(Circulation. 1999;99:3279-3285.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Origins of Heart Rate Variability
Inducibility and Prevalence of a Discrete, Tachycardic Event
From the Cardiovascular Research Group, University of Calgary, Calgary, Alberta, Canada.
Correspondence to Dr Robert Sheldon, University of Calgary, Health Sciences Centre, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada. E-mail sheldon{at}ucalgary.ca
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Abstract |
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Background—We propose that heart period sequences are linearly organized, like sentences, and that there is a lexicon of recurrent, similarly shaped transient structures like words. Each word (or lexon) has a characteristic physiological basis. One potential lexon is the transient, reversible tachycardia that is induced by exercise initiation under laboratory conditions. We hypothesized that this lexon was inducible and observable on ambulatory ECGs of most or all subjects, was morphologically similar in both induced and detected bursts, and shared a plausible origin in both circumstances.
Methods and Results—Ten healthy subjects (mean age, 36 years) underwent a protocol in which subjects rolled themselves from supine to lateral decubitus positions and back. Transient tachycardias ("bursts") were seen in 36 of 40 rollovers. Bursts were characterized by an initial monoexponential heart period decay (K=0.39±0.23 s-1), a maximum heart period decrease of 277±109 ms after 10.8±4.5 seconds, and a subsequent return to baseline 23.3±10.8 seconds after roll initiation. The roll-induced bursts were detected with 97% sensitivity and 99% specificity with a search algorithm that incorporated morphological parameters. In 24-hour ambulatory ECGs of 10 healthy subjects (mean age, 38 years; range, 17 to 69 years), 117±59 bursts were detected. Induced and detected bursts were similar in most morphological parameters. Finally, many bursts occurred at night, when rolling over also occurs.
Conclusions—Bursts are inducible, transient tachycardias that occur clinically and constitute a lexon with an understandable physiology.
Key Words: heart rate • tachycardia • electrocardiography • physiology
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Introduction |
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Measures of heart rate variability are often used as probes into the dynamics of the cardiovascular control system. Several mathematical approaches have been used, including spectral analysis and nonlinear analysis.1 2 3 4 These both assume the presence of long-term, deterministic signals. However, analyses with nonlinear predictability, correlation dimension, and information scaling have shown that heart period sequences do not have the characteristics of signals with long-term determinism.5 6 7 What then might be the origin of heart period variability?
Heart period sequences might be linear concatenations of temporally localized events. Time-frequency decomposition studies8 and the broad spectral width of the 0.10-Hz low-frequency band related to hemodynamic control1 2 3 4 argue that the origins of this component are transient and therefore temporally localized. Similarly, the broad spectral width of the high-frequency band and its inconstant timing and amplitude1 2 3 4 suggest that it too arises from temporally localized events. Finally, the sources of the ultralow-frequency band are localized around the times of going to bed and getting up.9 Thus, the sources of heart period variability seem to be discontinuous and localized in specific segments of the sequences.
Recently, predictability analyses of nonlinear systems have contributed complementary findings. These techniques compare results obtained from analyses of original sequences with those from sets of Fourier phase-randomized surrogate sequences. The surrogate sequences lack the deterministic features of their parent sequences. Kanters et al5 6 and we7 showed that parent heart period sequences from resting subjects were more predictable than their surrogates and that this predictability lasted for only 4 to 30 beats. Predictability is due to the recurrence of similar subsequences, suggesting that the parent sequences contain recurring subsequences.
We propose that heart period sequences are linearly organized, like sentences. There is a lexicon of recurrent, similarly shaped transient structures like words, and each word has a characteristic physiological basis.10 We coin the word "lexon" to refer to the meaningful transient structures in heart period sequences. These structures should fulfill 4 criteria:
- The lexon should be reliably inducible in most or all
healthy persons.
- The lexon should be detectable on ambulatory heart period
recordings in most or all healthy persons.
- The lexons detected in ambulatory recordings should
resemble lexons induced under controlled circumstances in
3
features.
- The behavioral or physiological factors
that are associated with the detected lexon should be related to those
factors that induce the lexon under controlled circumstances.
In the present study, we determined whether 1 lexon candidate, the brief tachycardia that occurs at the initiation of exercise,11 12 13 satisfied these criteria. We named these events "bursts" in recognition of their brief tachycardic nature. In preliminary studies, we noted that bursts that were morphologically similar to those reported herein were induced when subjects rolled themselves from supine to lateral decubitus positions and vice versa. Because rollover actions are expected at night while most other physical activities are curtailed, and because bursts are abrupt, large-magnitude structures, we considered them a favorable lexon candidate. Our specific objectives were as follows: (1) to determine whether there was a characteristic reversible tachycardia reliably induced by supine rolling actions in normal subjects; (2) to determine whether similar bursts occurred on ambulatory ECGs in normal subjects; (3) to determine whether induced and detected bursts were similar; and (4) to determine whether the induced and detected bursts plausibly shared a common origin.
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Methods |
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There were 2 protocols. In protocol 1, we induced bursts under controlled circumstances to parameterize their morphology. In protocol 2, we used these morphological parameters to locate bursts in ambulatory recordings and to validate their identity with the protocol 1 bursts. All subjects were healthy, consenting volunteers who were not receiving medications.
Protocol 1: Bursts Induced by Rolling Actions
Data Acquisition
Ten healthy subjects (6 men, 4 women; mean age, 34 years
[range, 20 to 49 years]) lay quietly supine for 10 minutes. They then
rolled from supine to a right lateral decubitus position for 1 minute,
then rolled back to supine for 1 minute, then rolled to a left lateral
decubitus position for 1 minute, and finally rolled back to supine for
1 minute. Each subject completed the 4 rolling actions. Subjects were
instrumented with ECG leads and the Finapres noninvasive blood pressure
monitor (Ohmeda) finger cuff. Outputs were digitized at 200 Hz,
delineated, and manually overread in CVSoft (Odessa Software). We used
the time of the first R wave of each heart period as the time of that
beat's heart period value. Mean arterial pressure (MAP)
values for each beat were calculated as MAP=(systolic
pressure/3)+[2x(diastolic pressure/3)]. The
resulting sequences were stored in Matlab (The Mathworks).
Morphological Parameters
We needed morphological parameters to construct an
algorithm to detect bursts in heart period sequences and to compare the
gold-standard induced bursts with the detected bursts. We first
reviewed the heart period sequences spanning the rollovers. A burst was
considered to have occurred if the sequence of heart period values
visually resembled those reported previously.11 12 13
Thirty-six of 40 rollovers induced acceptable bursts. Next, we
identified and quantified morphological features shared by all 36
bursts.
The 3 general features of a burst are its duration, magnitude,
and shape (Figure 1
; Table 1). The duration
parameters are the intervals between the initial beat, the
minimum heart period beat, and the final beat. Respiratory sinus
arrhythmia was evident in the interburst intervals of most
subjects (Figure 2), which made
delineation of the initial and final beats subject to imprecision. To
circumvent this, we also delineated the first beat in a burst to exceed
50% of the tachycardic magnitude and the first beat to exceed 50% of
the heart period recovery.
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, Beats included in burst. 
, Beats fitted by
monoexponential decay model (gray curve). See Table 1
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The magnitude parameters are the heart periods at baseline
and at the trough of the burst, as well as the difference between them
(Figure 1
; Table 1). The shape parameters
depended on the observation that the initial rapid heart period decay
resembled a monoexponential decay function. We fit the
heart period decay to a monoexponential curve and
obtained a theoretical asymptote heart period, a decay rate
constant (K), and a correlation coefficient quantifying the
fit of the model to the heart period data. We also analyzed the
MAP sequences recorded for each of the bursts and report the
magnitude and timing of the hypotension.11 12 13
Burst-Detection Algorithm
Our goal was to detect bursts using the fewest
parameters. We started with broad inclusion criteria for
the simplest of morphological parameters, which ensured
initial high sensitivity for the detection algorithm.14 We
then iteratively added new parameters, each time finding
the optimal value(s) for the newly added parameter. Six
cycles of this process identified a set of criteria with high
sensitivity and specificity (see Table 1 for definitions of
parameters). These included 2 magnitude
parameters (
HPmag and
HPmin-HPasymptote),
2 duration parameters (tmin and
t50%rec), a shape parameter
(expfit), and a parameter (stability of 15 preceding beats)
designed to eliminate false detection of high-magnitude, transient
bradycardic events described previously.15 16 The order of
the parameters and the optimized values are
presented in the Results section. The gold-standard–positive
population for the receiver operator characteristic analyses
consisted of the 36 bursts induced in protocol 1; the negative controls
were all large-magnitude tachycardic events in the protocol 1 heart
period sequences. These were defined broadly by the first 2
criteria of the algorithm: a heart period decrease 102 ms over a time
interval of 3 to 24 seconds.
Protocol 2: Detection of Bursts on Ambulatory ECGs
Data Acquisition
Ten subjects (6 males, 4 females; mean age, 38 years [range, 17
to 69 years]) underwent ambulatory ECG for 24 hours. The
recording system incorporated a synchronization pulse to reduce
variability due to tape-speed fluctuations.9 The
recordings were analyzed with the Marquette 8000
scanner with version 5.7 of the Marquette Arrhythmia
Analysis Program to identify and label each QRS. Entire
recordings were analyzed to eliminate cycles in which
ventricular beats had abnormal morphological
characteristics or were without normal P waves. These beats were
replaced by linear interpolation between adjacent normal beats.
Unclassified beats were corrected manually and verified. No
recordings had ectopy more severe than isolated complexes or
atrial couplets. The proportion of ectopic complexes ranged from 0 to
0.17%, and no patients had >7 ectopic beats/h. The corrected
sequences of 
105 beats were then transferred
into Matlab.
Derivation of Surrogate Control Sequences
We needed an objective way to distinguish the putative bursts in
the original heart period sequences from other transient
tachycardias that may have arisen by chance. To do this, we
first derived phase-randomized surrogate sequences for each original
sequence. These surrogate sequences have the same linear temporal
correlation as their original sequences but lack any phase-dependent
(or deterministic) structures that may be present in the original
sequences. To construct surrogates,5 6 7 we made fast
Fourier transformations (FFTs) of the original heart period sequences,
replaced the phases of the Fourier coefficients with values drawn
randomly from a uniform distribution between 0 and 2
, then applied
the inverse FFT.
Statistical Analysis
We used Mann-Whitney nonparametric comparisons of
measured variables with idealized parametric distributions
to test for normalcy of distributions. Normal distributions are
reported as mean±SD. Nonnormal distributions are reported as 25%,
median, and 75% quartile values. ANOVAs were performed on grouped
variables of normal distributions, and Kruskal-Wallis tests were
performed on grouped variables of nonnormal distributions. Unpaired
t tests were used to compare normal distributions, and
Mann-Whitney U nonparametric tests were used for
comparing nonnormal distributions. P<0.05 was deemed to be
significant.
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Results |
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Prevalence of Induced Bursts
Thirty-six (90%) of the 40 rolls resulted in bursts that were easily identified by visual inspection. Figure 2
depicts
the 4 recordings that were excluded from the analysis,
as well as 4 bursts (each from a different subject) that were
characteristic of those that were analyzed further. All
subjects produced 2 of the 4 possible bursts, satisfying the first
criterion of generalized inducibility.
Morphological Parameters of Induced Bursts
Table 2 summarizes the values
obtained for morphological parameters of the induced
bursts. Values for all of the morphological parameters of
the induced bursts were normally distributed. Three of the 4 duration
parameters, specifically t50%mag
(5.9±3.0 seconds), t50%rec (13.3±4.7 seconds),
and tburst (23.3±10.8 seconds), were invariant
among the subjects. The fourth duration parameter,
tmin, was significantly different in only 1
subject (ANOVA post hoc analysis). Thus, measures of the
duration of the bursts were generally constant among subjects.
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In contrast, the values of the magnitude parameters were
more variable. These included HPbaseline
(993±189 ms), HPmin (716±104 ms), and
HPmag (277±109 ms). However, this variance
appeared to be due to differences in the baseline heart periods. Figure 3 shows that both
HPmin and HPmag
correlated well (P<0.001;
r2=0.8) with
HPbaseline, which itself was subject dependent.
Thus, the variances of the values of the magnitude
parameters reflect more the variance in the baseline heart
periods rather than intersubject variability in burst magnitude.
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The values of the 2 shape parameters showed limited intersubject variability. The initial heart period decrease was well fit by the monoexponential decay model (expfit=0.98±0.02) in all subjects, but the decay constants (0.39±0.23 ms/s), although similar, were significantly subject dependent. Thus, the values of the shape parameters showed only limited intersubject variability.
Finally, all bursts were accompanied by a relative hypotension. MAP dropped 17.8±11.7 mm Hg over 12.1±5.9 seconds.
Burst-Detection Algorithm
The optimized burst-detection algorithm incorporated 6 criteria:
(1) preceding heart period quiescence, defined as all of the 15 beats
preceding the first beat of the burst having heart periods 78 ms
greater than the trough heart period HPmin; (2) a
high-magnitude drop in heart period, defined as a drop in heart period
HPmag >102 ms; (3) a bounded time to the
trough heart period, defined as tmin between 3.3
and 24 seconds; (4) a bounded time to the point at which heart period
recovered 50% toward its baseline value, defined as
t50%rec between 8.8 and 31.4 seconds; (5) a high
degree of fit of the initial heart period drop to a
monoexponential curve, defined as expfit >0.95; and
(6) close agreement between the theoretical trough heart period from
the monoexponential equation and the measured trough
heart period, defined as
HPmin-HPasymptote
between –63 and 128 ms. The sensitivity of the burst-detection
algorithm for the 36 gold-standard inducible bursts was 97%, and the
specificity for the negative truth set defined by criteria 1 and 2 was
99%. The positive predictive value for the detection algorithm was
95%.
Prevalence and Morphology of Detected Bursts
Bursts were detected with the algorithm in all 10 of the 24-hour
heart period sequences (Table 2). An average of 117±59 detected
bursts (range, 3 to 206 bursts) were obtained for the ten 24-hour heart
period sequences. All subjects had bursts, satisfying the second
criterion of generalized prevalence. Control sequences were derived
from the original sequences by phase randomization. There were
significantly fewer bursts in the control sequences for each of the
original sequences, as well as in all 10 control sequences as a whole
compared with the original population.
The magnitudes of the induced and detected bursts were generally
similar (Table 3). They had statistically
similar t50%mag, tmin, and
t50%rec and similar but not identical total
durations of tburst. Figure 4 depicts the differences in
tburst between induced and detected bursts. The
distributions have similar medians and modes, but detected bursts are
skewed by a number of very long events. These differences are addressed
in the Discussion. In contrast, induced and detected bursts had
different magnitudes. Detected bursts started and finished at shorter
heart periods and had slightly smaller magnitudes. The initial drops in
heart period in both induced and detected bursts were well fit by
monoexponential equations. However, the decay constants
of the detected bursts were slightly but significantly slower. In
general, induced and detected bursts were similar in duration and shape
but different in magnitude, satisfying the third criterion of
morphological similarity.
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Diurnal Distribution of Bursts
The occurrence of bursts with exercise initiation and rolling over
suggests that they should be detectable both at night and during
daytime hours. Figure 5 plots the number
of detected busts per hour over a 24-hour day for the 10 subjects of
protocol 2. Bursts occurred throughout the recordings, and
their incidence was independent of time of day (P=0.9).
Figure 6 depicts detected bursts that
occurred during normal sleeping and waking hours. The bursts are easily
seen on macroscopic heart period traces and accurately detected by the
burst-detection algorithm. During sleeping hours, the interburst
sequences were characterized by elevated heart period values and the
marked respiratory sinus arrhythmia of supine subjects. Their
prevalence throughout the recordings is consistent with
their being due to exercise initiation (including rolling over in bed),
partially satisfying the fourth criterion of teleological
plausibility.
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Discussion |
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We propose a novel, lexical approach to the study of heart period variability, propose formal criteria for judging the validity of candidate lexical events, and describe the first such lexon. This approach has potential applications in both basic and clinical studies.
The Burst Lexon
The candidate lexon that we chose was a brief, reversible
tachycardia previously reported to occur during standing up
and during initiation of cycling.11 12 13 It was chosen
because of its large size, ease of induction, and apparent
stereotypical shape. It is inducible and detectable in most or all
subjects; the detected bursts resemble the induced bursts; and the
induced and detected bursts share plausibly related
provocative factors. Thus, a burst is a discrete,
imperfectly reproducible heart period event that is related to known
physiological behavior and is easily detectable on
ambulatory heart period recordings.
Applications of Lexical Approach
We suggest that heart period sequences be modeled as concatenated
assemblages of characteristically scaled and characteristically shaped
events, or lexons. The lexical approach has several strengths and
applications. It does not assume the existence of any global
properties; rather, it focuses on temporally localized structures
within the recordings. It overcomes the problem of stationarity
that confounds frequency-domain analyses and is based on the
premise that there is useful information in the very structures that
might cause lack of stationarity. Similarly, it does not require
long-term statistical constancy in the sequence.
Second, it allows investigators to form reasonable deductions and hypotheses about the physiology that accompanies heart period changes recorded on ambulatory ECGs. For example, the first 3 to 5 seconds of burst tachycardia may be due to activation of skeletal mechanoreceptors, with the afferent impulses carried by type III and IV nerve fibers.12 17 18 19 Activation of both large and small muscles can cause this phase. The second phase may be a compensatory tachycardia due to unloading of arterial baroreceptors by hypotension.12 Thus, the observation of a burst on a heart period recording suggests the reasonable inference of vagal withdrawal, hypotension, and subsequent increased sympathetic activity mediated by baroreceptors.
Third, the lexical approach facilitates the investigation of temporal physiological transients that might precede clinical events such as arrhythmias and syncope. The identification of specific lexons preceding arrhythmias, coupled with an understanding of their physiology, may prove particular fruitful.
Sources of Variance in Bursts
Although bursts are generally similar, there were differences
noted between subjects and between induced and detected bursts. Most of
the larger differences were in magnitude measures. There is no
published physiological explanation for burst
magnitudes, but it is noteworthy that the magnitude variables
correlated well with the baseline heart period (Figure 3). We
speculate that some of those factors that affect resting heart period
also affect the magnitude of the tachycardic response induced by
rollover initiation. The burst-detection algorithm was sufficiently
robust to detect bursts with both high sensitivity and high specificity
in all subjects despite these differences in magnitudes.
The bursts detected on ambulatory recordings were slightly
longer than those induced by rolling over. Figure 5 shows that
the detected burst distribution has a pronounced tail of lengthy
tburst times. Given that bursts can also be
induced by sitting up, standing up, starting to walk, and starting to
cycle11 12 13 (and D. Roach, MD, unpublished data,
1998), it may be that these long tburst
values represent the initiation of exercise and that the heart
period values do not fully recover until the exercise is completed. We
speculate that the burst may be a universal response to the initiation
of activity from relative quiescence.
Limitations
It might be that the induced and detected bursts are fortuitous
coincidences. We used 2 methods to address this problem. First, we
constructed phase-scrambled control sequences that contained the
statistical and spectral characteristics of the original sequences but
lacked deterministic structures. There were significantly fewer bursts
in the control sequences. Because phase scrambling preserves some of
the properties of the shapes, it is not surprising that some residual
subsequences were detected. We also demonstrated the morphological
similarity of the induced and detected bursts. Although some of these
comparisons might be biased because the same parameters
were used in the detection algorithm as in the comparison, many were
not. Specifically, HPmag and expfit were
1-sided detection criteria, and tburst,
t50%mag, and K were not used as
detection criteria but as comparative criteria.
We only examined the response to rolling over. However, bursts occur at the initiation of standing up and cycling11 12 13 and with sitting up and initiation of walking (R. Sheldon, MD, et al, unpublished data, 1998). We have not proven that the bursts detected on Holter recordings are due to exercise initiation or rolling over in bed, and it remains possible that other activities and physiological perturbations cause bursts.
Finally, we have not examined the effects of aging,20 disease,13 20 or drugs11 on burst morphology or prevalence. Indeed, burst magnitude may decline with advancing age.21
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Acknowledgments |
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This study was supported by the Medical Research Council of Canada (PG11188), Ottawa, Canada, and the Calgary General Hospital, Calgary, Alberta, Canada (to Dr Sheldon).
Received November 7, 1998; revision received March 23, 1999; accepted April 9, 1999.
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References |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Akselrod SD, Gordon FA, Ubel DC, Shannon A, Barger C, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science. 1981;213:220–222.
2.
Malliani A, Pagani M, Lombardi F, Cerutti S.
Cardiovascular neural regulation explored in the
frequency domain. Circulation. 1991;84:482–492.
3.
Pagani M, Lombardi F, Suzzetti S, Rimoldi O, Furlan R,
Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E, Turiel
M, Baselli G, Cerutti S, Malliani A. Power spectral analysis of
heart rate for arterial pressure variabilities as a marker
of sympatho-vagal interaction in man and conscious dog. Circ
Res. 1986;59:178–193.
4.
Saul JP, Berger RD, Albrecht P, Stein SP, Chen MH,
Cohen RJ. Transfer function analysis of the circulation: unique
insights into cardiovascular regulation. Am
J Physiol. 1991;261:H1231–H1245.
5. Kanters JK, Holstein-Rathlou N-H, Agner E. Lack of evidence for low-dimensional chaos in heart rate variability. J Cardiovasc Electrophysiol. 1994;5:591–601.[Medline] [Order article via Infotrieve]
6. Kanters JK, Hojgaard MV, Agner E, Holstein-Rathlou N. Short- and long-term variations in nonlinear dynamics in heart rate variability. Cardiovasc Res. 1996;31:400–409.[Medline] [Order article via Infotrieve]
7.
Roach DE, Sheldon RS. Information scaling properties
of heart rate variability. Am J Physiol. 1998;274:H1970–H1971.
8. Lepicovska V, Novak P, Nadeau R. Time-frequency dynamics in neurally mediated syncope. Clin Auton Res. 1992;2:317–326.[Medline] [Order article via Infotrieve]
9.
Roach D, Sheldon A, Wilson W, Sheldon R. Temporally
localized contributions to measures of large-scale heart rate
variability. Am J Physiol. 1998;274:H1465–H1471.
10. Le Pape G, Giacomini H, Swynghedauw B, Mansier P. A statistical analysis of sequences of cardiac interbeat intervals does not support the chaos hypothesis. J Theor Biol. 1997;184:123–131.[Medline] [Order article via Infotrieve]
11.
Ewing DJ, Hume L, Campbell IW, Murray A, Neilson JMM,
Clarke BF. Autonomic mechanisms in the initial heart rate response to
standing. J Appl Physiol. 1980;49:809–814.
12. Borst C, Wieling W, Brederode JFMV, Hond A, Rijk LGD, Dunning AJ. Mechanisms of initial heart rate response to postural change. Am J Physiol. 1982;243:H678–H681.
13.
Wieling W, Harms MPM, ten Harkel ADJ, van Lieshout JJ,
Sprangers RLH. Circulatory response evoked by a 3 s bout of
dynamic leg exercise in humans. J Physiol. 1996;494:601–611.
14. Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8:283–298.[Medline] [Order article via Infotrieve]
15. Roach DE, Thakore E, Sheldon RS. Large magnitude, transient, bradycardic events in rabbits. Am J Physiol. In press.
16. Roach DE, Thakore E, Sheldon RS. Autonomic control of rabbit faintlets. Pacing Clin Electrophysiol. 1998;21:927. Abstract.
17. Shepherd JT, Blomqvist CG, Lind AR, Mitchell JH, Saltin B. Static (isometric) exercise: retrospection and introspection. Circ Res. 1981;48(suppl I):179–188.
18.
Hollander AP, Bouman LN. Cardiac acceleration in man
elicited by a muscle-heart reflex. J Appl Physiol. 1975;38:272–278.
19.
Borst C, Hollander AP, Bouman LN. Cardiac acceleration
elicited by voluntary muscle contractions of minimal duration.
J Appl Physiol. 1972;32:70–77.
20. Smith S. Reduced sinus arrhythmia in diabetic autonomic neuropathy: diagnostic value of an age-related normal value. BMJ. 1982;285:1599–1601.
21.
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.
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