(Circulation. 1996;93:1836-1844.)
© 1996 American Heart Association, Inc.
Articles |
Abnormalities in Beat-to-Beat Dynamics of Heart Rate Before the Spontaneous Onset of Life-Threatening Ventricular Tachyarrhythmias in Patients With Prior Myocardial Infarction
From the Division of Cardiology, Department of Medicine, University of Oulu (Finland) (H.V.H., T.S., M.J.K., K.E.J.A., M.J.I.), and the Division of Cardiology, Miami (Fla) University Medical Center (A.C., R.J.M.).
Correspondence to Heikki V. Huikuri, MD, Division of Cardiology, Department of Medicine, University of Oulu, Kajaanintie 50, FIN-90220 Oulu, Finland.
 |
Abstract |
|---|
 Top Abstract IntroductionMethodsResultsDiscussionReferences |
|---|
Background Beat-to-beat analysis of RR intervals can reveal patterns of heart-rate dynamics, which are not easily detected by summary measures of heart-rate variability. This study was designed to test the hypothesis that alterations in RR-interval dynamics occur before the spontaneous onset of ventricular tachyarrhythmias (VT).
Methods and Results Ambulatory ECG recordings from 15 patients with prior myocardial infarction (MI) who had spontaneous episodes of sustained VT during the recording and VT inducible by programmed electrical stimulation (VT group) were analyzed by plotting each RR interval of a sinus beat as a function of the previous one (Poincaré plot). Poincaré plots were also generated for 30 post-MI patients who had no history of spontaneous VT events and no inducible VT (MI control subjects) and for 30 age-matched subjects without heart disease (normal control subjects). The MI control subjects and VT group were matched with respect to age and severity of underlying heart disease. All the healthy subjects and MI control subjects showed fan-shaped Poincaré plots characterized by an increased next-interval difference for long RR intervals relative to short ones. All the VT patients had abnormal plots: 9 with a complex pattern, 3 ball-shaped, and 3 torpedo-shaped. Quantitative analysis of the Poincaré plots showed the SD of the long-term RR-interval variability (SD2) to be smaller in all VT patients (52±14 ms; range, 31 to 75 ms) than in MI control subjects (110±24 ms; range, 78 to 179 ms, P<.001) or the normal control subjects (123±38 ms, P<.001), but the SD of the instantaneous beat-to-beat variability (SD1) did not differ between the groups. The complex plots were caused by periods of alternating sinus intervals, resulting in an increased SD1/SD2 ratio in the VT group. This ratio increased during the 1-hour period preceding the onset of 27 spontaneous VT episodes (0.43±0.20) compared with the 24-hour average ratio (0.33±0.19) (P<.01).
Conclusions Reduced long-term RR-interval variability, associated with episodes of beat-to-beat sinus alternans, is a highly specific sign of a propensity for spontaneous onset of VT, suggesting that abnormal beat-to-beat heart-rate dynamics may reflect a transient electrical instability favoring the onset of VT in patients conditioned by structurally abnormal hearts.
Key Words: dynamics • heart rate • tachyarrhythmias
|
Introduction |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Reduced HRV, as measured by linear power spectrum and time-domain techniques, indicates an increased risk of future arrhythmic events after acute MI.1 2 Spectral analyses of HRV have shown that reduced very-low-frequency and low-frequency oscillations in heart rate are most closely associated with susceptibility to spontaneous ventricular arrhythmias in patients with ischemic heart disease.3 4 However, attempts to identify a causal relationship between abnormalities in HRV and the onset of life-threatening arrhythmic events have not shown consistent temporal changes in linear measures of HRV preceding the onset of VT in post-MI patients.3 5
The assessment of HRV by means of linear summary measures is partly invalidated by the requirement of stationarity of the RR-interval data without ectopic beats and by a lack of knowledge regarding temporal changes in beat-to-beat heart-rate dynamics. Nonlinear methods have therefore been used recently to quantify the apparently random or chaotic dynamics of heart-rate fluctuations under normal physiological conditions.6 7 8 9 The Poincaré plot method provides a beat-to-beat visual and quantitative analysis of RR intervals that can reveal patterns of heart-rate dynamics resulting from nonlinear processes that are not easily detected by linear summary methods.10 11 12 13 The purpose of the present study was to test the hypothesis that alterations in nonlinear beat-to-beat heart-rate dynamics occur before the spontaneous onset of life-threatening arrhythmias. RR intervals were plotted as a function of previous values in post-MI patients with and without spontaneous VT episodes during ECG recordings and in age-matched healthy subjects.
|
Methods |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Patients
The patients in the VT group were selected from among a population with prior Q-wave MI (>1 month from MI) who were admitted to the University of Miami Medical Center or Oulu University Central Hospital because of cardiac arrest or sustained VT. Fifteen consecutive patients who had experienced one or more episodes of sustained VT during the 48-hour Holter recording while in the hospital and who were inducible into sustained VT were included in the VT group. Patients with diabetes mellitus, atrial fibrillation, recent MI, congestive heart failure, or ischemic ST-segment depression preceding the onset of VT episodes were excluded from the consecutive series. Antiarrhythmic medication had been withdrawn at least 4 half-lives before the Holter recordings.
One control group consisted of 30 patients with a prior Q-wave MI (>1
month) but without any history of VT events (MI control subjects).
These patients were selected from among a consecutive series of
patients referred for angiography because of angina pectoris or for
prognostic reasons and in whom programmed electrical stimulation was
performed as a part of the study protocol. Patients with spontaneous
episodes of nonsustained or sustained VT during Holter
recordings or inducible VT (>5 consecutive beats), atrial
fibrillation, or diabetes mellitus were excluded. The VT group and MI
control group were matched with respect to age, sex, number of prior
MIs, left ventricular ejection fraction, number of diseased
coronary arteries, and ß-blocking medication. The matched
parameters were scaled as follows: (1) age between 40 and
50 years, 50 and 60 years, and 60 and 70 years; (2) male or female sex;
(3) location and number of prior MIs; (4) left ventricular
ejection fraction <30%, between 30% and 40%, between 40% and 50%,
or >50%; (5) single-vessel or multivessel coronary artery
disease; and (6) treatment with ß-blocking medication. Two
patients in the MI control group were matched for all six variables
with each patient in the VT group. Matching was performed before the
analysis of HRV from the ECG recordings. The clinical
data on the VT group and the MI control group are summarized in Table 1
.
|
Thirty additional age- and sex-matched subjects (mean age, 61±8 years; 6 women and 24 men) were selected from among healthy normotensive individuals (normal control subjects) who were randomly selected by social security number from the general population of Oulu who were participating in a cross-sectional population study comparing the HRV and other characteristics of normotensive and hypertensive subjects. They all underwent a complete physical examination and had a medical history that revealed no cardiovascular disease or medication. They also had normal blood pressure, 12-lead ECG, and M-mode, two-dimensional, and Doppler echocardiography, and none had evidence of ischemic ST-segment depression in an exercise ECG. All the healthy subjects and patients gave their informed consent, and the tests were approved by the appropriate ethical committees.
Procedures
Two-channel 24- or 48-hour Holter recordings were
recorded from all the subjects and analyzed with a Medilog
Excel (version 4.1 c, Oxford Medical Ltd) ECG software system. The
ambulatory recordings of the patients in the VT group and MI
control group were performed in the hospital 1 to 4 days before the
invasive studies, and the normal control subjects underwent the ECG
recordings outside the hospital. All the study subjects were
asked to maintain their normal activities and their normal
asleep-awake rhythm during the recordings.
Left-heart catheterization, including left ventricular cineangiography and coronary angiography, was performed on the patients in the VT group and the MI control group. Left ventricular cineangiograms were taken in a 45° right anterior oblique position, and the ejection fraction was calculated by a single-plane area-length method. Coronary artery stenoses with >50% luminal narrowing were considered significant.
The electrophysiological testing included incremental ventricular pacing and programmed ventricular stimulation using up to three extrastimuli at two basic drive cycle lengths (600 and 400 ms) from the right ventricular apex and outflow tract. VT was defined as sustained when its duration was >30 seconds or if cardioversion/defibrillation was required for its termination and as nonsustained if it lasted more than five beats but <30 seconds. The stimulation protocol and definition of induced VT have been described in detail previously.14
An exercise ECG was performed on the healthy subjects in the form of a symptom-limited bicycle exercise test, as described previously.15 ST-segment depression >0.1 mV at 0.08 second after the J-point was defined as an ischemic change.
A Hewlett-Packard 77020A ultrasound color Doppler system was used for M-mode, two-dimensional, and Doppler echocardiographic recordings by standard techniques; these were analyzed by a method described previously.16
Frequency-Domain and Time-Domain Analysis of
HRV
The ECG data were sampled digitally and transferred from the
Oxford Medilog scanner to a microcomputer for analysis of HRV
by a method described in detail previously.17 18 A linear
detrend was applied to the RR-interval data segments of 512 samples in
the spectral analysis of HRV to make the data more stationary.
The RR-interval series was passed through a filter that eliminates
premature beats and noise and either deletes the gaps or fills them
with an average value computed from the neighborhood. An RR interval
was interpreted as premature if it deviated from the previous qualified
interval by more than a given tolerance level, which defaults to 30%.
However, the tolerance limit is a programmable parameter,
and it was changed according to the prematurity index of ectopic beats
obtained for each patient before HRV analysis. All RR-interval
time series were first edited automatically, after which careful manual
editing was performed by visual inspection of all RR intervals. The
details of this analysis and filtering method have been
described previously.17 18 On the basis of our previous
experiments, only segments in which >85% of the beats qualified were
included in the spectral and time-domain analysis of
HRV.
An autoregressive model was used to estimate the power spectrum densities of the HRV.19 The computer program automatically calculates the autoregressive coefficients to define the power spectrum density. The power spectra were quantified by measuring the area in three frequency bands: very-low-frequency power, from 0.005 to 0.04 Hz; low-frequency power, from 0.04 to 0.15 Hz; and high-frequency power, from 0.15 to 0.4 Hz. The SDANN was used as a time-domain measure of HRV. Average 24-hour and 1-hour HRV values were calculated from the segments of 512 RR intervals.
Poincaré Analysis of HRV
The Poincaré plot is a diagram in which each RR interval
of a tachogram is plotted as a function of the previous RR interval for
a predetermined segment length. The program used in these experiments
provides a graphic display of the plots and a quantitative
analysis of the shape of the scattergrams. The markings of the
plot are gathered around a line of unitary slope (slope=1) passing
through the origin. The center point of the markings is at
(RRaver, RRaver), where
RRaver is the average RR-interval length for the
tachogram.
The Poincaré plots were analyzed both by visual
interpretation of the shape of the plots by two observers independently
and by quantification of the shape of the scattergram. A normal plot
for the purposes of visual analysis was defined to be a
fan-shaped configuration reflecting an increased RR-interval
dispersion at slow heart rates. Complex patterns were characterized by
asymmetrical RR-interval clusters and ball-shaped plots by
symmetrical RR-interval clusters around the center of the plot. A
torpedo-shaped pattern is a narrow configuration that lacks
RR-interval dispersion at slower heart rates. Quantitative
analysis entails fitting an ellipse to the plot, with its
center coinciding with the center point of the markings (Fig 1). The line defined as axis 2 describes the slope of
the longitudinal axis, while the other axis (axis 1) defines the
transverse slope, which is perpendicular in direction to axis 2. The
length of axis 1 is defined as the SD of the plot data in that
direction which describes the instantaneous beat-to-beat
variability of the data, SD1. The length of axis 2 is defined as the SD
of the plot data in the perpendicular direction, SD2. This measure
describes the continuous, long-term variability of the data in a
given segment. In addition, the ratio SD1/SD2 was computed, and the
distance from the center to the point at which the instantaneous
beat-to-beat variability is largest was analyzed. The
parameters quantified on the Poincaré plots are shown
in Fig 1. SD1 and SD2 were calculated as absolute values and in
normalized units obtained by dividing the absolute value by the average
RR interval and multiplying by 100.
|
Scattergrams of successive RR intervals were plotted for the 24-hour period and for 1-hour segments throughout the 24-hour recording period. Poincaré plots were also generated for the 1-hour period preceding each spontaneous VT episode and compared with the average 24-hour plots computed from the 1-hour segments.
All Poincaré plots were generated from the same preedited RR-interval tachograms that were used in the spectral and time-domain analyses of HRV by deletion of the gaps caused by ectopic beats or artifacts. In experiments in which different numbers of RR intervals were deleted at random, the quantitative analysis of Poincaré plots remained stable (<5% error) if <20% of the data were deleted. Therefore, only those segments with >80% qualified beats were included in the analyses.
To verify the background to the complex or ball-shaped Poincaré plots observed in the VT patients and to exclude ectopic beats or sinus pauses as a cause of complexity, ECGs were printed out from the 5-minute period before the onset of the 27 VT episodes at a speed of 25 mm/s. Randomly selected 5-minute periods of the ECG were also printed out for all the healthy subjects and postinfarction patients without a propensity for VT. The origin of each beat in the sinus node was defined as unchanged morphology of the p wave and unchanged PQ interval.
Statistics
Normal gaussian distribution of the data was verified by the
Kolmogorov-Smirnov goodness-of-fit test. Whenever the data were
not normally distributed (z value >1.2), a logarithmic
transformation was performed (for all spectral components of HRV)
before the statistical analyses. ANOVA followed by
Bonferroni's post hoc multiple-range tests (SPSS for Windows,
Release 6.0) were used to compare the differences between the groups. A
paired t test was used to estimate the differences between
two measurements in individual cases. Pearson's correlation
coefficients were used to estimate the univariate
correlations. P<.05 was considered significant. For
analysis of sensitivity, specificity, and predictive accuracy
of measures of HRV to predict the propensity for VT, the normal range
for different measures was determined by calculation of the 95%
tolerance limits of the mean logarithmic HRV values of the healthy
subjects.
|
Results |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Clinical, Angiographic, and Arrhythmic Data
The clinical and angiographic data are summarized in Table 1
.
There were no differences between the postinfarction groups with and
without susceptibility to VT in terms of cardiac medication, functional
class, or other clinical or angiographic characteristics. The patients in the VT group had a total of 27 spontaneous episodes of sustained VT during the Holter recordings (duration >30 seconds or requiring defibrillation/cardioversion for termination). Ten patients had only 1 VT episode and 5 patients more than 1 VT episode during the recording period. Four of these episodes degenerated into ventricular fibrillation, and 3 VTs were terminated by cardioversion. The other 20 VT episodes ended spontaneously. Sustained VT was inducible by programmed electrical stimulation in all the patients with spontaneous VT episodes, but none of the 30 matched MI control patients had episodes of nonsustained or sustained VT during the Holter recordings, nor could such episodes be induced.
Poincaré Plots
Examples of Poincaré plots of successive RR intervals of a
healthy subject, a post-MI patient without VT, and two post-MI patients
with VT are shown in Fig 2. All the healthy subjects and
patients without any propensity for VT typically showed fan-shaped
plots characterized by increased next-interval differences of long
RR intervals relative to short ones, whereas all the VT patients had
abnormal plots with three distinctive patterns: a complex pattern in 9
patients, ball-shaped in 3, and torpedo-shaped in 3 (see Fig 2).
|
Quantitative analysis of the Poincaré plots from the
total 24-hour period including at least 80 000 beats in each subject
showed the long-term continuous RR-interval variability (SD2) to be
significantly smaller in the VT group than in the MI control group or
normal control subjects (Table 2, Fig 3).
The highest individual SD2 value in the VT group was lower than the
lowest value in the patients without VT or in the healthy subjects (Fig 3), and SD2 remained significantly smaller in the VT patients
after normalization for average RR interval and also when
analyzed separately for daytime or sleeping hours. The
instantaneous beat-to-beat RR-interval variability (SD1) did
not differ between the groups, however, and consequently the SD1/SD2
ratio was significantly higher in the VT group than in the others. The
interval from the widest instantaneous beat-to-beat variability
to the average RR interval was also significantly shorter in the
patients with VT than in those without any VT propensity or the healthy
subjects (Table 2). The mean SD2 normalized for average RR interval was
somewhat smaller in the MI control group than in the normal control
group, but the other quantitative measures of the Poincaré plots
did not differ between these groups.
|
|
An analysis of the 1-hour period before the onset of VT
compared with the average 24-hour period measured from 1-hour segments
revealed that SD1 was higher and SD2 had a tendency to decreasing
values during the hour preceding VT (Table 3).
Consequently, the SD1/SD2 ratio increased significantly before the
spontaneous VT episodes (Table 3). Complex and ball-shaped
Poincaré plots and an increase in SD1 resulted from frequent
periods of alternating sinus intervals preceding the onset of
spontaneous VT episodes (Figs 4 and 5).
The ECG printouts before the 5-minute periods preceding the onset of VT
revealed that one or more periods of sinus alternans lasting at least 4
cycles (8 beats) occurred before all 27 spontaneous VT episodes. Sinus
alternans of small amplitude was also evident in 3 VT patients with
torpedo-shaped Poincaré plots. A total of 60 randomly
selected 5-minute periods from healthy subjects and post-MI patients
without any propensity for VT contained sinus alternans lasting
longer than 4 cycles only in 2 MI control patients and 1 healthy
subject. When the last 4 beats before the onset of VT were
analyzed separately, sinus alternans was observed before
8 VT episodes, short-long-long-short pattern was observed
in 9 patients, long-short-short-long pattern in 5,
short-short-long-long pattern in 4, and
long-long-short-short pattern in 1.
|
|
|
Linear Measures of HRV
Although all the summary measures of HRV were lower in the VT
group than in the other two groups, there was more overlapping among
the individual values for the time- and frequency-domain measures
of HRV than in the SD2 analyzed from the Poincaré plots
(Fig 2, Table 2). None of the time- or frequency-domain measures
for the 1-hour period before the onset of VT differed significantly
from the 24-hour average values (Table 3).
SD1 did not correlate with SD2 or low-frequency components of
HRV in the VT group, whereas there was a close correlation between SD1,
SD2, and the time- and frequency-domain measures of HRV in the MI
control group and normal control group (Table 4).
|
The sensitivity, specificity, and predictive accuracy of each
Poincaré and linear measure of HRV to predict the propensity for
VT are shown in Table 5. SD2 had the best positive and
negative predictive accuracy compared with other measures of HRV.
|
|
Discussion |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Abnormalities in Beat-to-Beat Dynamics of RR Intervals and Propensity for VT
The Poincaré plot analyses showed abnormal patterns of beat-to-beat RR-interval dynamics in the patients with a propensity for spontaneous onset of VT. Reduced long-term continuous RR variability was specifically related to VT susceptibility independently of the severity of the underlying structural heart disease. Concurrent with our previous observations based on a smaller group of patients,3 the SDANN and power spectrum components of HRV were also reduced in the patients with spontaneous VT episodes. In addition to reduced continuous RR-interval variability, many VT patients had complex or ball-shaped Poincaré plots resulting from episodes of alternating RR-interval dynamics. The complex patterns were not observed in any of the healthy subjects or postinfarction patients without a propensity for VT.
Present data show that continuous RR-interval variability is contained within narrow limits in patients with a propensity for VT, whereas long-term heart-rate dynamics are more variable or unpredictable in subjects having normal hearts or heart disease without a propensity for life-threatening arrhythmia. Reduced long-term RR-interval variability, as measured by linear summary measures, has also been documented in previous observational and follow-up surveys of patients with a risk of life-threatening arrhythmias.1 2 4 Linear measures were nevertheless less specific than the Poincaré technique in distinguishing between patients with and without a propensity for VT, and no temporal changes were observed in the time- or frequency-domain measures of HRV preceding the onset of VT events. Linear procedures may be less sensitive than beat-to-beat analysis techniques for assessing temporal changes in RR-interval dynamics, since heart-rate dynamics seldom meet the assumption of stationarity under normal physiological conditions, and summary measures are unable to document the fluctuations in instantaneous beat-to-beat changes of RR intervals. Skinner et al20 also reported abnormalities in correlation dimension of heartbeat intervals before imminent ventricular fibrillation without evident changes in linear measures of HRV, but no constant time interval was observed between the onset of altered RR-interval dynamics and the occurrence of ventricular fibrillation.
Complex or ball-shaped patterns in the Poincaré plots, caused by increased instantaneous RR-interval variability, became more evident during the 1-hour period before a VT event. Periods of alternating sinus intervals were also invariably observed at such times, suggesting a possible relationship between alternating beat-to-beat RR-interval dynamics and the onset of life-threatening arrhythmias. However, since the alternans pattern was not constantly observed during the last four beats before the onset of VT episodes and no consistent phase change in cycle length alternans was observed immediately preceding the onset of VT events, there might not be a direct causal relationship between phase resetting of RR-interval dynamics and triggering of the onset of VT. Rather, sinus rate alternans could reflect an alteration in neurohumoral state, creating a precondition for arrhythmogenesis. Electrical alternans has been observed previously in the ST segment and T wave before ventricular fibrillation in experimental models,21 and subtle alternans in T-wave amplitude has also been proposed as a marker of a propensity for inducible VT and occurrence of life-threatening arrhythmic events.22 23 The present data suggest that in addition to ventricular repolarization, electrical alternans expressed in cycle length patterns relates to vulnerability to the spontaneous onset of VT. The latter may be interpreted as a form of neurophysiological alternans, rather than alternans emerging from myocardial electrophysiological mechanisms. However, it is also possible that the cycle length and repolarization alternans are linked to each other, either by a direct dual neuroelectrophysiological effect or by the influence of cycle length on repolarization. In the latter case, alternans at the level of sinus or atrioventricular node may increase ventricular arrhythmogenesis by setting the stage for repolarization alternans. This concept is supported by experimental data, which have demonstrated that the action potential duration and inhomogeneity of ventricular repolarization change dynamically with abrupt changes in cycle length.24 25
Possible Pathophysiological Mechanisms for
Abnormal Beat-to-Beat Dynamics and a Propensity for
VT
Temporal changes in dynamic processes and bifurcations are
commonly observed in nonlinear dynamics as cascades to fixed
periodicity or chaotic dynamics.26 27 From a biophysical
point of view, electrical alternans is thought to represent an
unstable prechaotic state favoring the onset of life-threatening
arrhythmia.26 27 The association between
abnormalities in beat-to-beat RR-interval dynamics suggestive
of unstable nonlinear dynamics and the onset of VT events may be
explained by their common pathophysiological
mechanisms. Reductions in low-frequency oscillations in
HRV have been shown to be associated with increased levels of
circulating catecholamines.28 29 Periodic
doubling and alternating electrical dynamics have also been described
in norepinephrine-treated dogs,30 and it
has been suggested that T-wave alternans may be a consequence of
increased sympathetic tone.21 High
norepinephrine levels have also been reported to be
associated with complex Poincaré plots in patients with advanced
heart failure.31 These findings suggest that the
sympathetic nervous system may exert a prominent effect on abnormal
beat-to-beat RR-interval dynamics, a finding that is
consistent with its arrhythmic effects.
Instantaneous RR-interval variability in the patients with a propensity for VT was not related to measures of long-term variability, and its magnitude was similar at both fast and slow heart rates, suggesting that complex patterns and beat-to-beat alternans may not be determined by the same physiological mechanisms as the other components of HRV. Instantaneous changes in RR intervals are most likely to be vagally mediated, because vagal effects on the sinus node occur faster than sympathetically mediated effects. Increased vagal tone as a response to high sympathetic activity, a phenomenon of accentuated antagonism,32 is one possible explanation for the increased instantaneous beat-to-beat changes in RR intervals combined with reduced long-term variability. Since the frequency of beat-to-beat modulation during alternans is not related to the usual frequency of respiration, however, the complex beat-to-beat patterns may not be respiratory in origin. One potential explanation for the alternating periodicity could be electrophysiological abnormality at the sinus node level as a result of either autonomic or hemodynamic perturbation.
Conclusions
The present data indicate that reduced long-term
continuous RR-interval variability associated with episodes of
alternating variability is a specific sign of a propensity for the
spontaneous onset of sustained VT. We suggest that abnormal
beat-to-beat heart-rate dynamics, possibly due to altered
autonomic or neurohumoral influences, may result in electrical
instability, contributing to the spontaneous onset of
life-threatening ventricular arrhythmias in
hearts conditioned to such events by preexisting structural
abnormalities.33
|
Selected Abbreviations and Acronyms |
|---|
|
|
Acknowledgments |
|---|
This study was supported by grants from the Medical Council of the Academy of Finland and the Finnish Foundation for Cardiovascular Research, Helsinki, Finland. The authors wish to thank Tuija Ranta and Pirkko Huikuri, RN, for technical assistance.
Received August 14, 1995; revision received November 28, 1995; accepted December 6, 1995.
|
References |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Farrell TG, Bashir Y, Cripps T, Malik M, Poloniecki J, Bennett ED, Ward DE, Camm AJ. Risk stratification for arrhythmic events in postinfarction patients based on heart rate variability, ambulatory electrocardiographic variables and the signal-averaged electrocardiogram. J Am Coll Cardiol. 1991;18:687-697. [Abstract]
2.
Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM,
Kleiger RE, Rottman JN. Frequency domain measures of heart
period variability and mortality after myocardial infarction.
Circulation. 1992;85:164-171.
3. Valkama JO, Huikuri HV, Koistinen MJ, Yli-Mäyry S, Airaksinen KEJ, Myerburg RJ. Relation between heart rate variability and spontaneous and induced ventricular arrhythmias in patients with coronary artery disease. J Am Coll Cardiol. 1995;25:437-443. [Abstract]
4. Huikuri HV, Koistinen MJ, Yli-Mäyry S, Airaksinen KEJ, Seppänen T, Ikäheimo MJ, Myerburg RJ. Impaired low frequency oscillation of heart rate in patients with prior acute myocardial infarction and life-threatening arrhythmias. Am J Cardiol. 1995;76:56-60. [Medline] [Order article via Infotrieve]
5. Vybiral T, Glaeser DH, Goldberger AL, Rigney DR, Hess KR, Mietus J, Skinner JE, Francis M, Pratt CM. Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation. J Am Coll Cardiol. 1993;22:557-565. [Abstract]
6. Goldberger AL, West BJ. Applications of nonlinear dynamics to clinical cardiology. Ann N Y Acad Sci. 1987;504:155-212.
7. Denton TA, Diamond GA, Helfant RH, Khan S, Karagueuzian H. Fascinating rhythm: a primer to chaos theory and its application to cardiology. Am Heart J. 1990;120:1419-1439. [Medline] [Order article via Infotrieve]
8. Mayer-Kress G, Yates FE, Benton L, Keidel M, Tirsch W, Pöppl SJ, Geist K. Dimensional analysis of nonlinear oscillations in brain, heart, and muscle. Math Biosci. 1988;90:155-182.
9. Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol. 1994;226:H1643-H1655.
10. Woo MA, Stevenson WG, Moser DK, Harper RM, Trelease R. Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J. 1992;123:704-710. [Medline] [Order article via Infotrieve]
11. Raetz SL, Richard CA, Garfinkel A, Harper RM. Dynamic characteristics of cardiac R-R intervals during sleep and waking states. Sleep. 1991;14:526-533. [Medline] [Order article via Infotrieve]
12. Schechtman VL, Raetz SL, Harper RK, Garfinkel A, Wilson AJ, Southall DP, Harper RM. Dynamic analysis of cardiac R-R intervals in normal infants and in infants who subsequently succumbed to the sudden death syndrome. Pediatr Res. 1992;31:606-612. [Medline] [Order article via Infotrieve]
13. Schechtman VL, Harper RK, Harper RM. Development of heart rate dynamics during sleep-waking states in normal infants. Pediatr Res. 1993;34:618-623. [Medline] [Order article via Infotrieve]
14. Huikuri HV, Cox M, Interian A, Glicksman F, Kessler KM, Castellanos A, Myerburg RJ. Efficacy of intravenous propranolol for suppression of inducibility of ventricular tachyarrhythmias with different electrophysiologic characteristics in coronary artery disease. Am J Cardiol. 1989;64:1305-1309. [Medline] [Order article via Infotrieve]
15. Yli-Mäyry S, Huikuri HV, Airaksinen KEJ, Ikäheimo MJ, Linnaluoto MK, Takkunen JT. Evaluation of exercise test in predicting cardiac events after coronary artery bypass graft surgery. Am J Cardiol. 1992;69:1503-1507.
16. Airaksinen KEJ, Koistinen MJ, Ikäheimo MJ, Huikuri HV, Korhonen U, Pirttiaho H, Linnaluoto MK, Takkunen JT. Augmentation of atrial contribution to left ventricular filling in IDDM subjects as assessed by Doppler echocardiography. Diabetes Care. 1989;12:159-161. [Abstract]
17. Huikuri HV, Linnaluoto MK, Seppänen T, Airaksinen KEJ, Kessler KM, Takkunen JT, Myerburg RJ. Circadian rhythm of heart rate variability in survivors of cardiac arrest. Am J Cardiol. 1992;70:610-615. [Medline] [Order article via Infotrieve]
18.
Huikuri HV, Valkama JO, Airaksinen KEJ, Seppänen
T, Kessler KM, Takkunen JT, Myerburg RJ. Frequency domain
measures of heart rate variability before the onset of nonsustained and
sustained ventricular tachycardia in patients
with coronary artery disease.
Circulation. 1993;87:1220-1228.
19. Kay SM, Marple SL. Spectrum analysis: a modern perspective. Proc IEEE. 1981;69:1380-1384.
20. Skinner JE, Pratt CM, Vybiral T. A reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrillation in human subjects. Am Heart J. 1993;125:731-743. [Medline] [Order article via Infotrieve]
21.
Nearing BD, Huang AH, Verrier RL. Dynamic
tracking of cardiac vulnerability by complex demodulation of the T
wave. Science. 1991;252:437-440.
22. Raeder EA, Rosenbaum DS, Bhasin R, Cohen RJ. Alternating morphology of the QRST complex preceding sudden death. N Engl J Med. 1992;326:271-272. [Medline] [Order article via Infotrieve]
23.
Rosenbaum DS, Jackson LE, Smith JM, Garan H, Ruskin JN,
Cohen RJ. Electrical alternans and vulnerability to
ventricular arrhythmias. N
Engl J Med. 1994;330:235-241.
24.
Saitoh H, Bailey JC, Surawicz B. Alternans of
action potential duration after abrupt shortening of cycle length:
differences between dog Purkinje and ventricular muscle
fibers. Circ Res. 1988;62:1027-1040.
25.
Rosenbaum DS, Kaplan DT, Kanai A, Jackson L, Garan H,
Cohen RJ, Salama G. Repolarization inhomogeneities in
ventricular myocardium change dynamically with
abrupt cycle length shortening.
Circulation. 1991;84:1333-1345.
26. Guevara MR, Glass L. Phase locking, period doubling bifurcations and chaos in a mathematical model of a periodically driven oscillator: a theory for the entrainment of biological oscillators and the genesis of cardiac dysrhythmias. J Math Biol. 1982;14:1-23. [Medline] [Order article via Infotrieve]
27. Kaplan DT, Goldberger AL. Chaos in cardiology. J Cardiovasc Electrophysiol. 1991;2:342-354.
28.
Breuer HW, Skyschally A, Schulz R, Martin C, Wehr M,
Heusch G. Heart rate variability and circulating
catecholamine concentrations during steady state exercise
in healthy volunteers. Br Heart J. 1993;70:144-149.
29. Kienzle MG, Ferguson DW, Birkett CL, Myers GA, Berg WJ, Mariano DJ. Clinical, hemodynamic and sympathetic neural correlates of heart rate variability in congestive heart failure. Am J Cardiol. 1992;69:761-767. [Medline] [Order article via Infotrieve]
30. Ritzenberger AL, Adam DR, Cohen RJ. Period multupling: evidence for nonlinear behaviour of the canine heart. Nature. 1984;307:159-161. [Medline] [Order article via Infotrieve]
31. Woo MA, Stevenson WG, Moser DK, Middlekauff HR. Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure. J Am Coll Cardiol. 1994;23:565-569. [Abstract]
32.
Levy MN. Sympathetic-parasympathetic
interactions in the heart. Circ Res. 1971;29:437-445.
33. Myerburg RJ, Kessler KM, Kimura S, Castellanos A. Sudden cardiac death: future approaches based on identification and control of transient risk factors. J Cardiovasc Electrophysiol. 1992;3:626-640.
This article has been cited by other articles:
 |
|
 |
|
  A. Porta, K. R. Casali, A. G. Casali, T. Gnecchi-Ruscone, E. Tobaldini, N. Montano, S. Lange, D. Geue, D. Cysarz, and P. Van Leeuwen Temporal asymmetries of short-term heart period variability are linked to autonomic regulation Am J Physiol Regulatory Integrative Comp Physiol, August 1, 2008; 295(2): R550 - R557. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Zhong, K.-M. Jan, K. H. Ju, and K. H. Chon Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability Am J Physiol Heart Circ Physiol, September 1, 2006; 291(3): H1475 - H1483. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Guzzetti, E. Borroni, P. E. Garbelli, E. Ceriani, P. D. Bella, N. Montano, C. Cogliati, V. K. Somers, A. Mallani, and A. Porta Symbolic Dynamics of Heart Rate Variability: A Probe to Investigate Cardiac Autonomic Modulation Circulation, July 26, 2005; 112(4): 465 - 470. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 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]
|
|
|
|
 |
|
 M.J. Reed, C.E. Robertson, and P.S. Addison Heart rate variability measurements and the prediction of ventricular arrhythmias QJM, February 1, 2005; 98(2): 87 - 95. [Full Text] [PDF]
|
|
|
|
 |
|
 A.-M. Still, P. Raatikainen, A. Ylitalo, H. Kauma, M. Ikäheimo, Y. A. Kesäniemi, and H. V. Huikuri Prevalence, characteristics and natural course of inappropriate sinus tachycardia Europace, January 1, 2005; 7(2): 104 - 112. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C P Earnest, R Jurca, T S Church, J L Chicharro, J Hoyos, and A Lucia Relation between physical exertion and heart rate variability characteristics in professional cyclists during the Tour of Spain Br. J. Sports Med., October 1, 2004; 38(5): 568 - 575. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. K. Stein Increased randomness of heart rate could explain increased heart rate variability preceding onset of atrial fibrillation J. Am. Coll. Cardiol., August 4, 2004; 44(3): 668 - 669. [Full Text] [PDF]
|
|
|
|
 |
|
 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]
|
|
|
|
 |
|
 H. Luukinen, P. Laippala, and H.V. Huikuri Depressive symptoms and the risk of sudden cardiac death among the elderly Eur. Heart J., November 2, 2003; 24(22): 2021 - 2026. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Halamek, T. Kara, P. Jurak, M. Soucek, D. P. Francis, L. C. Davies, W. K. Shen, A. J.S. Coats, M. Novak, Z. Novakova, et al. Variability of Phase Shift Between Blood Pressure and Heart Rate Fluctuations: A Marker of Short-Term Circulation Control Circulation, July 22, 2003; 108(3): 292 - 297. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 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]
|
|
|
|
 |
|
 A. Loimaala, H. Huikuri, P. Oja, M. Pasanen, and I. Vuori Controlled 5-mo aerobic training improves heart rate but not heart rate variability or baroreflex sensitivity J Appl Physiol, November 1, 2000; 89(5): 1825 - 1829. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Pruvot, G. Thonet, J.-M. Vesin, G. van-Melle, K. Seidl, H. Schmidinger, J. Brachmann, W. Jung, E. Hoffmann, R. Tavernier, et al. Heart Rate Dynamics at the Onset of Ventricular Tachyarrhythmias as Retrieved From Implantable Cardioverter-Defibrillators in Patients With Coronary Artery Disease Circulation, May 23, 2000; 101(20): 2398 - 2404. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. V. Huikuri, T. H. Makikallio, C.-K. Peng, A. L. Goldberger, U. Hintze, and M. Moller Fractal Correlation Properties of R-R Interval Dynamics and Mortality in Patients With Depressed Left Ventricular Function After an Acute Myocardial Infarction Circulation, January 4, 2000; 101(1): 47 - 53. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. V. Huikuri, T. Makikallio, K. E. J. Airaksinen, R. Mitrani, A. Castellanos, and R. J. Myerburg Measurement of heart rate variability: a clinical tool or a research toy? J. Am. Coll. Cardiol., December 1, 1999; 34(7): 1878 - 1883. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Vikman, T. H. Makikallio, S. Yli-Mayry, S. Pikkujamsa, A.-M. Koivisto, P. Reinikainen, K. E. J. Airaksinen, and H. V. Huikuri Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation Circulation, November 16, 1999; 100(20): 2079 - 2084. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. V. Huikuri, A.-M. Poutiainen, T. H. Makikallio, M. J. Koistinen, K. E. J. Airaksinen, R. D. Mitrani, R. J. Myerburg, and A. Castellanos Dynamic Behavior and Autonomic Regulation of Ectopic Atrial Pacemakers Circulation, September 28, 1999; 100(13): 1416 - 1422. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. T. Korpelainen, K. A. Sotaniemi, A. Makikallio, H. V. Huikuri, and V. V. Myllyla Dynamic Behavior of Heart Rate in Ischemic Stroke Stroke, May 1, 1999; 30(5): 1008 - 1013. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Shusterman, B. Aysin, V. Gottipaty, R. Weiss, S. Brode, D. Schwartzman, K. P. Anderson, and for the ESVEM Investigators Autonomic nervous system activity and the spontaneous initiation of ventricular tachycardia J. Am. Coll. Cardiol., December 1, 1998; 32(7): 1891 - 1899. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. D Wagner and P. B Persson Chaos in the cardiovascular system: an update Cardiovasc Res, November 1, 1998; 40(2): 257 - 264. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. W. Hogue Jr, P. P. Domitrovich, P. K. Stein, G. D. Despotis, L. Re, R. B. Schuessler, R. E. Kleiger, and J. N. Rottman RR Interval Dynamics Before Atrial Fibrillation in Patients After Coronary Artery Bypass Graft Surgery Circulation, August 4, 1998; 98(5): 429 - 434. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. V. Huikuri, T. H. Makikallio, K. E. J. Airaksinen, T. Seppanen, P. Puukka, I. J. Raiha, and L. B. Sourander Power-Law Relationship of Heart Rate Variability as a Predictor of Mortality in the Elderly Circulation, May 26, 1998; 97(20): 2031 - 2036. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. P. Tulppo, T. H. Makikallio, T. Seppanen, R. T. Laukkanen, and H. V. Huikuri Vagal modulation of heart rate during exercise: effects of age and physical fitness Am J Physiol Heart Circ Physiol, February 1, 1998; 274(2): H424 - H429. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||