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(Circulation. 1997;96:842-848.)
© 1997 American Heart Association, Inc.
Articles |
Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics
From the Framingham Heart Study, Framingham, Mass (K.K.L.H., M.G.L., D.L.); Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (G.B.M.); Cardiovascular Division, Beth Israel Deaconess Medical Center, Boston, Mass (K.K.L.H., C.K.P., J.E.M., D.L., A.L.G.); Boston (Mass) University School of Medicine (D.L.); and the National Heart, Lung, and Blood Institute, Bethesda, Md (D.L.).
Correspondence to Kalon Ho, MD, MSc, RW-453, Cardiovascular Division, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215-5491. E-mail kho{at}bidmc.harvard.edu
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Abstract |
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Background Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted.
Methods and Results We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7±8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P<.01), LF (P<.01), VLF (P<.05), and TP (P<.01) and the nonlinear measure DFA (P<.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn (P>.3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD.
Conclusions These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.
Key Words: dynamics • Fourier analysis • heart failure • heart rate • survival
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Introduction |
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There has been much recent interest in analysis of HRV in a variety of clinical settings.1 2 3 The utility of conventional time- and frequency-domain HRV measures in assessing prognosis remains unclear, however, and the role of newer nonlinear indices of HRV is controversial. Often, subjective decisions (eg, related to treatment of noise and ectopy in the heart rate time series or to patient-specific selection of frequency bands) complicate the task of objective assessment of HRV measures. These subjective decisions not only obscure the utility of HRV measures but also add significantly to the cost of obtaining them. In the present investigation, therefore, we sought to address the following two questions in a feasibility study.
1. Can a fully automated analysis of HRV, requiring no subjective decisions, predict outcomes in a prospective, population-based study? For this purpose, we analyzed data obtained at the FHS from chronic CHF case patients and from sex- and age-matched control subjects, and we sought to develop HRV measures that are insensitive to the presence of typical errors in automated analysis of such data.
2. Do nonlinear indices of HRV have predictive value, in addition to time- and frequency-domain indices?3 4 5 6 To address this question, we studied ApEn, a measure of complexity,7 and DFA, an index of the presence or absence of long-term fractal correlations.8
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Methods |
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Subjects: CHF Case Patients and Control Subjects
Subjects for this analysis were drawn from participants in the FHS and the FOS. In 1948, 5209 residents of Framingham, Mass, were enrolled in the FHS, a prospective epidemiological study.9 10 These individuals have subsequently been evaluated at 2-year intervals with a review of their medical history, physical examination, ECG, blood tests, and various noninvasive pulmonary function and imaging studies. In 1971, children of the original study participants and spouses of these children (5124 in total) were entered in the FOS.11
The primary group of interest was individuals with CHF. From
information obtained at FHS and FOS examinations and from hospital and
physician records, a diagnosis of CHF was established by the
simultaneous presence of at least two major criteria or one
major and two minor criteria for heart failure (Table 1
).12 Minor criteria for heart failure were
acceptable only if they could not be attributed to another medical
condition. Information related to deaths was reviewed by three
physicians who assigned a cause of death according to established FHS
protocols.
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Ambulatory ECG Recording
Ambulatory ECG monitors were placed on participants for 2 to 4
hours during their evaluations in the 18th and 19th examination cycles
of the FHS (April 1983 to November 1985 and April 1985 to June 1988,
respectively) and during the 3rd examination cycle of the FOS (December
1983 to September 1987). Modified ECG leads V1 and
V5 were recorded simultaneously with
standard ambulatory ECG recorders (Clinical Data, Inc). During the
recording period, all subjects underwent a standard battery of
tests, including phlebotomy; measurements of height, weight, and blood
pressure; pulmonary function tests; 12-lead ECGs;
echocardiograms and other vascular ultrasound imaging; interviews and
self-administered questionnaires; and elucidation of medical history
and a physical examination performed by a physician. Participants were
seated for most of their visit but walked from each test station to the
next. Although most subjects received the complete battery of tests,
the order of test administration was randomly assigned. Heart Study
visits were scheduled in the morning for those in the FOS and in the
early afternoon for the FHS cohort.
Case Patients and Control Subjects
All subjects with an antecedent diagnosis of CHF who attended
one or more of the three specified examinations were identified.
Ambulatory ECG recordings were available from 52 individuals.
From the remainder of the FHS and FOS participants, we selected an
equal number of control subjects matched for sex and age (±6 months).
Those selected as control subjects had no documented history of
coronary heart disease; CHF; hypertension; atrial
arrhythmias; implanted cardiac pacemakers; or prior use of
cardiac glycosides or antiarrhythmic, anti-ischemic, or
antihypertensive medications.
We analyzed the first available recording for each of the 104 selected subjects (a few of those enrolled in the FHS had been recorded twice). The initial 2 hours of each of these 104 ECG recordings was processed in a fully automated manner to derive heart rate time series, time- and frequency-domain HRV measures, and nonlinear HRV measures. We excluded recordings that documented atrial fibrillation (from 12 subjects with CHF and 2 control subjects) and those that were technically inadequate (from 12 subjects with CHF and 9 control subjects) because of signal loss or insufficient duration.
Derivation of Heart Rate Time Series
Using a commercially available version of our Aristotle
arrhythmia analysis software,13 we
obtained a beat annotation file (a list of the type and time of
occurrence of each beat) for each ECG recording. Beats were
automatically detected, and beat types were automatically determined.
Using criteria based on timing and QRS morphology, Aristotle labels
each detected beat as normal, ventricular ectopic,
supraventricular ectopic, or unknown and identifies the
location of the R-wave peak (with a resolution of 8 ms in the
commercially available version used in this study). Although the
software may be expected to make a small number of errors with respect
to beat timing and type within a large and diverse data set, we did not
attempt to identify or correct these errors; rather, we attempted to
develop HRV measures that are robust with respect to the presence of
such errors.
From the beat annotation file, we selected only those beats that Aristotle had labeled as normal and determined the intervals, NN(i), between those beats. When ectopic beats were present or when noise or loss of signal prevented detection of beats, the NN time series included intervals between nonconsecutive normal beats. These intervals were identified as outliers and rejected. We used the following strategy to identify additional outliers.
1. For each set of five contiguous NN intervals, NN(i-2), NN(i-1), NN(i), NN(i+1), NN(i+2), a local mean, NNmean(i), can be computed excluding the central interval: NNmean(i)=[NN(i-2)+NN(i-1)+NN(i+1)+NN(i+2)]/4.
2. The central interval, NN(i), is considered to be an outlier unless it lies within 20% of NNmean(i), ie, unless 0.8xNNmean(i)<NN(i)<1.2xNNmean(i). This test was applied to each interval in the NN time series. All outliers identified in this way were rejected. From the remaining intervals, we constructed an instantaneous heart rate time series, HR(i): HR(i)=1/NN(i).
Segmentation of the Heart Rate Time Series
For each recording, we partitioned the heart rate time
series into segments, each containing data corresponding to a 15-minute
portion of the recording. The segments were constructed so that
each segment after the first overlapped the final half of the previous
segment. The number of heart rate values in each segment varied,
because heart rate is not constant. We measured the number and the
total duration of the interbeat intervals in each segment that were
rejected as outliers. If >20% of the intervals or of the duration of
any segment had been rejected, we excluded the segment from further
analysis. For each remaining segment, we calculated three raw
time-domain statistics [the mean, SD, and coefficient of variation of
HR(i)], five raw frequency-domain statistics (described below), and
one raw nonlinear statistic (ApEn, also described below). As noted
below, we also used DFA to characterize each recording; unlike
the other statistics described here, the DFA calculations were applied
to the entire recording and not separately to each segment.
Derivation of Time- and Frequency-Domain Measures
Both NN(i) and HR(i) may be regarded as samples of continuous
functions obtained at irregular intervals (ie, at the times of
occurrence of the normal beats that terminate each NN interval). To use
standard techniques for power spectral analysis, we resampled
the heart rate time series at uniform 500-ms intervals, using linear
interpolation between successive observations to fill in gaps as
needed. Using standard fast Fourier transform methods, we derived power
spectral density estimates of each 15-minute segment of these resampled
time series (after subtracting the mean and removing any linear trend
in each segment). From each such spectrum, we obtained the five raw
frequency-domain statistics: the estimated power in each of three bands
(VLF, LF, and HF), the TP over all three bands, and the scaling
exponent, ß (the slope of a line fit by a least-squares criterion to
a log-log plot of power versus frequency for frequencies between 0.001
and 0.1 Hz; this parameter characterizes the power-law
scaling of the spectrum if it is of the form
P=1/fß).14
We characterized each recording in the time domain by the mean values of each of the three raw time-domain statistics. Because the mean values of the five raw frequency-domain statistics were not normally distributed across the study population, logarithmic transformations of these statistics were used to characterize each recording in the frequency domain.
Derivation of Nonlinear Measures
Detrended Fluctuation Analysis
DFA quantifies the presence or absence of long-range (fractal)
correlations. This technique is a modification of root-mean-square
analysis of random walks applied to nonstationary
("real-world") data. The details of this algorithm have been
reported elsewhere.8 15 Unlike the other statistics used
in this study, which are based on the segmented heart rate time series
HR(i), the DFA index is based on the unsegmented NN interval series
NN(i). Briefly, the DFA computation involves the following steps.
First, the NN time series (of total length N) is integrated,
to yield
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). The y coordinates of the straight line
segments are denoted by yn(k). Next we detrend
the integrated time series, y(k), by subtracting the local
trend, yn(k), in each window. The
root-mean-square fluctuation of this integrated and detrended series is
calculated as
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, representing the slope of the line relating
log F(n) to log n.
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A continuous DFA index was then used to summarize these
analyses. In a previous study, the DFA scaling analysis
was performed on one group of CHF patients as well as one normal
control group.8 15 It was found that there is a region of
scaling behavior over which normal (healthy) cardiac control
operates.8 15 We used data sets from this prior study as
"training sets" and estimated the probability that each
individual heartbeat time series in the present study was operating
in that normal region. This probability was called the DFA index,
ranging between 0 and 1, with 1 indicating perfectly normal scaling
behavior (Fig 1
).
Approximate Entropy
ApEn is a "regularity statistic" that quantifies the
unpredictability of fluctuations in a time series such as
HR(i).5 7 Intuitively, one may reason that the presence of
repetitive patterns of fluctuation in a time series renders it more
predictable than a time series in which such patterns are absent. ApEn
reflects the likelihood that "similar" patterns of observations
will not be followed by additional "similar" observations. A time
series containing many repetitive patterns has a relatively small ApEn;
a less predictable (ie, more complex) process has a higher ApEn.
The algorithm for computing ApEn has been published
elsewhere5 7 16 ; here, we provide a brief summary of the
calculations, as applied to the HR(i) time series. Given a sequence
SN, consisting of N instantaneous heart rate measurements
HR(1), HR(2), ..., HR(N), we must choose values for two input
parameters, m and r, to compute the ApEn of the sequence,
ApEn(m,r,N). The first of these parameters, m, specifies
the pattern length, and the second, r, defines the criterion of
similarity. We denote a subsequence (or pattern) of m heart rate
measurements, beginning at measurement i within
SN, by the vector pm(i). Two patterns,
pm(i) and pm(j), are similar if the difference
between any pair of corresponding measurements in the patterns is less
than r, ie, if |HR(i+k)-HR(j+k)|<r, 0
k<m. Now consider the set
Pm of all patterns of length m [ie, pm(1),
pm(2), ..., pm(N-m+1)], within
SN. We may now define
Cim(r)=nim(r)/(N-m+1), where
nim(r) is the number of patterns in Pm that are
similar to pm(i) (given the similarity criterion r). The
quantity Cim(r) is the fraction of patterns of length m
that resemble the pattern of the same length that begins at interval
i. We can calculate Cim(r) for each pattern in
Pm, and we define Cm(r) as the mean of these
Cim(r) values. The quantity Cm(r) expresses the
prevalence of repetitive patterns of length m in SN.
Finally, we define the ApEn of SN, for patterns of length m
and similarity criterion r, as
ApEn(m,r,N)=ln[Cm(r)/Cm+1(r)], ie, as the
natural logarithm of the relative prevalence of repetitive patterns of
length m compared with those of length m+1.
Thus, if we find similar patterns in a heart rate time series, ApEn estimates the logarithmic likelihood that the next intervals after each of the patterns will differ (ie, that the similarity of the patterns is mere coincidence and lacks predictive value). Smaller values of ApEn imply a greater likelihood that similar patterns of measurements will be followed by additional similar measurements. If the time series is highly irregular, the occurrence of similar patterns will not be predictive for the following measurements, and ApEn will be relatively large.
In this study, we measured ApEn for each of the 15-minute segments of
the heart rate time series. In segments with missing data (because of
ectopy or noise), the measurements before and after each interval of
missing data were treated as if they had been adjacent. As described
above, segments were excluded if >20% of their intervals or duration
were rejected; in addition, for calculation of ApEn only, segments that
failed a stationarity test (
7%) were also excluded. (To assess the
stationarity of heart rate in each segment, we computed the mean heart
rate for each nonoverlapping subsequence of 100 measurements and
calculated 
100, the SD of these 100-beat mean heart
rates. The segment passed the stationarity test if 100
was <90% of the SD of heart rate measurements for the entire
segment.) In previous work,5 7 16 we explored various
values for m, the pattern length, and r, the similarity criterion, and
(on the basis of separability of similar populations of subjects not
included in the present study) we chose m=2 and r=4.75 bpm
(corresponding to 40% of the mean SD for all nonexcluded segments of
all recordings in the study). Because the number of beats was
not the same in each segment, n varied. The ApEn values obtained for
each segment were averaged to obtain a mean value of ApEn to
characterize each recording.
Statistical Methods
Using a multivariate regression model adjusting
for sex and age (in 5-year age groups), we compared the distributions
of the HRV statistics (two mean time-domain statistics, the logarithms
of the three mean frequency-domain statistics, the mean ApEn statistic,
and the DFA index) between the subjects with CHF and the control
subjects.17 Because five of the statistics (SD of heart
rate, coefficient of variation of heart rate, TP, VLF power, and LF
power) were highly correlated with one another (r=.72 to
.95), the coefficient of variation of heart rate, TP, and LF power were
excluded from the regression model, which examined all of the remaining
statistics together as a function of sex, age, and CHF. Using Cox
proportional-hazards regression models, we examined the relations of
each of the HRV statistics with overall survival after the
recording of the ambulatory ECG.18 Survival
estimates were computed by Kaplan-Meier methods. Continuous measures
are summarized as mean±SD. A value of P.05 was required
for statistical significance. All multivariable modeling was
performed with the SAS System (SAS Institute).
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Results |
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The baseline characteristics of the study sample are shown in Table 2
. The mean age of the study subjects was
71.7±8.1 years (range, 52 to 87 years); 43% of those with heart
failure and 44% of the control subjects were women. The average time
since diagnosis of heart failure was 6.9±6.2 years, with a median of
4.7 years and a range of 3 months to 26 years.
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Table 3 presents the average values, by group, of
the HRV measures we derived. After adjustment for age and sex, there
were statistically significant differences between the CHF case
patients and control subjects. The SD of the heart rate, VLF power, LF
power, and the ratio of LF to HF power were lower in the CHF case
patients than in the control subjects. The DFA index, derived from the
scaling exponent , was also lower in the CHF case patients,
indicating a lower amount of long-range correlations compared with the
control subjects.
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Over a mean follow-up period of 1.9 years, 12 deaths occurred (9 among
the heart failure case patients and 3 among the control subjects).
Causes of death among the CHF case patients included sudden death in 1,
coronary heart disease in 1, cerebrovascular accident in 1,
other cerebrovascular disease in another, cancer in 1, and other causes
in 4; there were 2 cancer deaths among the control subjects and 1 death
from other causes. Using several separate Cox proportional-hazards
regression models, the diagnosis of CHF, the SD of the heart rate, the
coefficient of variation of heart rate, the heart rate TP, VLF power,
LF power, and the DFA index were predictors of survival. After
stratification by the diagnosis of CHF, only coefficient of variation
and the DFA index were significant predictors, whereas SD and LF power
were of borderline significance (Table 4). When
coefficient of variation or SD was combined with the DFA index in
simple two-variable models, these variables were marginally
significant predictors of survival after adjustment for the diagnosis
of CHF (Table 4). This is illustrated in Fig 2, which
depicts Kaplan-Meier estimates of survival. We determined the median
values of SD and of the DFA index over the entire study population and
used these values to establish "high" (median or greater) or
"low" (submedian) ranges for these variables. As apparent
from the graph, subjects with a low SD and a low DFA index (ie, mostly
CHF case patients) did very poorly. Individuals who were at or above
the median for both variables (ie, control subjects) did very well.
The others had an intermediate prognosis.
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Discussion |
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The major findings of the study are that (1) HRV analysis of ambulatory ECG recordings, based on fully automated methods, is capable of predicting outcomes in a small sample of CHF case patients and control subjects; (2) this investigation confirms the importance of time-domain measures (the coefficient of variation and SD of the heart rate) as prognostic variables; (3) patients with CHF show a breakdown of the physiological long-range (or fractal) correlations of heart rate, as quantified by DFA; and (4) the DFA index is a significant prognostic indicator that may complement traditional HRV measures in assessing risk.
Assessment of HRV
There has recently been an explosion of interest in quantification
of HRV.1 2 3 7 19 20 21 22 23 24 Measurements of HRV can be classified
into two general classes: "moment" statistics and dynamic
statistics.3 7 24
The first class of measures, time-domain or moment statistics (eg,
mean, SD, and coefficient of variation of heart rate), is based on
linear analysis. These measures appear frequently in the
literature. They are relatively straightforward to obtain (although
careful attention to erroneous or missing data is necessary, because
they tend to be highly sensitive to isolated outliers). Such measures
do not depend on the order of the observations, however, and may
therefore obscure significant information about heart rate dynamics.
For example, two heart rate time series may have nearly identical mean
rates and SDs but very different dynamics.4 16 Of note, in
the present study, the mean heart rate for case patients and
control subjects was not significantly different. Previous work has
documented the very poor prognosis of CHF in participants in the FHS,
with 1-year mortality rates after diagnosis of 43% in men and 36% in
women.25 Only those subjects who survived to the next
examination cycle (
2 years later) were available for ambulatory ECG
recordings. Thus, the CHF case patients included in these
analyses (who had survived an average of 6.9 years since onset
of CHF) were probably healthier than hospitalized CHF patients used in
other HRV studies, as evidenced by the absence of the anticipated
relative sinus tachycardia in CHF case patients (mean heart
rate, 75 bpm).
Dynamic statistics, a class that includes both frequency-domain measures (eg, power spectral density estimates derived from Fourier ["all-zeroes"] or autoregressive ["all-poles"] analyses) and new measures derived from nonlinear dynamics (chaos theory and fractal mathematics), do preserve information about the order of observations. The Fourier transform provides a useful representation of the component frequencies of heart rate time series, including HF heart rate oscillations associated with respiration and lower-frequency oscillations associated in part with baroreflex control.3 19 24 Nonstationarity in typical heart rate time series severely limits the range of frequencies that can be studied by conventional frequency-domain analytic methods. Furthermore, frequency-domain analysis, while retaining information relating to ordering of observations, is still based on linear models and may conceal the details of interactions between mechanisms. (For example, a respiration-mediated change in heart rate may stimulate a response from another mechanism. In the time series, this phenomenon may be readily observable as a repetitive pattern of fluctuation, but in the frequency domain, it may be indistinguishable from uncorrelated fluctuations at different frequencies. Neither moment statistics nor frequency-domain measures reveal the presence or absence of such features in the time series.)
Nonlinear and Fractal Dynamics of the Heartbeat
Nonlinear and fractal dynamic analysis offers the prospect
of revealing these details by providing direct measures of
complexity5 7 and long-range
correlations.8 26 We studied two indices of HRV derived
from nonlinear and fractal dynamics: ApEn (a measure of complexity) and
DFA (a measure of the presence of long-term fractal correlations).
Reduced sinus rhythm heart rate complexity has been reported with a variety of pathological processes,7 with bed rest and deconditioning,16 and with aging.5 The present study did not indicate that ApEn was an independent predictor of survival, however. This result may be a consequence of the nonstationarity of the data sets and of intrinsic limitations of the measure in the context of very low overall variability, as in some case patients with CHF.
Healthy heart rate fluctuations show a complex type of variability that we and others have shown to be fractal, that is to say, having "self-similar" fluctuations on time scales ranging from seconds to hours.27 Furthermore, this fractal complexity generates long-range power-law correlations. The presence of such long-range fractal order indicates that fluctuations in heart rate are affected not only by the most recent value but also by much more remote events, in other words, a "memory" effect.27 In a previous study, we showed that a breakdown of these fractal scaling properties is noted with CHF and other disease conditions.8 The present study confirms the utility of DFA measurement and suggests that it may add prognostic information to that obtained from conventional SD and spectral measures of HRV.
Automated Analysis of HRV
As typically applied, HRV analysis requires interactive
data "massaging" (with the risk of introducing bias) to clean up
the heart rate time series sufficiently to obtain standard measures.
Often the interpretation of results requires additional subjective
decisions (eg, to determine which portion of the heart rate spectrum
represents the contribution of respiration to HRV). These
manipulations represent virtually the entire incremental cost
of HRV analysis (beyond that of conventional long-term ECG
analysis) while provoking skepticism with respect to the
objectivity of the procedure.
Our method requires neither nonstandard instrumentation or recording protocols nor tedious, expensive, and potentially bias-inducing interactive data preparation. Its success results from careful design of robust methods for obtaining measures of HRV, including nonlinear and fractal dynamic measures that complement standard moment and frequency-domain statistics. The computational demands of our analysis, though significantly greater than those of standard analyses restricted to derivation of moment statistics, remain quite modest relative to those of conventional long-term ECG analysis. These encouraging results from a small sample with limited follow-up should be confirmed in a larger study.
Reliable measures of HRV should not be inordinately affected by isolated errors in the RR interval sequence resulting from noise, ectopy, or missing data. A comprehensive analysis of HRV must also look considerably beyond moment statistics to take account of the order of observations, of the presence or absence of repetitive patterns of heart rate fluctuation as markers of the dynamic processes that control heart rate, and of their interactions. Significantly, we have shown that a totally automated method for reliable and comprehensive HRV analysis can predict outcomes in a group of prospectively identified CHF case patients and control subjects.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This study was supported by grants from the G. Harold and Leila Y. Mathers Charitable Foundation and the National Aeronautics and Space Administration and by National Institutes of Health/National Heart, Lung, and Blood Institute contract N01-HC-38038. Dr Peng is a recipient of a National Institute of Mental Health FIRST Award (MH-54081). Dr Ho was supported in part by a Fellowship from the Pfizer Clinical Investigator Training Program.
Received November 21, 1996; revision received February 12, 1997; accepted March 2, 1997.
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