larger
or smaller than the preceding value. Abnormal intervals were typically
caused by a missed beat or by triggering on the T wave as well as the
QRS complex. A beat was inserted when one was missed, whereas the two
short-interval values were deleted when the T wave was triggered and a
beat was inserted by interpolation. The filtered R-R interval and
systolic blood pressure data were then aligned sequentially to obtain
equally spaced samples of R-R interval and systolic blood pressure
(4).
Time series analysis.
The means and standard deviations of the R-R interval and systolic
blood pressure were obtained for each recording. For each subject we
obtained three series of 2,048 R-R intervals. In the middle of these
series the corresponding systolic blood pressure was also obtained for
1,024 beats. An experimenter, familiar with cardiovascular dynamics
analysis but blinded with respect to the phase during which the data
were recorded, selected a visually estimated stationary series of 256 beats (both R-R intervals and systolic blood pressures) from the
1,024-beat series. Each series of 1,024 and 256 beats (both R-R
intervals and systolic blood pressure) was analyzed by a fast Fourier
transform (FFT). The spectral parameters assessed were those
recommended by the Task Force (16).
The fractal components of HRV and BPV were analyzed by subjecting the
1,024-data point time series to coarse-graining spectral analysis
(CGSA; Ref. 17). CGSA discriminated fractal random walks from simple
harmonic motion on the basis of the fact that the original and rescaled
(coarse grained) time series had random phase relationships only for
fractal signals (17). Two rescaled versions
[x(ht)]
of the original time series
[x(t)]
were obtained by taking the scaling factors of
h = 0.5 and
h = 2. This had the effect of sampling
every second value (h = 0.5) or
holding each value for two sample points
(h = 2). The fractal component was plotted in a log-power versus log-frequency plane, and the spectral exponent () was estimated as the slope of the linear regression of
this plot from 2.5% of the Nyquist frequency to 0.3 Hz or higher if
the plot was still linear at higher frequencies (3). The series of
2,048 R-R intervals were analyzed by the methods of nonlinear
prediction (15) and correlation dimension (1) to test for evidence of
potential nonlinear dynamics.
Nonlinear prediction.
The time series was first transformed to obtain the first difference
time series, as in the study of Sugihara and May (15). We then
constructed a vector in an
M-dimensional euclidean space by
embedding samples of the time series. The embedding dimension M was the number of coordinates of
embedding space. The time series was analyzed for
M of 2, 4, 6, 8, 10, 12, 14, 16, and
18. The lag time was set at one beat, as was done by Sugihara and May (15). The time series was analyzed for each
M by the method of nonlinear
prediction to detect any correlation (
) between the observed and the
predicted values according to the prediction time (number of beats).
This method provided information about the dynamics of the time series.
For a random stochastic process, should be low, close to zero, and
independent of prediction time. In a deterministic linear process, should be high and independent of prediction time. For a deterministic
nonlinear process, should be high, with an abrupt drop as the
prediction time increases. The results were fitted in a
three-dimensional plot. The x-axis was
the prediction time (number of beats), the y-axis was , and the
z-axis was
M.
Correlation dimension.
The data were analyzed for the same M
as described in Nonlinear prediction (from 2 to
18), but the vectors were constructed with the raw time series. The lag
time was set at three beats (18). The time series was analyzed for each
M by the method of correlation
dimension (1). This method provides information on the static
properties of the time series. In a random stochastic process, the
correlation dimension should increase as
M increases. In a deterministic
nonlinear process, the correlation dimension should increase and then
reach a plateau at a noninteger value, when
M increases. However, the stochastic
1/f noise could bias these analyses
(13). Because HRV has a 1/f component,
it was necessary to determine whether the outcome of the nonlinear
analysis might have been caused by a random stochastic process. This
test was done by completing the same analysis on a
distribution-conserved isospectral surrogate (DCIS) data set. The
standard isospectral surrogate data set was computed for each time
series. We then generated DCIS data sets (10). Any differences between
observed and DCIS series should come from the nonlinear dynamics of the observed series.
Statistical methods.
Data are presented as means ± SE. A between-period comparison was
made by a Friedman test for each spectral analysis method, short FFT,
long FFT, and CGSA. If an overall significant difference was found, we
looked for the difference by means of Wilcoxon tests for paired data.
Correlations among the short-FFT, long-FFT, and CGSA parameters were
assessed by a Spearman rank test.
We looked for differences between the observed and DCIS series
nonlinear predictions and the correlation dimensions for each period
(SS, SM, and ST). This comparison was completed by two-way analysis of
variance for each M for nonlinear
prediction and by two-way analysis of variance for correlation
dimensions. Nonlinear predictions and correlation dimensions were
analyzed for differences between periods by a two-way analysis of
variance only for M where the
differences between observed and DCIS series were highly significant (P 
0.01), i.e., only when the
nonlinear prediction profile was probably caused by the nonlinear
dynamics of the data rather than by measurement noise. In other cases,
the significance level was set at P 0.05.
| |
RESULTS |
R-R interval and systolic blood pressure were not significantly
different between protocols despite a slight increase in systolic blood
pressure during SM and ST recording (SS: 840 ± 38 ms and 127.7 ± 9.1 mmHg; SM: 857 ± 41 ms and 134.1 ± 6.5 mmHg; ST: 780 ± 33 ms and 141.2 ± 5.5 mmHg). Examples of collected R-R
interval and systolic blood pressure time series are shown in Fig.
1.
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Fig. 1.
Example of R-R interval (bottom
curve) and systolic blood pressure
(top
curve) time series (1,024 points)
during 3 types of sitting recordings:
1) strictly steady state (SS), where
no movement was allowed; 2) relative
steady state recording (SM), where slight movements were allowed; and
3) free recording (ST), where both
slight movements and standing were allowed.
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|
Spectral analysis.
The overall R-R interval variability assessed by the total power of the
spectra was significantly increased by slight movements (SM) in
comparison to the SS period for short-FFT analysis (Table 1; Fig. 2).
With longer FFT analysis [1,024 beats (FFT1024), Table
2], the HRV total power during the ST
period was also different from that during the SS period. There was no
difference in the total power of BPV assessed by FFT for 256 points
(FFT256) or FFT1024, but the SS period was different from both SM and
ST periods when assessed by CGSA. All these effects disappeared when
the VLF power was subtracted from the total power. For the VLF and LF
components of HRV, both the SM and ST periods were different from SS
regardless of the length of data collection analyzed by means of FFT,
whereas only the LF and VLF SM were different from SS when analyzed by
CGSA. There was no difference between periods in the spectral component
of systolic blood pressure assessed by FFT256 or FFT1024. The
normalized HF power and the ratio of LF to HF power of BPV were
significantly changed by SM and ST when assessed by CGSA (Table
3).
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Fig. 2.
Example of harmonic component from coarse-graining spectral analysis of
R-R interval (A) and systolic blood
pressure (B) time series during 3 types of sitting recordings (SS, SM, and ST).
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|
Despite these differences most of the spectral parameters computed by
means of FFT256, FFT1024, and CGSA were well correlated (P 0.001). The only parameters that
were not correlated were the normalized LF power of HRV (between FFT256
and CGSA, r2 = 0.124) and the ratio of
LF to HF power of BPV (between CGSA and both FFT,
r2 = 0.217;
r2 = 0.132 for
FFT256 and FFT1024).
Fractal analysis.
The fractal component of BPV did not change regardless of the period
considered (SS, SM, or ST) (Table 3). Standing during recording (ST)
induced an increase of the percentage of fractal noise in HRV. Slight
movements without or with standing (SM and ST) induced an increase in
the spectral exponent of HRV.
Nonlinear analysis of HRV.
During all three periods (SS, SM, and ST) the pattern of nonlinear
prediction for R-R interval series was one of an initially high value
that decreased as the prediction time (number of beats) increased (Fig.
3). There were highly significant
differences (P 0.01) between the
observed series and the DCIS nonlinear prediction of R-R interval
series for each M
(M = 2-18). These findings
support the conclusion of nonlinear dynamics in the pattern of HRV. The
nonlinear prediction of the observed R-R interval series during the SS
period is different from that during both SM and ST periods for all
M (P 0.01; SM vs. ST, no significant difference). The correlation
dimensions of the R-R interval series were different between observed
and DCIS only for M = 6 and 14-18 during SS and for M = 6-18 during
SM, and no difference was observed during ST (Fig.
4). There was no difference in correlation
dimensions between periods.
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Fig. 3.
Nonlinear predictions of R-R interval series computed in embedding
dimensions (ED) from 2 to 18 during 3 types of sitting recordings
[SS (A), SM
(B), and ST
(C)]. , mean prediction
coefficients of 9 subjects. Nonlinear predictions of R-R interval
series during SS were different from those during both SM and ST for
each ED (P 0.01; SM vs. ST, no
significant difference).
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Fig. 4.
Correlation dimensions
(D2) of R-R
interval series ( ) and their distribution-conserved isospectral
surrogate series (DCIS,  ) computed in ED from 2 to 18 during 3 types
of sitting recordings [SS (A),
SM (B), and ST
(C)]. Values are means of 9 subjects; * P 0.01.
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| |
DISCUSSION |
The primary aim of this experiment was to assess the validity of
comparisons between cardiovascular variability studies in which data
are collected 1) on human healthy
volunteers in a laboratory setting,
2) on patients by means of regular
or Holter electrocardiogram, and 3)
on freely moving animals. Our results show that even slight spontaneous
movements influence spectral analysis and change the nonlinear dynamics
of HRV. Comparisons between studies must take into account differences
in data collection procedure. This could explain the discrepancy in
interpretation of the harmonic components of spectral analysis,
especially for LF, and the lack of consensus about the nonlinear
dynamics of HRV.
DeBoer's model (5) identifies the Mayer wave (0.1 Hz in humans) as a
resonance phenomenon caused by the delay in the sympathetic control
loop of the baroreflex despite an oversimplification of the complexity
of the neural cardiovascular control. This supposes that these
oscillation phenomena occur in an unperturbed system and result only
from spontaneous blood pressure and heart rate fluctuations. Exploring
human resting cardiovascular regulation in a strictly controlled steady
state avoids any external perturbation of the reflex loops. Bernardi et
al. (2) demonstrated that the results of spectral analysis of HRV
collected by means of Holter electrocardiogram are influenced by
physical activity. Our study extends and complements this previous
observation and shows that slight spontaneous movements could increase
the VLF and LF of R-R interval variability. This effect is limited when the time series are analyzed by means of short-term regular FFT (256 points) and when the spectral parameters are normalized. An alteration
in autonomic activity should parallel movements, even slight, in
comparison with the absolute resting sympathetic activity. However,
these movements occurring from time to time should change the
stationarity of the sympathetic activity during the recordings and then
influence the fluctuations related to noise in the cardiovascular time
series. There is no more increase in the LF component, even when
normalized, during ST in comparison to SM (Tables 1, 2, and 3; no
difference between SM and ST regardless of analysis methods used). We
hypothesized that the large one-way transitions resulting from standing
cannot additionally change HRV, which is already noisy because of
spontaneous movements. The bigger standard error of the spectral
parameters during SM and ST than during SS (Tables 1, 2, and 3) is in
agreement with our hypothesis that slight movements induce noise in
cardiovascular time series. Blood pressure seemed less sensitive to
these disturbances. The cardiovascular response of a slight movement
involving only one limb could be compensated for by the cumulated
inertness of the other vascular beds, which remained at rest. However,
the noise that is present in the LF components of BPV recorded by means
of an infrared photoplethysmograph (12) could also mask the noise
induced by the slight movements. The SS spectral exponent of HRV
was close to 1 (Table 3), and the percentage of fractal noise was close
to 80%, as expected during a rest recording (17). A bigger is
expected for standing HRV (3). However, slight movements increased this
exponent even on sitting recordings (SM), meaning that the fractal
component was less organized.
Instructing a subject to stay quiet and keep his or her eyes open for a
few minutes will result in repeated postural adjustments and ideomotor
movements, and this is why we usually train our subjects to be quiet
and motionless during the data recording. This is difficult in animals,
which are usually unrestrained to avoid anesthetic effects. The purpose
of a Holter electrocardiogram is to record heart rate during daily
regular activity, during which more movement means more variability.
There is no way to compare such different recordings because of the
data length differences (100,000 beats for 24-h and 300 beats for 5-min
human recordings) or the heart rate difference (60 beats/min in humans,
350 beats/min in rats). The recording cables and the sling limited the
spontaneous movements of our subjects. Our results show that the
cardiovascular dynamics analysis is clearly different between SS and SM
despite this limitation. We did not analyze the nonlinear dynamics of blood pressure because we did not record the Finapres signal during all
40 min of the protocols. The quality of the signal could be limited
because the servo mechanism was turned off over such a long duration.
The nonlinear prediction patterns of HRV are compatible with those of
chaotic time series with a dropping pattern as the prediction time
increases, but the quality of the prediction is better on the SS
recordings than on those from SM or ST (Fig. 3). The noise induced by
slight spontaneous movements and standing probably limits the quality
of the nonlinear prediction. Whether there are nonlinear dynamics in
the patterns of HRV is still debated (6, 9). This is illustrated by the
unclear difference between the correlation dimensions of the observed
and DCIS R-R interval series recorded during SS. When slight movements
were not restricted (SM), there was evidence for nonlinear dynamics in
the pattern of HRV. This would support the concept that under normal
resting conditions, regular postural adjustments and spontaneous
movements may induce chaos in the pattern of HRV. The rhythm of the
pathological stereotypes was demonstrated to be nonlinear (11). It is
possible that the rhythm of ideomotion of some subjects is nonlinear,
inducing a nonlinear disturbance in cardiovascular fluctuations. The
correlation dimensions (mean of 9 subjects) of SM recordings did not
reach a plateau despite the clear difference between observed and DCIS series and the high embedding dimensions (Fig. 4). However, individual results are very informative. Figure 5
shows the correlation dimension of a subject. The pattern of the
observed correlation dimension during the SM recording is typical of a
chaotic time series (plateau). Two subjects presented this typical
pattern during SM. The correlation dimensions of the other subjects are
only lower during SM than during SS, which explains why the mean curve
did not reach a plateau during SM.
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Fig. 5.
D2 of observed
R-R interval series (subject F) and
their DCIS computed in ED from 2 to 18 during 3 types of sitting
recordings (SS, SM, and ST).
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|
We conclude that more than one-half of cardiovascular variability is
caused by spontaneous movements when they are not controlled during
data recordings. These spontaneous movements disturb spectral analysis
and nonlinear prediction of the heart rate variability and can induce a
plateau in the result of its correlation dimensions. We suggest that
future studies on short-term cardiovascular variability must control
spontaneous movements or at least precisely describe the recording
conditions and that the specific value of the very low frequency
component could provide a crude assessment of the "quality" of
the recording conditions.
| |
ACKNOWLEDGEMENTS |
The authors thank Dr. John Carew for the English language
correction of this paper and thank the subjects for their willing cooperation.
| |
FOOTNOTES |
This work was supported by grants from Centre National d'Etudes
Spatiales and Groupement d'Intérêt Public Exercice,
St-Etienne, France.
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. §1734 solely to indicate this fact.
Address for reprint requests: J.-O. Fortrat, Laboratoire de Physiologie
de l'Environnement, Faculté de Médecine Lyon
Grange-Blanche, 8, Ave. Rockefeller, 69373 Lyon Cedex 08, France.
Received 19 August 1998; accepted in final form 11 March 1999.
| |
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Am J Physiol Heart Circ Physiol 277(1):H261-H267
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Copyright © 1999 the American Physiological Society
TOP
ABSTRACT
INTRODUCTION
