Published online before print March 30, 2001, doi: 10.1161/hh0701.088770
© 2001 American Heart Association, Inc.
Integrative Physiology |
Multidimensional Rhythm Disturbances as a Precursor of Sustained Ventricular Tachyarrhythmias
From the University of Pittsburgh (V.S., B.A.), Pa; Marshfield Clinic (K.P.A.), Marshfield, Wis; and Biosonix, Ltd (A.B.), Hod-Hasharon, Israel.
Correspondence to Vladimir Shusterman, University of Pittsburgh, 200 Lothrop St, Room B535, Pittsburgh, PA 15213. E-mail shustermanv{at}msx.upmc.edu
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Abstract |
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Abstract—Cardiac cycle dynamics reflect underlying physiological changes that could predict imminent arrhythmias but are obscured by high complexity, nonstationarity, and large interindividual differences. To overcome these problems, we developed an adaptive technique, referred to as the modified Karhunen-Loeve transform (MKLT), that identifies an individual characteristic ("core") pattern of cardiac cycles and then tracks the changes in the pattern by projecting the signal onto characteristic eigenvectors. We hypothesized that disturbances in the core pattern, indicating progressive destabilization of cardiac rhythm, would predict the onset of spontaneous sustained ventricular tachyarrhythmias (VTAs) better than previously reported methods. We analyzed serial ambulatory ECGs recorded in 57 patients at the time of VTA and non-VTA 24-hour periods. The disturbances in the pattern were found in 82% of the recordings before the onset of impending VTA, and their dimensionality, defined as the number of unstable orthogonal projections, increased gradually several hours before the onset. MKLT provided greater sensitivity and specificity (70% and 93%) compared with the best traditional method (68% and 67%, respectively). We present a theoretical analysis of MKLT and describe the effects of ectopy and slow changes in cardiac cycles on the disturbances in the pattern. We conclude that MKLT provides greater predictive accuracy than previously reported methods. The improvement is due to the use of individual patterns as a reference for tracking the changes. Because this approach is independent of the group reference values or the underlying clinical context, it should have substantial potential for predicting other forms of arrhythmic events in other populations.
Key Words: ventricular arrhythmias • cardiac cycle dynamics • orthogonal decomposition
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Introduction |
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Although substantial progress has been made in the understanding of arrhythmia mechanisms and identification of individuals at risk, short-term prediction of the timing of onset of sustained ventricular tachyarrhythmias (VTAs) has lagged, delaying development of preventive treatments.1 Because autonomic activity is thought to be an important trigger of VTA and because cardiac cycle lengths (CCLs) are modulated by autonomic tone, it has been assumed that the analysis of the changes in CCL could predict the timing as well as the triggers of VTA.2 This has been confirmed by studies that demonstrated heart rate increase before the VTA onset in many patients.2 3 4 5 However, the change in heart rate before the onset of VTA is usually small and indistinguishable from random daily variations.2 6 Descriptors of heart rate variability proved useful in general risk assessment but failed to predict the timing of VTA.5 7 Probable reasons for the failure include the high complexity of the interacting physiological influences and violation of the statistical assumptions that underlie traditional techniques.8 In addition, the attempts to summarize highly nonstationary and individually variable CCL dynamics in a few indices effectively resulted in non-uniform data compression and frequent oversight of individual changes that precede the onset of VTA.9
To overcome these problems, we sought a new approach that (1) automatically learns individual characteristic or "core" patterns of CCL (CPCCL); (2) accommodates the diversity of individual CPCCL, including the presence of ectopy and changes in neurohormonal activity; and (3) tracks the changes in CPCCL regardless of their linear or nonlinear properties. We used a pattern-recognition approach based on the modified Karhunen-Loeve transform (MKLT) to develop a method that, in each individual, identifies CPCCL; we then tested the hypothesis that disturbances in CPCCL indicate destabilization of cardiac rhythm that precedes the onset of spontaneous, sustained VTA. To elucidate the origins of the disturbances, we examined the effects of ectopy and compared MKLT with other techniques using the identical data set.
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Materials and Methods |
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Patient Characteristics
Clinical and Holter ECG data were collected prospectively in a uniform fashion in the course of a NIH-sponsored clinical trial; protocols, methods, definitions, and patient characteristics have been described in detail.2 9 In brief, ambulatory 24-hour ECGs from 57 patients (87% male, age 64±10 years, 83% ischemic heart disease, and left ventricular ejection fraction of 0.36±0.15) with spontaneous sustained VTA (duration:
30 seconds; rate: 100 bpm) and
with a minimum of 2 hours of ECG data preceding the onset of VTA were
examined. In addition, 86 serial 24-hour ECG recordings without
VTA events were obtained from the same patients and included into
analysis. All patients had a history of cardiac arrest,
documented ventricular fibrillation, sustained
ventricular tachycardia, or syncope. Enrolled
patients had to have at least 10 premature ventricular
complexes per hour and VTA induced at
electrophysiological study. None of the
patients were receiving antiarrhythmic drugs at the time of the
recordings. Patients with recent myocardial infarction, long-QT
syndrome, hypertrophic cardiomyopathy, or
arrhythmias due to transient or reversible disorders were
excluded.
Data Processing
ECG data were digitized at 400 Hz, and the QRS
complexes were classified using custom software and verified by a
cardiologist.2 The effects of
ectopy were estimated by analyzing an unfiltered series (all natural
cycles included) and a filtered series that excluded ectopic beats and
the 2 sinus beats that preceded and followed each ectopic beat. The
effects of pauses, escape beats, and short-long-short sequences were
eliminated by excluding intervals that differed by >75 ms from the
moving average of 5 cycles. Gaps in the time series resulting from
noise or ectopic beats were interpolated with linear
splines.10 The filtered
series of RR intervals were regularly spaced and sampled at 2 Hz using
a boxcar low-pass
filter.11
Time Domain Analysis
The mean and SD, square root of the mean of the
squared differences between adjacent cardiac cycles (r-MSSD), and
percentage of differences between adjacent cycles that are >50 ms
(pNN50) were estimated.
Frequency Domain Analysis
Power was integrated in the following frequency
ranges: total power (TP), 0.01 to 0.4 Hz; high-frequency power (HFP),
0.15 to 0.4 Hz; low-frequency power (LFP), 0.04 to 0.15 Hz; and
very-low-frequency power (VLFP), 0.01 to 0.04 Hz. The ratio of low- to
high-frequency power (LFP/HFP) was also
calculated.
Nonlinear Indices
Approximate entropy (ApEn), a measure of regularity,
was estimated as described by Pincus and
Keefe.12 Briefly, ApEn
measures the likelihood that the maximum distance between the scalar
components of vectors in m
dimensional space will remain similar in
m+1 dimensions. Low values of
ApEn signify that the m and
m+1 dimensional patterns are
similar. We used the same values of dimension and distance (2 and 20%
of SD, respectively) as in the previous studies of the series of
cardiac
cycles.13 14
To calculate the 
-1 and -2 scaling exponents, first we
computed the root-mean-square fluctuations of integrated and detrended
time series.15 Then the
relationship between the root-mean-square fluctuations and the segment
length was obtained as a slope on a double-log graph for the segments
that were shorter than 11 beats (-1) and those that were longer than
11 beats (-2).
Pattern Recognition Analysis
In this algorithm, the series of cardiac cycles is
separated into 5-minute segments referred to as the unit
vectors.16 Each unit vector
has 600 points and can be represented as a vector with 600
components in a Hilbert space. The high dimensionality of this vector
results in unwieldy complexity and obscures the detection of underlying
pattern. The Karhunen-Loeve transform (or the principal component
analysis), which was modified by the investigators for this
application, allows simplifying the pattern and exposing its most
significant features. The reduction of dimensionality of the unit
vector is achieved by projecting it onto linearly independent basis
vectors or eigenvectors, which represent the most
characteristic features of the signal. To obtain the eigenvectors,
first, a unit autocovariance matrix,
U, is calculated for each unit
vector (matrices appear in boldface type throughout this article). In
this matrix, the strongest relationships between the data samples are
magnified, whereas the weakest ones that are usually related to noise
are reduced. Averaging the matrices
U for all unit vectors yields an
average autocovariance matrix,
C, that represents the
most characteristic components of the entire signal. Then, the
characteristic eigenvectors are obtained by diagonalizing the matrix
C. To reduce the dimensionality
of the original data with a minimal information loss, we select the
eigenvectors that correspond to the biggest
eigenvalues.17 The quality
of this reduction is controlled by the residual error of the signal
reconstruction from its low-dimensional projection. MKLT
coefficients are obtained by projecting the original series onto
the corresponding eigenvectors; the time series of each MKLT
coefficient represents temporal changes in the projection
of the signal onto the corresponding eigenvector. Finally, because the
time course of the changes does not correspond to the constant 5-minute
length of the unit vectors, the window lengths are adjusted to separate
the segments with different properties (see online data supplement
available at http://www.circresaha.org for further
description).
Analysis of the Core Pattern of Cardiac
Cycles
The first 6 eigenvectors of the matrix
C, which contain most of the
information about the signal, were extracted, and their MLKT
coefficients,
ck, were
obtained as described above. The time series of
ck were
used to estimate the SD of the series of each coefficient
(
k). A 3k threshold
was established so that the probability of a random occurrence of the
CCLs exceeding 3k would be <0.0013 assuming
a normal distribution. At the next step, the adaptive segmentation was
applied to
c1
through
c6, and
the number of coefficients exceeding the threshold
(3k) was calculated in each window
(see online data supplement available at http://www.circresaha.org).
For each subject, the thresholds were determined using the training set
and then applied to the recordings from the same subject in the
test sets. Combined excursions of several
ck
values beyond the threshold reflect simultaneous
instabilities in the orthogonal projections of the signal, which in
turn signify complex and pronounced changes in the pattern of cardiac
cycles.
The CPCCL is said to be at a steady
state when all 6 MKLT coefficients are within the limits of
3k. An excursion of 1 or more MKLT
coefficients beyond the 3k threshold
indicates disturbances of CPCCL. The
dimensionality (Dm) of the
disturbances is defined as the number of MKLT coefficients that
simultaneously exceed the corresponding
3k thresholds. Thus,
Dm shows the number of
orthogonal projections in which the behavior of the series becomes
unstable.
The relationships between the variables were analyzed using a nonlinear Spearman correlation to eliminate the effects of the scaling differences between the studied variables.
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Results |
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Steady-State Pattern of Cardiac Cycles
The process of distinguishing the steady-state CPCCL and its disturbances is illustrated on a representative series of cardiac cycles beginning 16 hours before the onset of a spontaneous, sustained VTA in Figure 1
. No clear pattern can be found in the plot of
cardiac cycles
(Figure 1A). However, the 6 MKLT coefficients plotted over
the same time frame
(Figures 1B through 1G) expose the transition from the
steady-state pattern to the CPCCL
disturbances.
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The shape and the magnitude of the autocovariance
matrix C (see Materials and
Methods) provide insight into the changes in
CPCCL. Matrix representations of the
steady-state CPCCL have smooth shape and low
amplitudes of variations, indicating a regular but weakly correlated
and nonperiodic structure of the series
(Figure 2, top and middle). An increase in the magnitude of
the matrix elements and the number of spurious correlation spikes
during the CPCCL disturbances shows that
multiple nonstationarities and irregular sequences develop toward the
onset of VTA
(Figure 2, bottom).
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The most significant basis vectors that represent
CPCCL and their frequency content are shown in
Figure 3. Because the slow changes predominate, the spectral
energy of all eigenvectors is concentrated in the low frequency range.
Using our previous experiments, we chose the first 6 eigenvectors,
which contain 88% of the information and represent
CPCCL with a 12% residual error. The time
series of the corresponding MKLT coefficients track the most
significant changes in the structure of the signal over time, and
multidimensional (Dm>3)
disturbances in CPCCL were detected in
most patients before the initiation of spontaneous VTA
(Figure 4). Of note, different combinations of MKLT
coefficients exhibited disturbances equally often before the
onset time. Therefore, the dimensionality of the disturbances
Dm, rather than the specific
combinations of MKLT coefficients, indicated an unstable trajectory of
the cardiac rhythm that led to the initiation of
arrhythmia.
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i were ordered according to the corresponding eigenvalues of which the absolute values represent the amount of information in the corresponding eigenvectors. After this reordering,
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Influence of Heart Rate and Ectopy on the
Pattern of Cardiac Cycles
Average heart rate represents an envelope or
slowly changing component of the cardiac cycle series. In most
subjects, the slow, minutes-to-hours variations of heart rate are
predominant, and this envelope contains most of the information about
the series.9 Therefore, the
time series of the first MKLT coefficient
c1
tracks the slow changes in the heart rate
(Figure 1B
). However, the fact that the changes occur
simultaneously in several MKLT coefficients shows that, in
addition to the slow changes in heart rate,
CPCCL and its disturbances are linked to
other independent dynamic processes.
To investigate the effects of ectopy on the series of MKLT
coefficients, the analysis was repeated after filtering out
ventricular and supraventricular ectopy and
outliers as described in Materials and Methods
(Figure 1A). Because ectopic activity introduces ultrashort
interbeat irregularities into the series of cardiac cycles, the
processing effectively eliminated or reduced the high-frequency
beat-to-beat oscillations. Although ectopy and short-term
irregularities influence CPCCL, the filtering
did not affect the detection of CPCCL
disturbances that preceded the onset of VTA. This result shows
that the impact of slow changes in the cardiac cycles on
CPCCL is more important than the influence of
ectopy and ultrashort interbeat irregularities. Note that measurements
of the heart rate envelope (first MKLT coefficient) cannot adequately
describe the complexity of these slow changes; at least 6 MKLT
components are required for tracking the CPCCL
disturbances.
Because the eigenvectors are orthogonal, we examined the
dynamics of the series with and without ectopy using 3-dimensional
trajectories of the variances of the first 3 MKLT coefficients
(Figure 5). The variations of the trajectories in the plane
of the 2 most significant MKLT coefficients are similar, indicating
that the disturbances in CPCCL are not
eliminated by filtering of ectopy. However, the series without ectopy
has lower amplitude of variation for the third MKLT coefficient,
showing that ectopy and ultrashort irregularities mostly affect the
higher-order MKLT coefficients.
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Multidimensional Disturbances in the
Pattern of Cardiac Cycles and the Initiation of
Ventricular Tachycardia
The training data set comprised tapes from 30 patients
with a single VTA during the 24 hours. Using the disturbances
that had Dm=4 to 6, the
initiation of VTA was predicted with 70% sensitivity and 93%
specificity during the 6.8±4.4 hours before the onset
(Table 1). The number of MKLT coefficients exceeding the
threshold increased progressively over several hours before the event,
indicating gradual increase in the dimensionality (complexity) of the
disturbances and progressive destabilization of cardiac rhythm
(Figure 4).
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The robustness of the method was validated in the 2
demanding test sets. The generality test set consisted of 27 ambulatory
recordings from a different group of patients who had several
VTAs during the 24-hour period. The longest VTA was chosen as the index
event. Multiple disturbances that preceded the onset of each
VTA enhanced the variance of MKLT coefficients and interfered with the
analysis of the index event. This provided a naturally
"noisy" environment for testing the robustness of MKLT on the most
complicated perturbations of cardiac cycles. Predictably, the accuracy
of the method decreased, but the expected decline of sensitivity and
specificity was relatively modest
(Table 1). The specificity test set included 86 serial
24-hour VTA-free ECGs from the same patients who had VTAs in the
training set. In this test set, a steady-state
CPCCL was identified and the
disturbances leading to the initiation of VTA were excluded,
with a specificity of 73%. When the arrhythmia-free tape was
recorded within 3 months from the time of the training
recording, the specificity increased to 80% (n=40), which
suggests that CPCCL remains unchanged for 3
months and then changes slowly over a longer period. Inclusion of
ectopy into the analysis increased the sensitivity of the
method but did not change the specificity as compared with the series
of CCLs without ectopic beats and outliers
(Table 1).
Relationship Between the Changes in the Pattern
of Cardiac Cycles and Traditional Linear and Nonlinear Indices
The sensitivity and specificity of MKLT in predicting
the onset time of VTA
(Table 1) were higher than those of traditional linear and
nonlinear methods
(Table 2). Series of the time domain, spectral, and
nonlinear indices were strongly correlated with the dynamics of cardiac
cycles
(P<10-4).
The most prominent changes in all studied indices resulted from signal
nonstationarities that elicit profound and complex perturbations in the
basic structure of the series
(Figure 6). However, the traditional indices could not
distinguish among the changes in a singular property, in a multitude of
properties, and in the entire structure of the series. The sensitivity
of each index depended on a type of perturbation. Therefore, no single
index could expose the complexity or the magnitude of multidimensional
changes; some perturbations would be missed or underestimated
with a single-index approach. In contrast, MKLT provides an accurate
quantitative description of the magnitude and complexity (ie,
dimensionality) of the changes, and therefore, it is more effective in
detecting the transients that precede the onset of VTA
(Table 2).
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. LFP is shown because it exhibited the most pronounced change among the other spectral indices before onset of VTA. |
Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Main Results and Comparison With Previous Studies
Multidimensional disturbances in the individual pattern of cardiac cycles provided more sensitive and specific prediction of the onset time of VTA than traditional linear and nonlinear methods (Tables 1
and 2). Although changes in heart rate, traditional
time domain, spectral, and nonlinear estimators, including ApEn and
scaling exponents, have been reported before the onset of VTA, their
predictive value was not
assessed.2 3 4 5 18 Data about the accuracy of prediction of the onset time are scarce. Skinner et al19 reported that changes in the correlation dimension, a nonlinear measure of signal complexity, identified 11 Holter ECGs with ventricular fibrillation (sensitivity, 91%; specificity, 85%). Mani et al20 found that changes in the spectral power in the 0.8 to 0.9–Hz frequency range predicted the onset of VTA with 76% sensitivity and 76% specificity in 78 patients using 1024 CCLs. Because the training set and the test set were not separated in these studies, the generality of the results (ie, applicability to other groups) could not be confirmed.21 Furthermore, the specificity of the findings is unclear because the analysis did not include serial recordings from the same patients during the VTA-free periods.
Because comparative analysis of the methods applied
to different groups is limited, we used an identical data set to
compare the performance of MKLT with that of the traditional
techniques
(Table 2). The methods were initially applied to a training
set, and then the sensitivity and specificity were tested on the other
2 test sets. The generality test set included 24-hour ECGs from a
different group of patients who had multiple spontaneous VTAs. In
contrast to the previous studies, the prediction was considered correct
if and only if the onset occurred within the same time window, of which
the length was determined by the algorithm (see online data supplement
available at http://www.circresaha.org for details). The specificity
test set included serial 24-hour VTA-free ECGs from the same patients
who had VTAs in the training set. This set allowed us to assess
specificity and temporal stability of MKLT. In all sets, the predictive
accuracy of MKLT was similar, which confirms generality and reliability
of the results
(Table 1).21 The
predictive accuracy did not change if the recordings were
obtained within 3 months, which shows that CPCCL
remains stable during this period.
In agreement with previous studies, inclusion of ectopic beats into analysis improved the accuracy of the prediction.20 This shows that an increase in the number of ectopic beats and ultrashort irregularity plays an important role in the CPCCL disturbances in some patients. Still, the disturbances of the same dimensionality could be detected before VTAs in more than half of those patients who had them before filtering. This suggests that in most patients, the CPCCL and its disturbances are determined not by ectopy or ultrashort irregularities but by the more complex, longer-term relationships between the cardiac cycles. This observation is consistent with the predominant spectral energy concentration in the very-low-frequency range, which has an important prognostic value.22 Our results, as well as other recent reports, provide new insights into the role of the very-low-frequency oscillations and their nonstationary behavior.23
Modified Karhunen-Loeve Transform
Although the traditional methods detected some
changes, the search for specific precursors of VTA was impeded by
violation of the statistical assumptions that underlie the traditional
techniques. The traditional methods assume (1) that the signal is
stationary and (2) that the changes occur in a single, a
priori–defined property, whereas all other properties remain
unchanged. However, the series of CCL before the onset of VTA are
highly nonstationary, have enormous structural individual variability,
and have a large number of unstable properties that cannot be
adequately described by single-valued
techniques.8
MKLT can be considered as a generalization of the
traditional methods that are limited by the assumptions of the
stationarity of the signals and by the single-feature searching
capabilities. Indeed, the Fourier transform can be considered as a
special case of MKLT in which the basis functions are complex
exponentials.17 If the
series is periodic and stationary, the Fourier transform can
project the signal onto a finite set of periodic basis functions
and thus expose the corresponding frequency elements. However,
stationarity and exact periodicity are not characteristics of the
signals that precede VTA. The time domain indices, including SD,
r-MSSD, and pNN50, also capture certain a priori–defined
properties of the signal that may or may not represent the
changes that occur before the onset of
VTA.24 The nonlinear
descriptors, ApEn and scaling exponents, also attempt to summarize the
complexity of the series using a single measure that is selectively
sensitive to certain types of changes. ApEn, for example, does not
respond to the changes in amplitude but reacts to the changes in
variance and therefore can be used only on the series of which the
variances are relatively
stable.12 As
Figure 6 clearly shows, changes in ApEn and scaling
exponents before the onset of VTA reflect changes in the variance
rather than specific changes in the complexity of the signal. In
addition, interpretation of changes in ApEn is obscured by its
sensitivity to ectopy, whereas MKLT analysis, as our results
demonstrate, is relatively unaffected by
ectopy.25
Semantic analysis, which has been proposed for characterizing short sequences of cardiac cycles, can also be considered as a special case of MKLT in which a small number of features are explicitly modeled using a limited set of parameters.26 The method is appropriate for simple patterns; however, complex and individually variable disturbances would require an enormous number of descriptors. In contrast, MKLT has an advantage of learning complex, highly variable individual patterns without the limitation of explicit modeling.
Using a method similar to MKLT, Ivanov et al8 showed that a set of wavelet coefficients provides a better general assessment of the cardiac cycle complexity than single-valued techniques. Motivated by the complexity of cardiac cycle dynamics and the inability of any single index to represent multidimensional changes, we used a set of MKLT coefficients to track the dynamics of the series. However, the method of Ivanov et al8 gives a general assessment of signal complexity, whereas MKLT was applied here to detect and quantify the complexity (dimensionality) of the short-term changes. In contrast to the constant, empirically defined wavelet function and analytic scales in the method of Ivanov et al,8 the MKLT basis vectors are directly derived from each individual series and represent a "fingerprint" or characteristic steady-state pattern. This adaptive property of MKLT makes it uniquely sensitive to the changes in the series regardless of interindividual differences.
The traditional Karhunen-Loeve transform (KLT) has long been used for analysis of electrocardiographic waveforms and their spatial and temporal distribution.27 28 There are, however, important differences between the traditional applications of KLT and MKLT analysis. First, the traditional KLT requires the investigated pattern, eg, the QRS complex, to be deterministic and already identified. In contrast, MKLT is "blind" to the shape and location of the characteristic pattern and does not require any prerequisite classification of the series of cardiac cycles. Second, in the traditional KLT, the resulting "typical" pattern resembles individual waveforms, and their relationship can be examined by visual inspection or correlation analysis. In MKLT, the characteristic pattern is complex and nondeterministic; this requires examination of the variances of MKLT coefficients. Third, the time windows in the traditional KLT analysis are constant and a priori defined, whereas in MKLT, the time windows are automatically adjusted to separate the segments with different properties.
Future Research
The idea that the dynamics of cardiac cycles may reveal
hidden instabilities that precede the onset of arrhythmias is
not new.29 Still, most
events are unheralded, which has led to the perception that the
initiation of malignant arrhythmias is the immediate
consequence of a random event such as a critically timed premature
beat. Unexplained is why the premature depolarizations that appear to
initiate VTA have not been shown to have the features that clearly
distinguish them from the thousands of premature beats that occur daily
in patients with heart disease but do not initiate
arrhythmias.1
In contrast, we detected disturbances in
CPCCL several hours before the onset of VTA. The
gradual increase in the dimensionality of the disturbances
(Figure 4) could reflect changes in the milieu that transform
an otherwise benign premature depolarization to a malignant trigger and
may explain why spontaneous arrhythmias usually occur without
the signs of intense stimulation (multiple tightly coupled
extrastimuli, acute ischemia, or high concentrations of
arrhythmogenic drugs) that is required for artificial initiation of
arrhythmias.1 The
slow development and continuance of a proarrhythmic vulnerable state
could also explain why sustained arrhythmias often occur in
clusters.30 On the other
hand, low-dimensional disturbances do not necessarily progress
but may resolve, followed by resumption of a steady state. Certain
modes of stimulation are shown to prevent arrhythmias,
suggesting that restoration of the steady-state
CPCCL reverses the progression of
electrophysiological changes and prevents
arrhythmia.31
Disturbances in CPCCL have also been reported before the onset of paroxysmal atrial fibrillation.32 Description of the time course and dimensionality of the disturbances that precede the onset of different arrhythmias might lead to the development of clinically useful predictive algorithms.
In summary, hours before the onset of sustained VTAs, there is evidence for progressive changes in the core pattern of cardiac cycles. Better understanding of these events could lead to methods of predicting and preventing arrhythmias and sudden cardiac death.
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Acknowledgments |
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This study was supported by Scientist Development Grant 0030248N from the American Heart Association, by NIH Specialized Center of Research Grant P50 HL52338, and by a grant from Guidant Corporation of St. Paul, Minn.
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Footnotes |
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Original received May 30, 2000; resubmission received December 13, 2000; revised resubmission received February 14, 2001; accepted February 14, 2001.
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References |
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