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(Circulation. 1999;100:2079-2084.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation
From the Division of Cardiology, Department of Medicine (S.V., S.Y.-M.), the Tampere School of Public Health (A.M.K.), and the Division of Physiology (P.R.), University of Tampere; and the Division of Cardiology, Department of Medicine, University of Oulu (T.H.M., S.P., J.A., H.V.H.), Finland.
Correspondence to Saila Vikman, MD, Division of Cardiology, Department of Medicine, University of Tampere, PL 2000, 33521 Tampere, Finland. E-mail saila.vikman{at}koti.tpo.fi
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Abstract |
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 Top Abstract IntroductionMethodsResultsDiscussionReferences |
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Background—Trigger mechanisms for the onset of paroxysmal atrial fibrillation (AF) in patients without structural heart disease are not well established. New analysis methods of heart rate (HR) variability based on nonlinear system theory may reveal features and abnormalities in R-R interval behavior that are not detectable by traditional analysis methods. The purpose of this study was to reveal possible alterations in the dynamics of R-R intervals before the spontaneous onset of paroxysmal AF.
Methods and Results—Traditional time and frequency domain HR
variability indices, along with the short-term scaling exponent
1 and approximate entropy (ApEn), were analyzed
in 20-minute intervals before 92 episodes of spontaneous, paroxysmal AF
in 22 patients without structural heart disease. Traditional HR
variability measures showed no significant changes before the onset of
AF. A progressive decrease occurred both in ApEn (1.09±0.26 120 to 100
minutes before AF; 0.88±0.24 20 to 0 minutes before AF;
P<0.001) and in 1 (1.01±0.28 120 to 100
minutes before AF, 0.89±0.28 20 to 0 minutes before AF;
P<0.05) before the AF episodes. Both ApEn (0.89±0.27
versus 1.02±0.30; P<0.05) and 1
(0.91±0.28 versus 1.27±0.21; P<0.001) were also lower
before the onset of AF compared with values obtained from matched
healthy control subjects.
Conclusions—A decrease in the complexity of R-R intervals and altered fractal properties in short-term R-R interval dynamics precede the spontaneous onset of AF in patients with no structural heart disease. Further studies are needed to determine the physiological correlates of these new, nonlinear HR variability measures.
Key Words: tachyarrhythmias • heart rate • nervous system, autonomic
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Analysis of heart rate (HR) variability has become an important, noninvasive tool for assessing cardiac autonomic regulation.1 2 Conventionally, nonspectral and spectral analyses of HR variability have been used to measure R-R interval variability. A number of new methods based on nonlinear system theory have recently been developed to give more insight into complex HR dynamics.3 4 5 These methods may reveal subtle abnormalities in cardiac autonomic regulation that may not be uncovered by traditional measures of HR variability. These new methods have already provided clinically important information on abnormalities in HR behavior in various clinical settings.6 7 8 9 10
The autonomic nervous system may play an important role as a trigger for the spontaneous onset of paroxysmal atrial fibrillation (AF).11 12 However, only a few and partly controversial reports exist concerning changes in HR variability preceding the onset of AF.13 14 15 16 A recent study suggested that altered complexity of R-R interval dynamics precedes the AF episodes of patients after coronary artery bypass graft surgery.10 The trigger mechanisms and pathophysiology of AF may be different between patients with and without organic heart disease and in various clinical situations. The present study was designed to test the hypothesis that altered R-R interval dynamics, as analyzed by new complexity and fractal measures, may also precede the onset of paroxysmal, idiopathic AF.
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Patients
The 24-hour ECG recordings of patients who had paroxysmal episodes of AF, who had no structural heart disease, and who were studied at Tampere and Oulu University hospitals in Finland were prospectively collected from 1991 to 1998 from patients who underwent clinically indicated ECG recordings. Only recordings containing
1 paroxysmal AF episode(s) lasting >10 seconds, with at
least 20 minutes of sinus rhythm preceding the AF, were included in the
analyses. Patients with structural heart disease, hypertension,
diabetes, sick sinus syndrome, or atrioventricular
accessory pathways were excluded from the study. Patients >60 years of
age who had sinus pauses >2.5 seconds were also excluded.
The study population consisted of 22 patients, for whom 26 ECG
recordings (24-hour) containing 92 episodes of paroxysmal AF
were made. The clinical characteristics of the patients are shown in
Table 1
. An age- and sex-matched
healthy control group of subjects who had no evidence of organic heart
disease and no history of AF was selected from among individuals who
were participating in a larger trial comparing the characteristics of
hypertensive and normotensive subjects; the latter group was randomly
selected from the general population of Oulu using their social
security numbers. All control patients had undergone a complete
physical examination and had a medical history that revealed no
cardiovascular disease or medication. They all had
normal blood pressure. All underwent 12-lead ECG; M-mode, 2D, and
Doppler echocardiography; and a 2-hour glucose
tolerance test. None had evidence of ischemic ST-segment
depression in exercise ECG. The test protocol was approved by the
Ethics Committee of the University of Oulu.
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Electrocardiographic Recordings
All 2-channel 24-hour recordings were analyzed
both with the Medilog Excel (version 4.1c, Oxford Medical Ltd) ECG
software system and manually to detect and quantify arrhythmias
and artifacts. The data were sampled digitally and transferred to a
microcomputer for the analysis of HR variability.
Analysis of HR Variability
After the ECG data were transferred to the microcomputer, the
R-R interval series was edited automatically; after this, manual
editing was also performed to delete all premature beats and noise. All
questionable portions were compared with 2-channel Holter ECGs. Only
segments with >80% qualified sinus beats were included. Details of
this analysis and filtering method have been described
previously.17 18 Analysis of HR variability was
performed on 6 sequential, 20-minute intervals starting 120 minutes
before the onset of AF. From 1:1 matched control subjects, the HR
variability measures were analyzed in one 20-minute interval at
the same time of day as the AF patients’ last 20-minute period before
AF onset. All analyses of R-R interval variability were
performed with a custom-made analysis program (Hearts, Heart
Signal Co), and the details of the methods have been described
elsewhere.9 17 18 19
Twenty-minute R-R interval data were then divided into 2 segments of equal size according to their beat count; a linear detrend was applied to those segments of 400 to 1000 samples to make the data more stationary. An R-R interval spectrum was computed over the 20-minute periods according to a previously described method.20 A fast Fourier transform method was used to estimate the power-spectrum densities of HR variability. The power spectra were quantified by measuring the area in 2 frequency bands: 0.04 to 0.15 Hz (low frequency [LF]) and 0.15 to 0.40 Hz (high frequency [HF]). The ratio between LF and HF spectra was also calculated. The SD of the normal R-R intervals and the mean length of the R-R intervals in 20-minute segments were used as time-domain measures of HR variability.
Nonlinear Analysis of R-R Data
The same pre-edited R-R interval time series that was used for
the spectral and time domain analyses of HR variability were
also used for calculating approximate entropy (ApEn) and for detrended
fluctuation analysis. ApEn measures the logarithmic likelihood
that runs of patterns that are close to each other will remain close in
the subsequent incremental comparisons. A time series containing many
repetitive patterns has a relatively small ApEn; conversely, more
random data produce higher values. Details of this method have been
described previously.3 4 9 Two input values, m and r, must
be fixed to compute ApEn; m=2 and r=20% of the SD of the data sets
were chosen on the basis of previous findings of good statistical
validity.3 4 A detrended fluctuation analysis
technique was used to quantify the fractal correlation properties of
the R-R interval data. This method is a modified root mean square
analysis of a random walk. In the present study, we used
the scaling exponent 1, which measures the
strength of the short time (
11 beats) correlation properties of R-R
interval data. The details of this method have been described
previously.7 21 22 23 Analyses of ApEn and
1 were also carried out from data in which
only noise was abolished and ectopic beats were not excluded. In the
final analysis, both edited and unedited data were used.
Effects of Premature Beats
The amount of ectopic beats by percentage in each 20-minute
segment was also analyzed. Because of the potential effect of
premature beats on ApEn and scaling exponents, the effect of the
premature beats on ApEn and 1 was assessed by
various experiments with real and artificial R-R signals. Short and
long time intervals resembling premature beats with a compensatory
pause were added, and the amount of replaced beats was increased
progressively. First, premature beats with a constant coupling interval
(500 ms) were added. The amount of replaced beats was increased
progressively from 0% to 40%. Then, the same procedure was repeated,
but the time length of coupling intervals was changed randomly within
certain limits (350 to 800 ms). The tests were performed on real R-R
interval data from a healthy subject with a mean HR of 
60
min, and an SD of RR intervals of 130 ms; they were also
performed on artificial signals with 1/f signal properties, a mean R-R
interval length of 1000 ms, and a SD of 160 ms.
Statistical Methods
Normal gaussian distribution of the data were verified by the
Kolmogorov-Smirnov goodness-of-fit test. Whenever the data were not
normally distributed (z>1.0), a logarithmic transformation
was performed for all spectral components of HR variability before the
statistical analysis. To evaluate whether a significant change
occurred in different HR variability measures or in the amount of
ectopic beats before the onset of AF, linear mixed
models24 were used. With these models, it is possible
to analyze unbalanced repeated-measure designs that use
different types of mean and covariance structures. Linear mixed
models were fitted using PROC MIXED in the SAS System for Windows
(version 6.12). Student’s t test was used to
analyze differences between the healthy subjects and patients
with AF. P<0.05 was considered significant.
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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The clinical characteristics of the study population are presented in Table 1
. The mean duration of the 92 AF
episodes was 22 min, 41 s (median, 38 s; range, 10 s to
6 h, 2 min, 16 s).
Changes in HR Variability Measures and the Amount of Ectopic Beats
Before the Onset of AF
Table 2 presents HR variability
measures and the amount of ectopic beats in different time periods.
None of the traditional time or frequency domain measures showed
significant changes before the onset of AF. The amount of ectopic beats
increased during the last 40-minute period before the start of AF. ApEn
analyzed from fully edited data decreased before the onset of
AF (Table 2). When ApEn was analyzed from the real R-R
interval data without excluding the ectopic beats, an even more
prominent reduction was observed in the complexity of R-R interval
dynamics before the onset of AF (Figure).
1 also decreased progressively before the
onset of AF when analyzed from data including the premature
beats. When all ectopic beats were abolished,
1 showed no significant change before the
onset of AF (Table 2).
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|
When the same analyses were performed in a subgroup of patients
(n=11) who had no medication during a Holter recording, a
similar decrease before the onset of AF (n=33) was seen in ApEn values,
both from fully edited data and from unedited data (Table 3). Also, 1 from
unedited data showed a decreasing trend (Table 3). None of the
time or frequency domain measures showed any significant change before
AF in patients without medication.
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Comparison of Nonlinear Measures of HR Dynamics Between
Nonmedicated Patients With AF and Healthy Controls
Both ApEn and 1 were significantly lower
in the AF patients than in healthy controls when analyzed from
the identical 20-minute segments from the unedited R-R intervals. The
short-term scaling exponent was also lower in AF patients when
analyzed from pure sinus beats (Table 4).
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The Effect of Added Ectopic Beats on ApEn and Fractal Scaling
Exponent in Real and Artificial Data
ApEn decreased progressively when the amount of premature beats
with a constant coupling interval increased. A paradoxical increase was
observed when a very small amount of ectopic beats was added. The
opposite effect and increasing values of ApEn were observed when the
coupling interval time varied randomly between 350 and 800 ms.
1 clearly decreased when ectopic beats, either
with fixed or variable coupling intervals, were added to the data
(Table 5). The results were almost
identical with real and artificial data, but the amount of ectopic
beats required to cause a reduction in ApEn was higher with artificial
data.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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The main finding of this study is that altered HR dynamics precede the spontaneous onset of paroxysmal AF episodes in subjects who have no evidence of structural heart disease. These abnormalities in HR dynamics were not detectable by traditional time and frequency domain methods, but they were uncovered by a method describing the complexity and predictability of HR behavior.
Complexity and Correlation Properties of HR Dynamics Before
AF
Analysis methods derived from nonlinear dynamics have
opened a new approach for studying and understanding the
characteristics of HR behavior.3 22 25 These
analysis methods differ from the traditional measures of HR
variability because they are not designed to assess the magnitude of
variability. Notably, only a weak correlation exists between the new
nonlinear measures and traditional measures of HR
variability,9 26 showing that these new indices describe
features of HR behavior that are not detectable by conventional
methods. Methods analyzing the complexity (ApEn) and fractal-like
correlation properties of HR behavior have been most commonly used to
detect abnormalities in R-R interval dynamics in various
cardiovascular disorders.6 7 8 9 10
A stepwise, linear reduction in ApEn values analyzed from
20-minute time periods was the most uniform and consistent
finding before the onset of AF episodes. ApEn is a measure that
quantifies the regularity and predictability of time series data.
Reduced ApEn indicates larger predictability in HR behavior and
increased repeatability of the patterns of R-R intervals. ApEn values
1.0 have been previously described in healthy human heartbeat
dynamics. Reduced complexity in HR dynamics has been previously found
in various cardiovascular
disorders,19 27 28 during bed rest,29 and in
normal aging.26 Concurrent with the present
observations, a recent study also reported on reduced ApEn preceding
spontaneous AF episodes in patients after coronary artery
bypass surgery.10 These results suggest that a reduced
complexity of R-R interval dynamics is a common finding preceding the
onset of AF episodes, independent of the clinical condition and cause
of an underlying structural heart disease.
Reduced ApEn was observed here before the AF episodes also when only pure sinus beats were included in the analysis. Ectopic beats with a fixed coupling interval resulted in further reduction in ApEn values, both in tests with artificial signals and in real R-R interval data, resulting in a marked reduction of ApEn before the onset of AF. However, in a large proportion of cases, AF was not preceded by an increase in the frequency of atrial ectopic beats, showing that ectopy itself may not serve as the only trigger of paroxysmal AF.
The short-term scaling exponent 1 also showed
a tendency toward lower values before the onset of AF.
1 quantifies the correlation properties of
short-term HR dynamics. Consistent with previous findings,
1 values were significantly lower when ectopic
beats were left in the data when compared with values from fully edited
data,8 but no change was observed in
1 values in pure sinus interval data. A
similar reduction in short-term correlation properties has been
reported to precede ventricular fibrillation in
postinfarction patients,8 but the abnormalities in the
scaling exponent were more prominent before the onset of
ventricular fibrillation than preceding AF.
Traditional Measures of HR Variability Before AF
Increased sympathetic activity is characterized by a shift of the
LF-HF balance in favor of the LF component; the opposite shift in favor
of the HF component occurs during vagal tone.30 Both LF
and HF components showed a tendency toward reduced values before AF,
but the LF/HF ratio remained unchanged. Consistent with the
present findings, in a previous study, only a minority of AF
episodes could have been categorized as being induced either by vagal
or sympathetic influence.15
Nonstationarity of the data and the replacement of ectopic beats by artificial R-R intervals are the major problems in the spectral analysis of HR variability during uncontrolled conditions. These analysis techniques may not be able to detect the subtle abnormalities in cardiovascular autonomic regulation that occur in ambulatory conditions. Therefore, the lack of change in the spectral components of HR variability that precede the onset of AF episodes may not exclude the significance of the autonomic nervous system as an important trigger of the onset of AF.
Potential Pathophysiologic Background for the Spontaneous Onset
of AF
The normal complexity (ApEn of 1.0) and fractal characteristics
of R-R interval dynamics have been suggested to be markers of healthy
cardiovascular regulation. Any deviation from the
normal R-R interval dynamics may predispose patients to untoward
cardiac events. From this dynamic point of view, the reduced R-R
interval complexity might thus by itself serve as a trigger of the
onset of AF. Although the ApEn and 1 values
were lower before the onset of AF than those from healthy controls,
significant overlapping occurred in the individual values. Therefore,
altered R-R interval behavior is more likely a marker of a change in
cardiovascular autonomic regulation that preconditions
the onset of AF in subjects with abnormal
electrophysiological properties of the
atria rather than being causally related to the onset of AF.
In experiments with artificial and real R-R interval signals, the fixed coupling of premature beats resulted in a reduction of ApEn values, but variable coupling resulted in an increase of ApEn. Thus, a reduction of ApEn before the onset of AF in patients with an increase in premature atrial beats resulted mainly from atrial ectopy with fixed coupling intervals, suggesting an increase of firing from a single atrial focus. Consistent with recent observations of the importance of ectopic firing as a trigger of paroxysmal AF in subjects without evidence of other structural cardiac abnormalities,31 the majority of AF episodes here started with single or repetitive ectopic beats. Thus, the reduced complexity of R-R interval dynamics before the spontaneous onset of AF seems to be a marker of both altered regulation of sinus node behavior and an increase of atrial firing from a single ectopic focus, which together predispose the spontaneous onset of AF.
The physiological determinants of ApEn and
1 have not been well defined. The dynamics of
R-R interval variability are the result of a complex interaction
between autonomic tone, sensory input, central influence, vasomotor
regulation, and target organ responsiveness. Further studies will be
needed to establish the mechanisms behind the altered complexity and
the fractal characteristics of HR behavior to better understand the
physiological correlates of the spontaneous onset
of AF.
Limitations of the Study
Many patients were on medication during the Holter
recordings, which may have influenced the measures of HR
variability. However, the results remained the same in a subgroup of
patients who were not on medication, which suggests that the cardiac
medication itself had no major effect on these observations. A
relatively small sample of patients was included in this study, and the
results should be confirmed in larger patient groups before
generalizing these observations. Finally, new nonlinear HR variability
measures are rather complex, and their
physiological determinants are not well defined.
Therefore, further research on the physiological
background of these new HR variability indices and on their clinical
usefulness in various settings will be needed.
Conclusions
The number of ectopic beats increases before the onset of
paroxysmal episodes of AF in patients with no structural heart disease.
Moreover, an alteration of short-term, fractal-like, correlation
properties and a reduced complexity of R-R interval data precede the
onset of AF episodes. None of the traditional time and frequency domain
measures showed any significant changes before AF episodes. These
observations confirm the hypothesis that the normal complexity and
fractal properties of HR behavior are important for the
maintenance of healthy cardiovascular dynamics
and that analyzing HR behavior by new methods can provide clinically
important information on abnormal cardiovascular
regulation that cannot be uncovered by traditional analyses of
HR variability.
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Acknowledgments |
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This work was supported by the Medical Research Fund of Tampere University, by the Finnish Foundation for Cardiovascular Research, and by the Medical Council of the Finnish Academy of Science (H.V.H.).
Received April 7, 1999; revision received June 24, 1999; accepted July 15, 1999.
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V. Tuzcu, S. Nas, T. Borklu, and A. Ugur Decrease in the heart rate complexity prior to the onset of atrial fibrillation Europace, June 1, 2006; 8(6): 398 - 402. [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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 Z.-K. Wu, S. Vikman, J. Laurikka, E. Pehkonen, T. Iivainen, H. V. Huikuri, and M. R. Tarkka Nonlinear heart rate variability in CABG patients and the preconditioning effect Eur. J. Cardiothorac. Surg., July 1, 2005; 28(1): 109 - 113. [Abstract] [Full Text] [PDF]
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 S. Vikman, K. Lindgren, T. H. Makikallio, S. Yli-Mayry, K.E. J. Airaksinen, and H. V. Huikuri Heart rate turbulence after atrial premature beats before spontaneous onset of atrial fibrillation J. Am. Coll. Cardiol., January 18, 2005; 45(2): 278 - 284. [Abstract] [Full Text] [PDF]
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 H Ansakorpi, J T Korpelainen, H V Huikuri, U Tolonen, V V Myllyla, and J I T Isojarvi Heart rate dynamics in refractory and well controlled temporal lobe epilepsy J. Neurol. Neurosurg. Psychiatry, January 1, 2002; 72(1): 26 - 30. [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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A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley PhysioBank, PhysioToolkit, and PhysioNet : Components of a New Research Resource for Complex Physiologic Signals Circulation, June 13, 2000; 101 (23): e215 - e220. [Abstract] [Full Text] [PDF]
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 V. Shusterman, B. Aysin, K. P. Anderson, and A. Beigel Multidimensional Rhythm Disturbances as a Precursor of Sustained Ventricular Tachyarrhythmias Circ. Res., April 13, 2001; 88(7): 705 - 712. [Abstract] [Full Text] [PDF]
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