(Circulation. 1996;93:1043-1065.)
© 1996 American Heart Association, Inc.
Articles |
Heart Rate Variability
Standards of Measurement, Physiological Interpretation, and Clinical Use
Correspondence to Marek Malik, PhD, MD, Chairman, Writing Committee of the Task Force, Department of Cardiological Sciences, St George's Hospital Medical School, Cranmer Terrace, London SW17 0RE, UK.
Key Words: heart rate • electrocardiography • computers • nervous system, autonomic • risk factors
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Introduction |
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 Top Introduction Background Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
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The last two decades have witnessed the recognition of a significant relationship between the autonomic nervous system and cardiovascular mortality, including sudden cardiac death.1 2 3 4 Experimental evidence for an association between propensity for lethal arrhythmias and signs of either increased sympathetic or reduced vagal activity has spurred efforts for the development of quantitative markers of autonomic activity.
HRV represents one of the most promising such markers. The apparently easy derivation of this measure has popularized its use. As many commercial devices now provide an automated measurement of HRV, the cardiologist has been provided with a seemingly simple tool for both research and clinical studies.5 However, the significance and meaning of the many different measures of HRV are more complex than generally appreciated, and there is a potential for incorrect conclusions and for excessive or unfounded extrapolations.
Recognition of these problems led the European Society of Cardiology and the North American Society of Pacing and Electrophysiology to constitute a Task Force charged with the responsibility of developing appropriate standards. The specific goals of this Task Force were to (1) standardize nomenclature and develop definitions of terms, (2) specify standard methods of measurement, (3) define physiological and pathophysiological correlates, (4) describe currently appropriate clinical applications, and (5) identify areas for future research.
To achieve these goals, the members of the Task Force were drawn from the fields of mathematics, engineering, physiology, and clinical medicine. The standards and proposals offered in this text should not limit further development but should allow appropriate comparisons, promote circumspect interpretations, and lead to further progress in the field.
The phenomenon that is the focus of this report is the oscillation in the interval between consecutive heartbeats as well as the oscillations between consecutive instantaneous heart rates. "Heart rate variability" has become the conventionally accepted term to describe variations of both instantaneous heart rate and RR intervals. To describe oscillation in consecutive cardiac cycles, other terms have been used in the literature, for example, cycle length variability, heart period variability, RR variability, and RR interval tachogram, and they more appropriately emphasize the fact that it is the interval between consecutive beats that is being analyzed rather than the heart rate per se. However, these terms have not gained as wide acceptance as HRV; thus, we will use the term HRV in this document.
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Background |
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TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
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The clinical relevance of HRV was first appreciated in 1965 when Hon and Lee6 noted that fetal distress was preceded by alterations in interbeat intervals before any appreciable change occurred in heart rate itself. Twenty years ago, Sayers7 and others8 9 10 focused attention on the existence of physiological rhythms imbedded in the beat-to-beat heart rate signal. During the 1970s, Ewing et al11 devised a number of simple bedside tests of short-term RR differences to detect autonomic neuropathy in diabetic patients. The association of higher risk of postinfarction mortality with reduced HRV was first shown by Wolf et al12 in 1977. In 1981, Akselrod et al13 introduced power spectral analysis of heart rate fluctuations to quantitatively evaluate beat-to-beat cardiovascular control.
These frequency domain analyses contributed to the understanding of autonomic background of RR interval fluctuations in the heart rate record.14 15 The clinical importance of HRV became appreciated in the late 1980s, when it was confirmed that HRV was a strong and independent predictor of mortality after an acute myocardial infarction.16 17 18 With the availability of new, digital, high-frequency, 24-hour, multichannel ECG recorders, HRV has the potential to provide additional valuable insight into physiological and pathological conditions and to enhance risk stratification.
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Measurement of HRV |
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TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
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Time Domain Methods
The variations in heart rate may be evaluated by a number of methods. Perhaps the simplest to perform are the time domain measures. In these methods, either the heart rate at any point in time or the intervals between successive normal complexes are determined. In a continuous ECG record, each QRS complex is detected, and the so-called normal-to-normal (NN) intervals (that is, all intervals between adjacent QRS complexes resulting from sinus node depolarizations) or the instantaneous heart rate is determined. Simple time domain variables that can be calculated include the mean NN interval, the mean heart rate, the difference between the longest and shortest NN interval, the difference between night and day heart rate, and so forth. Other time domain measurements that can be used are variations in instantaneous heart rate secondary to respiration, tilt, Valsalva maneuver, or phenylephrine infusion. These differences can be described as either differences in heart rate or cycle length.
Statistical Methods
From a series of
instantaneous heart rates or cycle intervals,
particularly those recorded over longer periods, traditionally 24
hours, more complex statistical time domain measures can be calculated.
These may be divided into two classes: (1) those derived from direct
measurements of the NN intervals or instantaneous heart rate and (2)
those derived from the differences between NN intervals. These
variables may be derived from analysis of the total ECG
recording or may be calculated using smaller segments of the
recording period. The latter method allows comparison of HRV to
be made during varying activities, for example, rest, sleep, and so
on.
The simplest variable to calculate is the standard deviation of the NN intervals (SDNN), that is, the square root of variance. Since variance is mathematically equal to total power of spectral analysis, SDNN reflects all the cyclic components responsible for variability in the period of recording. In many studies SDNN is calculated over a 24-hour period and thus encompasses short-term HF variations as well as the lowest-frequency components seen in a 24-hour period. As the period of monitoring decreases, SDNN estimates shorter and shorter cycle lengths. It also should be noted that the total variance of HRV increases with the length of analyzed recording.19 Thus, on arbitrarily selected ECGs, SDNN is not a well-defined statistical quantity because of its dependence on the length of recording period. In practice, it is inappropriate to compare SDNN measures obtained from recordings of different durations. On the contrary, durations of the recordings used to determine SDNN values (and similarly other HRV measures) should be standardized. As discussed further in this document, short-term 5-minute recordings and nominal 24-hour long-term recordings appear to be appropriate options.
Other commonly used statistical variables calculated from segments of the total monitoring period include SDANN, the standard deviation of the average NN intervals calculated over short periods, usually 5 minutes, which is an estimate of the changes in heart rate due to cycles longer than 5 minutes, and the SDNN index, the mean of the 5-minute standard deviations of NN intervals calculated over 24 hours, which measures the variability due to cycles shorter than 5 minutes.
The most commonly used measures derived from interval
differences
include RMSSD, the square root of the mean squared differences of
successive NN intervals, NN50, the number of interval differences of
successive NN intervals greater than 50 ms, and pNN50, the proportion
derived by dividing NN50 by the total number of NN intervals. All of
these measurements of short-term variation estimate
high-frequency variations in heart rate and thus are highly
correlated (Fig 1
).
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Geometric Methods
The series of NN intervals also can be converted into a geometric
pattern such as the sample density distribution of NN interval
durations, sample density distribution of differences between adjacent
NN intervals, Lorenz plot of NN or RR intervals, and so forth, and a
simple formula is used that judges the variability on the basis of the
geometric and/or graphics properties of the resulting pattern. Three
general approaches are used in geometric methods: (1) a basic
measurement of the geometric pattern (for example, the width of the
distribution histogram at the specified level) is converted into the
measure of HRV, (2) the geometric pattern is interpolated by a
mathematically defined shape (for example, approximation of the
distribution histogram by a triangle or approximation of the
differential histogram by an exponential curve) and then the
parameters of this mathematical shape are used, and (3) the
geometric shape is classified into several pattern-based categories
that represent different classes of HRV (for example, elliptic,
linear, and triangular shapes of Lorenz plots). Most geometric methods
require the RR (or NN) interval sequence to be measured on or converted
to a discrete scale that is not too fine or too coarse and permits the
construction of smoothed histograms. Most experience has been obtained
with the length of the bins of approximately 8 ms (precisely 7.8125
ms=1/128 seconds), which corresponds to the precision of current
commercial equipment.
The HRV triangular index measurement is the
integral of the density
distribution (that is, the number of all NN intervals) divided by the
maximum of the density distribution. Using a measurement of NN
intervals on a discrete scale, the measure is approximated by the value
(total number of NN intervals)/(number of NN intervals in the modal
bin), which is dependent on the length of the bin, that is, on the
precision of the discrete scale of measurement. Thus, if the discrete
approximation of the measure is used with NN interval measurement on a
scale different from the most frequent sampling of 128 Hz, the size of
the bins should be quoted. The triangular interpolation of NN interval
histogram (TINN) is the baseline width of the distribution measured as
a base of a triangle approximating the NN interval distribution (the
minimum square difference is used to find such a triangle). Details of
computing HRV triangular index and TINN are shown in Fig 2.
Both these measures express overall HRV measured over
24 hours and are more influenced by the lower than by the higher
frequencies.17 Other geometric methods are still in the
phase of exploration and explanation.20 21
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0+
(D(t)-q(t))2 dt is the minimum among all selections of all
values N and M. The TINN measure is expressed in
milliseconds and given by the formula TINN=M-N. Also see
Table 1The major advantage of the geometric methods lies in their relative insensitivity to the analytical quality of the series of NN intervals.22 The major disadvantage of the geometric methods is the need for a reasonable number of NN intervals to construct the geometric pattern. In practice, recordings of at least 20 minutes (but preferably 24 hours) should be used to ensure the correct performance of the geometric methods; that is, the current geometric methods are inappropriate to assess short-term changes in HRV.
Summary and Recommendations
The
variety of time domain measures of HRV is summarized in Table
1. Since many of the measures correlate closely with
others, the following four measures are recommended for time domain HRV
assessment (1) SDNN (estimate of overall HRV), (2) HRV triangular index
(estimate of overall HRV), (3) SDANN (estimate of long-term
components of HRV), and (4) RMSSD (estimate of short-term
components of HRV).
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Two estimates of the overall HRV are recommended because the HRV triangular index permits only casual preprocessing of the ECG signal. The RMSSD method is preferred to pNN50 and NN50 because it has better statistical properties.
The methods expressing overall HRV and its long- and short-term components cannot replace each other. The selection of method used should correspond to the aim of each particular study. Methods that might be recommended for clinical practices are summarized in "Clinical Use of HRV."
Distinction should be made between measures derived from direct measurements of NN intervals or instantaneous heart rate and from the differences between NN intervals.
It is inappropriate to compare time domain measures, especially those expressing overall HRV, obtained from recordings of different durations.
Other practical recommendations are listed in "Recording Requirements," together with suggestions related to the frequency analysis of HRV.
Frequency Domain Methods
Various spectral
methods23 for the analysis
of the tachogram have been applied since the late 1960s. Power spectral
density (PSD) analysis provides the basic information of how
power (variance) distributes as a function of frequency. Independent of
the method used, only an estimate of the true PSD of the signal can be
obtained by proper mathematical algorithms.
Methods for the calculation of PSD may be generally classified as nonparametric and parametric. In most instances, both methods provide comparable results. The advantages of the nonparametric methods are (1) the simplicity of the algorithm used (fast Fourier transform [FFT] in most of the cases) and (2) the high processing speed, while the advantages of parametric methods are (1) smoother spectral components that can be distinguished independent of preselected frequency bands, (2) easy postprocessing of the spectrum with an automatic calculation of low- and high-frequency power components with an easy identification of the central frequency of each component, and (3) an accurate estimation of PSD even on a small number of samples on which the signal is supposed to maintain stationarity. The basic disadvantage of parametric methods is the need of verification of the suitability of the chosen model and of its complexity (that is, the order of the model).
Spectral Components
Short-term recordings. Three main spectral
components are distinguished in a spectrum calculated from
short-term recordings of 2 to 5
minutes7 10 13 15 24 :
VLF, LF, and HF components. The
distribution of the power and the central frequency of LF and HF are
not fixed but may vary in relation to changes in autonomic modulations
of heart period.15 24 25 The
physiological explanation of the VLF component is
much less defined, and the existence of a specific
physiological process attributable to these heart
period changes might even be questioned. The nonharmonic component,
which does not have coherent properties and is affected by algorithms
of baseline or trend removal, is commonly accepted as a major
constituent of VLF. Thus, VLF assessed from short-term
recordings (
5 minutes) is a dubious measure and should be
avoided when the PSD of short-term ECGs is interpreted.
The measurement
of VLF, LF, and HF power components is usually made in
absolute values of power (milliseconds squared). LF and HF may also be
measured in normalized units,15 24 which represent
the relative value of each power component in proportion to the total
power minus the VLF component. The representation of LF and HF
in normalized units emphasizes the controlled and balanced behavior of
the two branches of the autonomic nervous system. Moreover, the
normalization tends to minimize the effect of the changes in total
power on the values of LF and HF components (Fig 3).
Nevertheless, normalized units should always be quoted with absolute
values of the LF and HF power in order to describe completely the
distribution of power in spectral components.
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Long-term recordings. Spectral analysis
also may be used to analyze the sequence of NN intervals of the
entire 24-hour period. The result then includes a ULF component, in
addition to VLF, LF, and HF components. The slope of the 24-hour
spectrum also can be assessed on a log-log scale by linear fitting
the spectral values. Table 2 lists selected frequency
domain measures.
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The problem of "stationarity" is
frequently discussed with
long-term recordings. If mechanisms responsible for heart
period modulations of a certain frequency remain unchanged during the
whole period of recording, the corresponding frequency
component of HRV may be used as a measure of these modulations. If the
modulations are not stable, the interpretation of the results of
frequency analysis is less well defined. In particular,
physiological mechanisms of heart period
modulations responsible for LF and HF power components cannot be
considered stationary during the 24-hour period.25 Thus,
spectral analysis performed on the entire 24-hour period as
well as spectral results obtained from shorter segments (5 minutes)
averaged over the entire 24-hour period (the LF and HF results of these
two computations are not different26 27 ) provide
averages
of the modulations attributable to the LF and HF components (Fig
4). Such averages obscure the detailed information about
autonomic modulation of RR intervals that is available in shorter
recordings.25 It should be remembered that the
components of HRV provide measurement of the degree of autonomic
modulations rather than of the level of autonomic tone,28
and averages of modulations do not represent an averaged level
of tone.
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measure of HRV.
Technical Requirements and Recommendations
Because of the important differences in the interpretation of the
results, the spectral analyses of short-term and
long-term ECGs should always be strictly distinguished, as reported
in Table 2
.
The analyzed ECG signal should satisfy several requirements in order to obtain a reliable spectral estimation. Any departure from the following requirements may lead to unreproducible results that are difficult to interpret.
To attribute individual spectral components to well-defined physiological mechanisms, such mechanisms modulating the heart rate should not change during the recording. Transient physiological phenomena may perhaps be analyzed by specific methods that currently constitute a challenging research topic but are not yet ready to be used in applied research. To check the stability of the signal in terms of certain spectral components, traditional statistical tests may be used.29
The sampling rate must be properly chosen. A
low sampling rate may
produce a jitter in the estimation of the R-wave fiducial point, which
alters the spectrum considerably. The optimal range is 250 to 500 Hz or
perhaps even higher,30 while a lower sampling rate (in any
case 
100 Hz) may behave satisfactorily only if an algorithm of
interpolation (parabolic) is used to refine the R-wave fiducial
point.31 32
Baseline and trend removal (if used) may affect the lower components in the spectrum. It is advisable to check the frequency response of the filter or the behavior of the regression algorithm and to verify that the spectral components of interest are not significantly affected.
The choice of QRS fiducial point may be critical. It is necessary to use a well-tested algorithm (derivative plus threshold, template, correlation method) to locate a stable and noise-independent reference point.33 A fiducial point localized far within the QRS complex may also be influenced by varying ventricular conduction disturbances.
Ectopic beats, arrhythmic events, missing data, and noise effects may alter the estimation of the PSD of HRV. Proper interpolation (or linear regression or similar algorithms) on preceding/successive beats on the HRV signal or on its autocorrelation function may decrease this error. Preferentially, short-term recordings that are free of ectopy, missing data, and noise should be used. In some circumstances, however, acceptance of only ectopic-free, short-term recordings may introduce significant selection bias. In such cases, proper interpolation should be used and the possibility of the results being influenced by ectopy should be considered.34 The relative number and relative duration of RR intervals that were omitted and interpolated should also be quoted.
Algorithmic
Standards and Recommendations
The series of data subjected to spectral
analysis can be
obtained in different ways. A useful pictorial representation
of the data is the discrete event series (DES), that is, the plot of
RiRi-1 interval versus time (indicated at
Ri occurrence), which is an irregularly time-sampled
signal. Nevertheless, spectral analysis of the sequence of
instantaneous heart rates has also been used in many
studies.26
The spectrum of the HRV signal is generally
calculated either from the
RR interval tachogram (RR durations versus number of progressive beats;
see Fig 5a,b) or by interpolating the DES, thus
obtaining a continuous signal as a function of time, or by calculating
the spectrum of the counts–unitary pulses as a function of time
corresponding to each recognized QRS complex.35 Such a
choice may have implications on the morphology, the measurement units
of the spectra, and the measurement of the relevant spectral
parameters. To standardize the methods used, the use of RR
interval tachogram with the parametric method, or the use of
the regularly sampled interpolation of DES with the
nonparametric method may be suggested; nevertheless,
regularly sampled interpolation of DES is also suitable for
parametric methods. The sampling frequency of interpolation of
DES must be sufficiently high that the Nyquist frequency of the
spectrum is not within the frequency range of interest.
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Standards for
nonparametric methods (based on the FFT
algorithm) should include the values reported in Table 2, the
formula
of DES interpolation, the frequency of sampling the DES interpolation,
the number of samples used for the spectrum calculation, and the
spectral window used (Hann, Hamming, and triangular windows are most
frequently used).36 The method of calculating the power in
respect of the window also should be quoted. In addition to
requirements described in other parts of this document, each study
using the nonparametric spectral analysis of HRV
should quote all these parameters.
Standards for parametric methods
shall include the values
reported in Table 2, the type of the model used, the number of
samples,
the central frequency for each spectral component (LF and HF), and the
value of the model order (numbers of parameters).
Furthermore, statistical figures must be calculated in order to test
the reliability of the model. The prediction error whiteness test
(PEWT) provides information about the goodness of the fitting
model,37 while the optimal order test (OOT) checks the
suitability of the order of the model used.38 There are
different possibilities of performing OOT that include final prediction
error and Akaike information criteria. The following operative
criterion for choosing the order P of an autoregressive
model might be proposed: the order shall be in the range of 8 to 20,
fulfilling the PEWT test and complying with the OOT test
(P
min[OOT]).
Correlation and Differences Between Time and Frequency Domain
Measures
In the analysis of stationary short-term
recordings, more experience and theoretical knowledge exist on
physiological interpretation of the frequency
domain measures compared with the time domain measures derived from the
same recordings.
On the contrary, many time and frequency domain
variables measured
over the entire 24-hour period are strongly correlated with each other
(Table 3). These strong correlations exist because of
both mathematical and physiological relationships.
In addition, the physiological interpretation of
the spectral components calculated over 24 hours is difficult, namely
because of reasons mentioned above (see "Long-term
recordings"). Thus, unless special investigations are
performed that use the 24-hour HRV signal to extract information other
than the usual frequency components (for example, the log-log slope
of spectrogram), the results of the frequency-domain
analysis are equivalent to those of the time domain
analysis, which is easier to perform.
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Rhythm Pattern Analysis
As illustrated in Fig
6,39 the
time domain and spectral methods share limitations imposed by the
irregularity of the RR series. Clearly different profiles
analyzed by these techniques may give identical results. Trends
of decreasing or increasing cycle length are in reality not
symmetric40 41 as heart rate accelerations are
usually
followed by a faster decrease. In spectral results, this tends to
reduce the peak at the fundamental frequency and to enlarge its basis.
This leads to the idea of measuring blocks of RR intervals determined
by properties of the rhythm and investigating the relationship of such
blocks without considering the internal variability.
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Approaches derived
from the time domain and the frequency domain have
been proposed in order to reduce these difficulties. The interval
spectrum and spectrum of counts methods lead to equivalent results (Fig
6d) and are well suited to investigate the relationship between
HRV and
the variability of other physiological measures.
The interval spectrum is well adapted to link RR intervals to
variables defined on a beat-to-beat basis (blood pressure).
The spectrum of counts is preferable if RR intervals are related to a
continuous signal (respiration) or to the occurrence of special events
(arrhythmia).
The "peak-valley" procedures are based either on the detection of the summit and the nadir of oscillations42 43 or on the detection of trends of heart rate.44 The detection may be limited to short-term changes,42 but it can be extended to longer variations: second- and third-order peaks and troughs43 or stepwise increase of a sequence of consecutive increasing or decreasing cycles surrounded by opposite trends.44 The various oscillations can be characterized on the basis of the heart rate acceleration or slowing, the wavelength, and/or the amplitude. In a majority of short- to mid-term recordings, the results are correlated with frequency components of HRV.45 The correlations, however, tend to diminish as the wavelength of the oscillations and the recording duration increase. Complex demodulation uses the techniques of interpolation and detrending46 and provides the time resolution necessary to detect short-term heart rate changes as well as to describe the amplitude and phase of particular frequency components as functions of time.
Nonlinear Methods
Nonlinear phenomena are certainly involved
in the genesis of HRV.
They are determined by complex interactions of
hemodynamic,
electrophysiological, and humoral
variables as well as by the autonomic and central nervous
regulations. It has been speculated that analysis of HRV based
on the methods of nonlinear dynamics might elicit valuable information
for physiological interpretation of HRV and for the
assessment of the risk of sudden death. The parameters that
have been used to measure nonlinear properties of HRV include 1/f
scaling of Fourier spectra,47 19 H scaling exponent,
and
Coarse Graining Spectral Analysis (CGSA).48 For
data representation, Poincaré sections, low-dimension
attractor plots, singular value decomposition, and attractor
trajectories have been used. For other quantitative descriptions, the
D2 correlation dimension, Lyapunov exponents, and
Kolmogorov entropy have been used.49
Although in principle, these techniques have been shown to be powerful tools for characterization of various complex systems, no major breakthrough has yet been achieved by their application to biomedical data including HRV analysis. It is possible that integral complexity measures are not adequate to analyze biological systems and thus are too insensitive to detect the nonlinear perturbations of RR interval, which would be of physiological or practical importance. More encouraging results have been obtained using differential rather than integral complexity measures, for example, the scaling index method.50 51 However, no systematic study has been conducted to investigate large patient populations with the use of these methods.
At present, the nonlinear methods represent potential tools for HRV assessment. Standards are lacking, and the full scope of these methods cannot be assessed. Advances in technology and the interpretation of the results of nonlinear methods are needed before these methods are ready for physiological and clinical studies.
Stability and Reproducibility of HRV Measurement
Multiple
studies have demonstrated that short-term measures of
HRV rapidly return to baseline after transient perturbations induced by
such manipulations as mild exercise, administration of short-acting
vasodilators, and transient coronary occlusion. More powerful
stimuli, such as maximum exercise or administration of long-acting
drugs, may result in a much more prolonged interval before return to
control values.
There are far fewer data on the stability of long-term measures of HRV obtained from 24-hour ambulatory monitoring. Nonetheless, the limited data available suggest great stability of HRV measures derived from 24-hour ambulatory monitoring in both normal subjects52 53 and in the postinfarction54 and ventricular arrhythmia55 populations. There also exist some fragmentary data to suggest that stability of HRV measures may persist for periods of months and years. Because 24-hour indices appear to be stable and free of placebo effect, they may be ideal variables to assess intervention therapies.
Recording Requirements
ECG Signal
The fiducial
point recognized on the ECG tracing that identifies a
QRS complex may be based on the maximum or baricentrum of the complex,
on the determination of the maximum of an interpolating curve, or found
by matching with a template or other event markers. To localize the
fiducial point, voluntary standards for diagnostic ECG
equipment are satisfactory in terms of signal-to-noise ratio,
common mode rejection, bandwidth, and so forth.56 An
upper-band frequency cutoff substantially lower than that
established for diagnostic equipment (200 Hz) may create
a jitter in the recognition of the QRS complex fiducial point,
introducing an error of measured RR intervals. Similarly, limited
sampling rate induces an error in the HRV spectrum that increases with
frequency, thus affecting more high-frequency
components.31 An interpolation of the undersampled ECG
signal may decrease this error. With proper interpolation, even a
100-Hz sampling rate can be sufficient.32
When solid-state storage recorders are used, data compression techniques must be carefully considered in terms of both the effective sampling rate and the quality of reconstruction methods that may yield amplitude and phase distortion.57
Duration and
Circumstances of ECG Recording
In studies researching HRV, the
duration of recording is
dictated by the nature of each investigation. Standardization is needed
particularly in studies investigating the
physiological and clinical potential of HRV.
Frequency domain methods should be preferred to the time domain methods when short-term recordings are investigated. The recording should last for at least 10 times the wavelength of the lower frequency bound of the investigated component, and, in order to ensure the stability of the signal, should not be substantially extended. Thus, recording of approximately 1 minute is needed to assess the HF components of HRV, while approximately 2 minutes are needed to address the LF component. To standardize different studies investigating short-term HRV, 5-minute recordings of a stationary system are preferred unless the nature of the study dictates another design.
Averaging of spectral components obtained from sequential periods of time is able to minimize the error imposed by the analysis of very short segments. Nevertheless, if the nature and degree of physiological heart period modulations changes from one short segment of the recording to another, the physiological interpretation of such averaged spectral components suffers from the same intrinsic problems as that of the spectral analysis of long-term recordings and warrants further elucidation. A display of stacked series of sequential power spectra (for example, over 20 minutes) may help confirm steady state conditions for a given physiological state.
Although the time domain methods, especially the SDNN and RMSSD methods, can be used to investigate recordings of short durations, the frequency methods are usually able to provide results that are more easily interpretable in terms of physiological regulations. In general, the time domain methods are ideal for the analysis of long-term recordings (the lower stability of heart rate modulations during long-term recordings makes the results of frequency methods less easily interpretable). The experience shows that a substantial part of the long-term HRV value is contributed by the day-night differences. Thus, the long-term recording analyzed by the time domain methods should contain at least 18 hours of analyzable ECG data that include the whole night.
Little is known about the effects of the environment (type and nature of physical activity and emotional circumstances) during long-term ECG recordings. For some experimental designs, environmental variables should be controlled and in each study, the character of the environment should always be described. The design of investigations also should ensure that the recording environment of individual subjects is similar. In physiological studies comparing HRV in different well-defined groups, the differences between underlying heart rate also should be properly acknowledged.
Editing of the RR
Interval Sequence
The errors imposed by the imprecision of the NN
interval sequence
are known to affect substantially the results of statistical time
domain and all frequency domain methods. It is known that casual
editing of the RR interval data is sufficient for the approximate
assessment of total HRV by the geometric methods, but it is not known
how precise the editing should be to ensure correct results from other
methods. Thus, when the statistical time domain and/or frequency domain
methods are used, the manual editing of the RR data should be performed
to a very high standard, ensuring correct identification and
classification of every QRS complex. Automatic "filters" that
exclude some intervals from the original RR sequence (for example,
those differing by more than 20% from the previous interval) should
not replace manual editing because they are known to behave
unsatisfactorily and to have undesirable effects leading potentially to
errors.58
Suggestions for Standardization of
Commercial
Equipment
Standard measurement of HRV. Commercial
equipment designed to
analyze short-term HRV should incorporate
nonparametric and preferably also parametric
spectral analysis. To minimize the possible confusion imposed
by reporting the components of the cardiac beat–based
analysis in time frequency components, the analysis
based on regular sampling of the tachograms should be offered in all
cases. The nonparametric spectral analysis
should use at least 512 but preferably 1024 points for 5-minute
recordings.
Equipment designed to analyze HRV in long-term recordings should implement time domain methods, including all four standard measures (SDNN, SDANN, RMSSD, and HRV triangular index). In addition to other options, the frequency analysis should be performed in 5-minute segments (using the same precision as with the analysis of short-term ECGs). When spectral analysis of the total nominal 24-hour record is performed to compute the whole range of HF, LF, VLF, and ULF components, the analysis should be performed with a similar precision of periodogram sampling as suggested for the short-term analysis, for example, using 218 points.
The strategy of
obtaining the data for the HRV analysis should
copy the design outlined in Fig 7.
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Precision and testing of commercial equipment. To ensure the quality of different equipment involved in HRV analysis and to find an appropriate balance between the precision essential to research and clinical studies and the cost of the equipment required, independent testing of all equipment is needed. Because the potential errors of the HRV assessment include inaccuracies in the identification of fiducial points of QRS complexes, the testing should include all the recording, replay, and analysis phases. Thus, it seems ideal to test various equipment with signals (that is, computer simulated) of known HRV properties rather than with existing databases of already digitized ECGs. When commercial equipment is used in studies investigating physiological and clinical aspects of HRV, independent tests of the equipment used should always be required. A possible strategy for testing of commercial equipment is proposed in "Appendix B." Voluntary industrial standards should be developed adopting this or similar strategy.
Summary and
Recommendations
To minimize the errors caused by improperly designed
or
incorrectly used techniques, the following points are recommended.
The ECG equipment used should satisfy the current voluntary industrial standards in terms of signal-to-noise ratio, common mode rejection, bandwidth, and so forth.
Solid-state recorders used should allow signal reconstruction without amplitude and phase distortion.
Long-term ECG recorders using analogue magnetic media should accompany the signal with phase-locked time tracking.
Commercial equipment used to assess HRV should satisfy the technical requirements listed in "Standard measurement of HRV," and its performance should be independently tested.
To standardize physiological and clinical studies, two types of recordings should be used whenever possible: (a) short-term recordings of 5 minutes made under physiologically stable conditions processed by frequency domain methods and/or (b) nominal 24-hour recordings processed by time-domain methods.
When long-term ECGs are used in clinical studies, individual subjects should be recorded under fairly similar conditions and in a fairly similar environment.
When statistical time domain or frequency domain methods are used, the complete signal should be carefully edited using visual checks and manual corrections of individual RR intervals and QRS complex classifications. Automatic "filters" based on hypotheses on the logic of RR interval sequence (for example, exclusion of RR intervals according to a certain prematurity threshold) should not be relied on when the quality of the RR interval sequence is ensured.
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Physiological Correlates of HRV |
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TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
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Physiological Correlates of HRV Components
Autonomic Influences of Heart Rate
Although cardiac automaticity is intrinsic to various pacemaker tissues, heart rate and rhythm are largely under the control of the autonomic nervous system.59 The parasympathetic influence on heart rate is mediated via release of acetylcholine by the vagus nerve. Muscarinic acetylcholine receptors respond to this release mostly by an increase in cell membrane K+ conductance.60 61 62 Acetylcholine also inhibits the hyperpolarization-activated "pacemaker" current If.63 64 The "Ik decay" hypothesis65 proposes that pacemaker depolarization results from slow deactivation of the delayed rectifier current, Ik, which, due to a time-independent background inward current, causes diastolic depolarization.65 66 Conversely, the "If activation" hypothesis67 suggests that after action potential termination, If provides a slowly activating inward current predominating over decaying Ik, thus initiating slow diastolic depolarization.
The sympathetic influence on heart rate is mediated by release of epinephrine and norepinephrine. Activation of ß-adrenergic receptors results in cAMP-mediated phosphorylation of membrane proteins and increases in ICaL68 and in If.69 70 The end result is an acceleration of the slow diastolic depolarization.
Under resting conditions, vagal tone prevails71 and variations in heart period are largely dependent on vagal modulation.72 The vagal and sympathetic activity constantly interact. Because the sinus node is rich in acetylcholinesterase, the effect of any vagal impulse is brief because the acetylcholine is rapidly hydrolyzed. Parasympathetic influences exceed sympathetic effects probably through two independent mechanisms: (1) a cholinergically induced reduction of norepinephrine released in response to sympathetic activity and (2) a cholinergic attenuation of the response to a adrenergic stimulus.
Components of HRV
The RR interval variations
present during resting conditions
represent a fine tuning of the beat-to-beat control
mechanisms.73 74 Vagal afferent stimulation leads to
reflex excitation of vagal efferent activity and inhibition of
sympathetic efferent activity.75 The opposite reflex
effects are mediated by the stimulation of sympathetic afferent
activity.76 Efferent vagal activity also appears to be
under "tonic" restraint by cardiac afferent sympathetic
activity.77 Efferent sympathetic and vagal activities
directed to the sinus node are characterized by discharge largely
synchronous with each cardiac cycle that can be modulated by central
(vasomotor and respiratory centers) and peripheral
(oscillation in arterial pressure and
respiratory movements) oscillators.24 These oscillators
generate rhythmic fluctuations in efferent neural discharge that
manifest as short- and long-term oscillation in the
heart period. Analysis of these rhythms may permit inferences
on the state and function of (a) the central oscillators, (b) the
sympathetic and vagal efferent activity, (c) humoral factors, and (d)
the sinus node.
An understanding of the modulatory effects of neural
mechanisms on the
sinus node has been enhanced by spectral analysis of HRV. The
efferent vagal activity is a major contributor to the HF component, as
seen in clinical and experimental observations of autonomic maneuvers
such as electrical vagal stimulation, muscarinic receptor blockade, and
vagotomy.13 14 24 More controversial is
the interpretation
of the LF component, which is considered by
some24 78 79 80
as a marker of sympathetic modulation (especially when expressed in
normalized units) and by others13 81 as a
parameter that includes both sympathetic and vagal
influences. This discrepancy is due to the fact that in some conditions
associated with sympathetic excitation, a decrease in the absolute
power of the LF component is observed. It is important to recall that
during sympathetic activation the resulting tachycardia is
usually accompanied by a marked reduction in total power, whereas the
reverse occurs during vagal activation. When the spectral components
are expressed in absolute units (milliseconds squared), the changes in
total power influence LF and HF in the same direction and prevent the
appreciation of the fractional distribution of the energy. This
explains why in supine subjects under controlled respiration, atropine
reduces both LF and HF14 and why during exercise LF is
markedly reduced.24 This concept is exemplified in Fig
3,
showing the spectral analysis of HRV in a normal subject during
control supine conditions and 90° head-up tilt. Because of the
reduction in total power, LF appears as unchanged if considered in
absolute units. However, after normalization an increase in LF becomes
evident. Similar results apply to the LF/HF ratio.82
Spectral analysis of 24-hour recordings24 25 shows that in normal subjects, LF and HF expressed in normalized units exhibit a circadian pattern and reciprocal fluctuations, with higher values of LF in the daytime and of HF at night. These patterns become undetectable when a single spectrum of the entire 24-hour period is used or when spectra of subsequent shorter segments are averaged. In long-term recordings, the HF and LF components account for only approximately 5% of total power. Although the ULF and VLF components account for the remaining 95% of total power, their physiological correlates are still unknown.
LF and HF can increase under different conditions. An increased LF (expressed in normalized units) is observed during 90° tilt, standing, mental stress, and moderate exercise in healthy subjects, and during moderate hypotension, physical activity, and occlusion of a coronary artery or common carotid arteries in conscious dogs.24 79 Conversely, an increase in HF is induced by controlled respiration, cold stimulation of the face, and rotational stimuli.24 78
Summary and
Recommendations for Interpretation of HRV
Components
Vagal activity is the major contributor to the HF
component.
Disagreement exists in respect to the LF component. Some studies suggest that LF, when expressed in normalized units, is a quantitative marker of sympathetic modulations; other studies view LF as reflecting both sympathetic activity and vagal activity. Consequently, the LF/HF ratio is considered by some investigators to mirror sympathovagal balance or to reflect the sympathetic modulations.
Physiological interpretation of lower-frequency components of HRV (that is, of the VLF and ULF components) warrants further elucidation.
It is important to note that HRV measures fluctuations in autonomic inputs to the heart rather than the mean level of autonomic inputs. Thus, both autonomic withdrawal and saturatingly high level of sympathetic input lead to diminished HRV.28
Changes of HRV Related to Specific Pathologies
A reduction of
HRV has been reported in several cardiological and
noncardiological
diseases.24 78 81 83
Myocardial Infarction
Depressed HRV after MI may reflect
a decrease in vagal activity
directed to the heart, which leads to prevalence of sympathetic
mechanisms and to cardiac electrical instability. In the acute phase of
MI, the reduction in 24-hour SDNN is significantly related to left
ventricular dysfunction, peak creatine kinase, and Killip
class.84
The mechanism by which HRV is transiently reduced after MI and by which a depressed HRV is predictive of the neural response to acute MI is not yet defined, but it is likely to involve derangements in the neural activity of cardiac origin. One hypothesis85 involves cardiocardiac sympathosympathetic86 87 and sympathovagal reflexes75 and suggests that the changes in the geometry of a beating heart due to necrotic and noncontracting segments may abnormally increase the firing of sympathetic afferent fibers by mechanical distortion of the sensory endings.76 87 88 This sympathetic excitation attenuates the activity of vagal fibers directed to the sinus node. Another explanation, especially applicable to marked reduction of HRV, is the reduced responsiveness of sinus nodal cells to neural modulations.82 85
Spectral analysis of HRV in patients surviving an acute MI revealed a reduction in total and in the individual power of spectral components.89 However, when the power of LF and HF was calculated in normalized units, an increased LF and a diminished HF were observed during both resting controlled conditions and 24-hour recordings analyzed over multiple 5-minute periods.90 91 These changes may indicate a shift of sympathovagal balance toward a sympathetic predominance and a reduced vagal tone. Similar conclusions were obtained by considering the changes in LF/HF ratio. The presence of an alteration in neural control mechanisms was also reflected by the blunting of the day-night variations of RR interval91 and LF and HF spectral components91 92 present in a period ranging from days to a few weeks after the acute event. In post-MI patients with a very depressed HRV, most of the residual energy is distributed in the VLF frequency range below 0.03 Hz, with only a small respiration-related HF.93 These characteristics of the spectral profile are similar to those observed in an advanced cardiac failure or after cardiac transplant and are likely to reflect either a diminished responsiveness of the target organ to neural modulatory inputs82 or a saturating influence on the sinus node of a persistently high sympathetic tone.28
Diabetic
Neuropathy
In neuropathy associated with diabetes mellitus
characterized by alteration of small nerve fibers, a reduction in time
domain parameters of HRV seems not only to carry negative
prognostic value but also to precede the clinical expression of
autonomic
neuropathy.94 95 96 97 In
diabetic
patients without evidence of autonomic neuropathy,
reduction of the absolute power of LF and HF during controlled
conditions was also reported.96 However, when the LF/HF
ratio was considered or when LF and HF were analyzed in
normalized units, no significant difference in comparison to normal
subjects was present. Thus, the initial manifestation of this
neuropathy is likely to involve both efferent limbs of the
autonomic nervous system.96 98
Cardiac Transplantation
A very reduced HRV with no
definite spectral components was
reported in patients with a recent heart
transplant.97 99 100 The appearance of
discrete spectral
components in a few patients is considered to reflect cardiac
reinnervation.101 This reinnervation may occur as
early as 1 to 2 years after transplantation and is usually of
sympathetic origin. Indeed, the correlation between the respiratory
rate and the HF component of HRV observed in some transplanted patients
also indicates that a nonneural mechanism may contribute to generate a
respiration-related rhythmic
oscillation.100 The initial observation of
identifying patients developing an allograft rejection according to
changes in HRV could be of clinical interest but needs further
confirmation.
Myocardial Dysfunction
A reduced HRV
has been observed consistently in patients
with cardiac
failure.24 78 81 102 103 104 105 106
In this condition
characterized by signs of sympathetic activation such as faster heart
rates and high levels of circulating catecholamines, a
relation between changes in HRV and the extent of left
ventricular dysfunction was reported.102 104
In fact, whereas the reduction in time domain measures of HRV seemed to
parallel the severity of the disease, the relationship between spectral
components and indices of ventricular dysfunction appears
to be more complex. In particular, in most patients with a very
advanced phase of the disease and with a drastic reduction in HRV, an
LF component could not be detected despite the clinical signs of
sympathetic activation. Thus, in conditions characterized by a marked
and unopposed persistent sympathetic excitation, the sinus node seems
to drastically diminish its responsiveness to neural
inputs.104
Tetraplegia
Patients with
chronic complete high cervical spinal cord lesions
have intact efferent vagal and sympathetic neural pathways directed to
the sinus node. However, spinal sympathetic neurons are deprived of
modulatory control and in particular of baroreflex supraspinal
inhibitory inputs. For this reason, these patients
represent a unique clinical model to evaluate the contribution
of supraspinal mechanisms in determining the sympathetic activity
responsible for LF oscillations of HRV. It has been
reported107 that no LF could be detected in tetraplegic
patients, thus suggesting the critical role of supraspinal mechanisms
in determining the 0.1 Hz rhythm. Two recent studies, however, have
indicated that an LF component also can be detected in HRV and
arterial pressure variabilities of some tetraplegic
patients.108 109 While Koh et al108
attributed the LF component of HRV to vagal modulations, Guzzetti et
al109 attributed the same component to sympathetic
activity because of the delay with which the LF component appeared
after spinal section, suggesting an emerging spinal rhythmicity capable
of modulating sympathetic discharge.
Modifications of HRV by Specific Interventions
The rationale
for trying to modify HRV after MI stems from the
multiple observations indicating that cardiac mortality is higher among
those post-MI patients who have a more depressed
HRV.93 110 The inference is that interventions that
augment HRV may be protective against cardiac mortality and sudden
cardiac death. Although the rationale for changing HRV is sound, it
also contains the inherent danger of leading to the unwarranted
assumption that modification of HRV translates directly into cardiac
protection, which may not be the case.111 The target is
the improvement of cardiac electrical stability, and HRV is just a
marker of autonomic activity. Despite the growing consensus that
increases in vagal activity can be beneficial,112 it is
not as yet known how much vagal activity (or its markers) has to
increase in order to provide adequate protection.
ß-Adrenergic Blockade and HRV
The data on the effect
of ß-blockers on HRV in post-MI
patients are surprisingly scant.113 114 Despite the
observation of statistically significant increases, the actual changes
are very modest. However, it is of note that ß-blockade prevents
the rise in the LF component observed in the morning
hours.114 In conscious post-MI dogs, ß-blockers do
not modify HRV.115 The unexpected observation that before
MI, ß-blockade increases HRV only in the animals destined to be
at low risk for lethal arrhythmias after MI115 may
suggest novel approaches to post-MI risk stratification.
Antiarrhythmic Drugs and HRV
Data exist for several
antiarrhythmic drugs. Flecainide and
propafenone but not amiodarone were reported to decrease time
domain measures of HRV in patients with chronic ventricular
arrhythmia.116 In another study,117
propafenone reduced HRV and decreased LF much more than HF, resulting
in a significantly smaller LF/HF ratio. A larger study118
confirmed that flecainide, also encainide and moricizine, decreased HRV
in post-MI patients but found no correlation between the change in HRV
and mortality during follow-up. Thus, some antiarrhythmic drugs
associated with increased mortality can reduce HRV. However, it is not
known whether these changes in HRV have any direct prognostic
significance.
Scopolamine and HRV
Low-dose
muscarinic receptor blockers, such as atropine and
scopolamine, may produce a paradoxical increase in vagal efferent
activity, as suggested by a decrease in heart rate. Different studies
examined the effects of transdermal scopolamine on indices of vagal
activity in patients with a recent
MI119 120 121 122 and with
congestive heart failure.123 Scopolamine markedly
increases HRV, which indicates that pharmacological modulation of
neural activity with scopolamine may effectively increase vagal
activity. However, the efficacy during long-term treatment has not
been assessed. Furthermore, low-dose scopolamine does not prevent
ventricular fibrillation caused by acute myocardial
ischemia in post-MI dogs.124
Thrombolysis and
HRV
The effect of thrombolysis on HRV (assessed by
pNN50) was reported in 95 patients with acute MI.125 HRV
was higher 90 minutes after thrombolysis in the
patients with patency of the infarct-related artery. However, this
difference was no longer evident when the entire 24 hours were
analyzed.
Exercise Training and HRV
Exercise
training may decrease cardiovascular
mortality and sudden cardiac death.126 Regular exercise
training is also thought capable of modifying the autonomic
balance.127 128 A recent experimental study designed
to
assess the effects of exercise training on markers of vagal activity
has simultaneously provided information on changes in
cardiac electrical stability.129 Conscious dogs documented
to be at high risk by the previous occurrence of
ventricular fibrillation during acute myocardial
ischemia were randomly assigned to 6 weeks of either daily
exercise training or cage rest followed by exercise
training.129 After training, HRV (SDNN) increased by 74%,
and all animals survived a new ischemic test. Exercise training
can also accelerate recovery of the physiological
sympathovagal interaction, as shown in post-MI
patients.130
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Clinical Use of HRV |
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TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
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Although HRV has been the subject of numerous clinical studies investigating a wide spectrum of cardiological and noncardiological diseases and clinical conditions, a general consensus of the practical use of HRV in adult medicine has been reached only in two clinical scenarios. Depressed HRV can be used as a predictor of risk after acute MI and as an early warning sign of diabetic neuropathy.
Assessment of Risk After Acute MI
The
observation12 that in patients with an acute MI
the absence of respiratory sinus arrhythmias is associated with
an increase in "in-hospital" mortality represents the
first of a large number of
reports16 93 131 that have
demonstrated the prognostic value of assessing HRV to identify
high-risk patients.
Depressed HRV is a powerful predictor of mortality
and of arrhythmic
complications (for example, symptomatic sustained
ventricular tachycardia) in patients after
acute MI16 131 (Fig 8). The predictive
value of HRV is independent of other factors established for
postinfarction risk stratification, such as depressed left
ventricular ejection fraction, increased
ventricular ectopic activity, and presence of late
potentials. For prediction of all-cause mortality, the value of HRV
is similar to that of left ventricular ejection fraction.
However, HRV is superior to left ventricular ejection
fraction in predicting arrhythmic events (sudden cardiac death and
ventricular tachycardia).131 This
permits speculation that HRV is a stronger predictor of arrhythmic
mortality rather than nonarrhythmic mortality. However, clear
differences between HRV in patients suffering from sudden and nonsudden
cardiac death after acute MI have not been observed. Nevertheless, this
also might be related to the nature of the presently used
definition of sudden cardiac death,132 which is bound to
include not only patients suffering arrhythmia-related
death but also fatal reinfarctions and other
cardiovascular events.
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The value of both conventional time domain and frequency domain parameters has been fully assessed in several independent prospective studies. Because of using optimized cutoff values defining normal and depressed HRV, these studies may slightly overestimate the predictive value of HRV. Nevertheless, the confidence intervals of such cutoff values are rather narrow because of the sizes of investigated populations. Thus, the observed cutoff values of 24-hour measures of HRV, that is, SDNN <50 ms and HRV triangular index <15 for highly depressed HRV, or SDNN <100 ms and HRV triangular index <20 for moderately depressed HRV, are likely to be broadly applicable.
It is not known whether different indices of HRV (assessments of the short- and long-term components) can be combined in a multivariate fashion in order to improve postinfarction risk stratification. There is a general consensus, however, that combination of other measures with the assessment of overall 24-hour HRV is probably redundant.
Pathophysiological
Considerations
It has not been established whether depressed HRV is
part of the
mechanism of increased postinfarction mortality or is merely a marker
of poor prognosis. The data suggest that depressed HRV is not a simple
reflection of sympathetic overdrive and/or vagal withdrawal due to poor
ventricular performance but that it also reflects
depressed vagal activity, which has a strong association with the
pathogenesis of ventricular arrhythmias and sudden
cardiac death.112
Assessment of HRV for Risk
Stratification After Acute
MI
Traditionally, HRV used for risk stratification after MI has been
assessed from 24-hour recordings. HRV measured from
short-term ECG recordings also provides prognostic
information for risk stratification after MI but whether it is as
powerful as that from 24-hour recordings is
uncertain.133 134 135 HRV measured from
short-term
recordings is depressed in patients at high risk; the
predictive value of depressed HRV increases with increased length of
recording. Thus, the use of nominal 24-hour recordings
may be recommended for risk stratification studies after MI. On the
other hand, the assessment of HRV from short-term
recordings can be used for initial screening of survivors of
acute MI.136 Such an assessment has similar sensitivity
but lower specificity for predicting patients at high risk compared
with 24-hour HRV.
Spectral analysis of HRV in survivors of MI suggested that the ULF and VLF components carry the highest predictive value.93 Because the physiological correlate of these components is unknown and because these components correspond to up to 95% of the total power, which can be easily assessed in the time domain, the use of individual spectral components of HRV for risk stratification after MI is not more powerful than the use of those time domain methods that assess overall HRV.
Development of HRV After Acute MI
The time after
acute MI at which the depressed HRV reaches the
highest predictive value has not been investigated comprehensively.
Nevertheless, the general consensus is that HRV should be assessed
shortly before hospital discharge, that is, approximately 1 week after
index infarction. Such a recommendation also fits well into the common
practice of hospital management of survivors of acute MI.
HRV is decreased early after acute MI and begins to recover within a few weeks; it is maximally but not fully recovered by 6 to 12 months after MI.91 137 Assessment of HRV at both the early stage of MI (2 to 3 days after acute MI)84 and before discharge from the hospital (1 to 3 weeks after acute MI) offers important prognostic information. HRV measured late (1 year) after acute MI also predicts further mortality.138 Data from animal models suggest that the speed of HRV recovery after MI correlates with subsequent risk.115
HRV Used for Multivariate
Risk
Stratification
The predictive value of HRV alone is modest.
Combination with
other techniques substantially improves the positive predictive
accuracy of HRV over a clinically important range of sensitivity (25%
to 75%) for cardiac mortality and arrhythmic events (Fig 9).
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