(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
 |
Introduction |
|---|
 Top Introduction Background Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
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.
|
Background |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
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.
|
Measurement of HRV |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
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
).
|
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
|
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).
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
Physiological Correlates of HRV |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
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
|
Clinical Use of HRV |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
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.
|
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).
|
Improvements of the positive predictive accuracy over the range of sensitivities have been reported for combinations of HRV with mean heart rate, left ventricular ejection fraction, frequency of ventricular ectopic activity, parameters of high resolution ECGs (presence or absence of late potentials), and clinical assessment.139 However, it is not known which other stratification factors are the most practical and most feasible to be combined with HRV for multifactorial risk stratification.
Systematic multivariate studies of post-MI risk stratification are needed before a consensus can be reached and before it can be recommended how to combine HRV with other variables of proven prognostic importance. Many aspects that are not relevant for a univariate risk stratification need to be examined: It is not obvious whether the optimum cutoff values of individual risk factors known from univariate studies are appropriate in a multivariate setting. Different multivariate combinations are probably needed for optimizing predictive accuracy at different ranges of sensitivity. Stepwise strategies should be examined to identify optimum sequences of performing individual tests used in multivariate stratification.
Summary and Recommendations for
Interpreting Predictive Value of
Depressed HRV After Acute MI
The following facts should be noted when
HRV assessment is
exploited in clinical studies and/or trials involving survivors of
acute myocardial infarction.
Depressed HRV is a predictor of mortality and arrhythmic complications that is independent of other recognized risk factors.
There is a general consensus that HRV should be measured approximately 1 week after index infarction.
Although HRV assessed from short-term recordings provides prognostic information, HRV measured in nominal 24-hour recordings is a stronger risk predictor. HRV assessed from short-term recordings may be used for initial screening of all survivors of an acute MI.
No currently recognized HRV measure provides better prognostic information than the time domain HRV measures assessing overall HRV (SDNN or HRV triangular index). Some other measures, for example, ULF of entire 24-hour spectral analysis, perform equally well. A high-risk group may be selected by the dichotomy limits of SDNN <50 ms or HRV triangular index <15.
For clinically meaningful ranges of sensitivity, the predictive value of HRV alone is modest, although it is higher than that of any other recognized risk factor. To improve the predictive value, HRV may be combined with other factors. However, an optimum set of risk factors and corresponding dichotomy limits have not yet been established.
Assessment of Diabetic Neuropathy
As a complication of
diabetes mellitus, autonomic
neuropathy is characterized by early and widespread
neuronal degeneration of small nerve fibers of both sympathetic and
parasympathetic tracts.140 Its clinical manifestations are
ubiquitous with functional impairment and include postural hypotension,
persistent tachycardia, gustatory sweating, gastroparesis,
bladder atony, and nocturnal diarrhea. Once clinical manifestations of
diabetic autonomic neuropathy (DAN) supervene, the
estimated 5-year mortality is approximately 50%.141 Thus,
early subclinical detection of autonomic dysfunction is important for
risk stratification and subsequent management. Analyses of
short-term and/or long-term HRV have been proven useful in
detecting
DAN.96 142 143 144 145 146 147
For the patient presenting with a real or suspected DAN, there are three HRV methods from which to choose: (1) simple bedside RR interval methods, (2) long-term time domain measures that are more sensitive and more reproducible than the short-term tests, and (3) frequency domain analysis performed under short-term steady state conditions, which is useful in separating sympathetic from parasympathetic abnormalities.
Long-term Time Domain Measures
HRV computed from 24-hour Holter records are more sensitive
than simple bedside tests (Valsava maneuver, orthostatic
test, and deep breathing11 ) for detecting DAN. Most
experience has been obtained with the NN50144 and SDSD
(see Table 1)145 methods. When the NN50 count is
used,
where the lower 95% confidence interval for total counts range from
500 to 2000, depending on the age, about half of diabetic patients
demonstrate abnormally low counts per 24 hours. Moreover, there is a
strong correlation between the percentage of patients with abnormal
counts and the extent of autonomic neuropathy determined
from conventional measures.
Besides their increased sensitivity, these 24-hour time domain methods are strongly correlated with other established HRV measurements and have been found to be reproducible and stable over time. Similar to survivors of MI, patients with DAN are also predisposed to poor outcomes such as sudden death, but it remains to be determined whether the HRV measures confer prognostic information among diabetics.
Frequency Domain Measures
The following
abnormalities in frequency HRV
analysis are associated with DAN: (1) reduced power in all
spectral bands, which is the most common
finding,96 146 147 148 (2)
failure to increase LF on standing,
which is a reflection of impaired sympathetic response or depressed
baroreceptor sensitivity,96 147 (3) abnormally
reduced
total power with unchanged LF/HF ratio,96 and (4) a
leftward shift in the LF central frequency, the
physiological meaning of which needs further
elucidation.147
In advanced neuropathic states, the resting supine power spectrum often reveals extremely low amplitudes of all spectral components, making it difficult to separate signal from noise.96 146 147 It is therefore recommended that an intervention such as standing or tilt be included. Another method to overcome the low signal-to-noise ratio is to introduce a coherence function that utilizes the total power coherent with one or the other frequency band.146
Other Clinical Potential
Selected studies investigating HRV
in other cardiological diseases
are listed in Table
4.100 102 104 110 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
|
|
Future Possibilities |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
Development of HRV Measurement
The currently available time domain methods predominantly used to assess long-term profile of HRV probably are sufficient for this purpose. Their improvements are possible, especially in terms of numerical robustness. The contemporary nonparametric and parametric spectral methods probably are sufficient to analyze short-term ECGs without transient changes of heart period modulations.
Apart from the need to develop numerically robust techniques suitable for fully automatic measurement (the geometric methods are only one possibility in this direction), the following three areas deserve attention.
Dynamics and Transient Changes of HRV
The
present possibilities of characterizing and quantifying
the dynamics of the RR interval sequence and transient changes of HRV
are sparse and still under mathematical development. Moreover, it is
plausible to assume that proper assessment of HRV dynamics will lead to
substantial improvement in our understanding of both the modulations of
heart period and their physiological and
pathophysiological correlates.
It remains to be seen whether the methods of nonlinear dynamics will be most appropriate for the measurement of transient changes of RR intervals or whether new mathematical models and algorithmic concepts will be needed to tailor the principles of measurement more closely to the physiological nature of cardiac periodograms. In any case, the task of assessing transient changes in HRV seems to be more important than further refinements of the current technology used to analyze stable stages of heart period modulations.
PP
and PR Intervals
Little is known about the interplay between the PP
and PR
autonomic modulations. For these reasons, the sequence of PP intervals
also should be studied.168 Unfortunately, a precise
location of a P-wave fiducial point is almost impossible to achieve in
surface ECGs recorded with the current technology. However,
developments in the technology should allow PP interval and PR interval
variability to be investigated in future studies.
Multisignal Analysis
The modulations of heart
periods are naturally not the only
manifestation of the autonomic regulatory mechanisms. Currently,
commercial or semicommercial equipment exists that enables
simultaneous recording of ECG, respiration, blood
pressure, and so forth. However, despite the ease with which the
signals can be recorded, no widely accepted method exists for
comprehensive multisignal analysis. Each signal can be
analyzed separately, for example, with parametric
spectral methods, and the results of the analysis compared.
Analysis of coupling between physiological
signals allows the properties of the coupling to be
measured.169 170 171 172 173 174
Studies Needed to Improve Physiological
Understanding
Efforts should be made to find the
physiological correlates and the biological
relevance of various HRV measures currently used. In some cases, for
example, the HF component, this has been achieved. In other cases, for
example, the VLF and ULF components, the
physiological correlates are still largely
unknown.
This uncertain knowledge limits the interpretation of associations between these variables and the risk of cardiac events. The use of markers of autonomic activity is very attractive. However, unless a tenable mechanistic link between these variables and cardiac events is found, there is an inherent danger of concentrating therapeutic efforts on the modification of these markers.111 112 This may lead to incorrect assumptions and serious misinterpretations.
Possibilities of Future Clinical Utility
Normal
Standards
Large prospective population studies with longitudinal
follow-up are needed to establish normal HRV standards for various
age and sex subsets.110 Recently, investigators from the
Framingham Heart Study reported on the time and frequency domain
measures of HRV in 736 elderly subjects and the relationship of these
HRV measures to all-cause mortality during 4 years of
follow-up.175 These investigators concluded that HRV
offers prognostic information independent of and beyond that provided
by traditional risk factors. Additional population-based HRV
studies involving the full age spectrum in male and female subjects
need to be performed.
Physiological Phenomena
It
would be of interest to evaluate HRV in various circadian
patterns such as normal day-night cycles, sustained reversed
day-night cycles (evening-night shift work), and transiently
altered day-night cycles such as might occur with international
travel. The autonomic fluctuations occurring during various stages of
sleep including rapid eye movement (REM) sleep have been studied in
only a few subjects. In normal subjects, the HF vagal component of the
power spectrum is augmented only during non-REM sleep, whereas in
post-MI patients, this increase in HF is absent.176
The autonomic nervous system response to athletic training and rehabilitative exercise programs after various disease states is thought to be a conditioning phenomenon. HRV data should be useful in understanding the chronological aspects of training and the time to optimal conditioning as it relates to the autonomic influences on the heart. Also, HRV may provide important information about deconditioning with prolonged bed rest and with weightlessness and zero gravity that accompany space flight.
Pharmacological Responses
Many medications act directly or indirectly on the autonomic
nervous system, and HRV can be used to explore the influence of various
agents on sympathetic activity and parasympathetic activity. It is
known that parasympathetic blockade with full-dose atropine
produces marked diminution of HRV. Low-dose scopolamine has
vagotonic influences and is associated with increased HRV, especially
in the HF range. ß-Adrenergic blockade was observed to increase HRV
and to reduce the normalized units of the LF component.15
Considerably more research is needed to understand the effects and
clinical relevance of altered vagotonic and adrenergic tone on total
HRV power and its various components in health and disease.
At present, few data exist on the effects of calcium channel blockers, sedatives, anxiolytics, analgesics, anesthetics, antiarrhythmic agents, narcotics, and chemotherapeutic agents such as vincristine and doxorubicin on HRV.
Risk Stratification
Both time and frequency measures of HRV calculated from long
24-hour and short 2- to 15-minute ECG recordings have been used
to predict time to death after MI as well as the risk of all-cause
mortality and sudden cardiac death in patients with structural heart
disease162 163 177 and a number of other
pathophysiological conditions.177
With diagnostic instruments that can measure HRV together
with the frequency and complexity of ventricular
arrhythmias, signal-averaged ECG, ST-segment variability,
and repolarization heterogeneity, it should be possible
to markedly improve the identification of patients at risk for sudden
cardiac death and arrhythmic events. Prospective studies are needed to
evaluate the sensitivity, specificity, and predictive accuracy of
combined testing.
Fetal and neonatal HRV is an important area of investigation, and it might provide early information about fetal and neonatal distress and identify those at risk for sudden infant death syndrome. Most of the preliminary work in this field was carried out in the early 1980s before the more sophisticated power spectral techniques became available. Insight into autonomic maturation in the developing fetus also might be possible through the proper application of these techniques.
Disease Mechanisms
A fertile area of
research is the use of HRV techniques to explore
the role of autonomic nervous system alterations in disease mechanisms,
especially those conditions in which sympathovagal factors are thought
to play an important role. Recent work suggests that alterations in
autonomic innervation to the developing heart might be responsible for
some forms of long QT syndrome.178 Fetal HRV studies in
pregnant mothers with this disorder is certainly feasible and might be
very informative.179
The role of the autonomic nervous system in essential hypertension is an important area of investigation.180 The question regarding the primary or secondary role of enhanced sympathetic activity in essential hypertension might be answered by longitudinal studies of subjects who are initially normotensive. Does essential hypertension result from augmented sympathetic activity with altered responsiveness of neural regulatory mechanisms?
Several primary neurological disorders including Parkinson's disease, multiple sclerosis, Guillain-Barre syndrome, and orthostatic hypotension of the Shy-Drager type are associated with altered autonomic function. In some of these disorders, changes in HRV may be an early manifestation of the condition and may be useful in quantitating the rate of disease progression and/or the efficacy of therapeutic interventions. This same approach may also be useful in the evaluation of secondary autonomic neurological disorders that accompany diabetes mellitus, alcoholism, and spinal cord injuries.
Conclusions
HRV has considerable potential to assess the role
of autonomic
nervous system fluctuations in normal healthy individuals and in
patients with various cardiovascular and
noncardiovascular disorders. HRV studies should
enhance our understanding of physiological
phenomena, the actions of medications, and disease mechanisms. Large
prospective longitudinal studies are needed to determine the
sensitivity, specificity, and predictive value of HRV in the
identification of individuals at risk for subsequent morbid and mortal
events.
|
Appendix A |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
Normal Values of Standard Measures of HRV
As no comprehensive investigations of all HRV indices in large normal populations have yet been performed, some of the normal values listed in the following table were obtained from studies involving small numbers of subjects. The values should therefore be considered as approximate and no definite clinical conclusions should be based on them. The adjustment of normal limits for age, sex, and environment, which is also needed, has been omitted here because of the limited sources of data.
Table 5 lists only values of those
measures of HRV that
might be suggested for standardization of further
physiological and clinical
studies.181
|
|
Appendix B |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
Suggestion of Procedures for Testing of Commercial Equipment Designed to Measure HRV
Concept
To achieve comparable accuracy of measurements reported by different commercial equipment, each device should be tested independently of the manufacturer (for example, by an academic institution). Each test should involve several short-term and, if applicable, long-term test recordings with precisely known HRV parameters and with different morphological characteristics of the ECG signal. If the involvement of the manufacturer is required during the testing procedure (for example, for manual editing of the labels of QRS complexes), the manufacturer must be blinded in respect of both the true HRV parameters of the testing recordings and the features used to obtain the signal. In particular, when the results of the test are disclosed to the manufacturer for further improvement of the device or otherwise, new tests should involve a completely new set of test recordings.
Technical Requirements
Each device
should be tested comprehensively, including all its
parts. In particular, the test should involve both the
recording and the analytical part of the device. An appropriate
technology should be used to record a fully reproducible signal
with precisely known HRV parameters, that is, the test
signal should be computer and/or hardware generated. Both brand new
recorders as well as recorders that have been routinely used
and routinely serviced for approximately half of their lifetime should
be used in the tests if this is feasible (the testing should not be
delayed for newly introduced systems). If a manufacturer claims that
the device is capable of analyzing ECG records (such as Holter
tapes) obtained with recorders of other manufacturers, each
combination should be tested independently.
Since the analysis of HRV by implantable devices may be foreseen, similar procedures as described further should be used to generate simulated intracardiac signals. If feasible, implantable devices with fully charged batteries as well as devices with partly discharged batteries should be tested.
Test Recordings
It is intrinsically difficult to know precisely the HRV
parameters of any real ECG recordings independently
of equipment used to analyze the recording. Therefore,
simulated ECG signals are preferable. However, the morphology of such
simulated ECG signals as well as the HRV characteristics must closely
reflect the morphology of real recordings. The discrete
frequency used to generate such signals must be substantially higher
than the sampling frequency of the tested device. Features that should
be introduced into such recordings should include different
factors known to influence or potentially influence the precision of
HRV assessment, for example, variable noise levels, variable
morphology of QRS signals that may cause jitter of the fiducial point,
randomly alternating noise in different channels of the signal, gradual
and abrupt changes of HRV characteristics, and different frequencies of
atrial and ventricular ectopic beats with realistic
morphologies of the signal.
The quality of records on magnetic tape–based systems may not be constant during long-term recording due to spool torque control, back tension, and other factors. Performance of all recorders can be influenced by changes of the outside environment. Long-term (full 24-hour test) rather than short-term tests therefore should be used.
Testing Procedures
Each
device or each configuration of the device should be tested
with several different recordings having different mixtures of
features and different HRV characteristics. For each test record
and for each selected portion of the test record, the HRV
parameters obtained from the commercial device should be
compared with the known characteristics of the initial signal. Any
discrepancies found should be itemized in respect to features
introduced into the recording, for example, errors caused by
increased noise, errors caused by fiducial point wander, and so forth.
Systematic bias introduced by the equipment as well as its relative
errors should be established.
Reporting the Results
A technical report of the testing should be prepared solely at
the testing site independent of the manufacturer(s) of the tested
device.
|
Appendix C |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
The Task Force was established by the Board of the European Society of Cardiology and cosponsored by the North American Society of Pacing and Electrophysiology. It was organized jointly by the Working Groups on Arrhythmias and on Computers of Cardiology of the European Society of Cardiology. After exchanges of written views on the subject, the main meeting of a writing core of the Task Force took place May 8 through 10, 1994, on Necker Island, British Virgin Islands. After external reviews, the text of this report was approved by the Board of the European Society of Cardiology on August 19, 1995, and by the Board of the North American Society of Pacing and Electrophysiology on October 3, 1995.
Members of the Task Force
The Task Force was composed of 17
members: Cochairmen: A. John
Camm, London, UK; Marek Malik, London, UK; Members: J. Thomas
Bigger, Jr, New York, NY; Günter Breithardt, Münster,
Germany; Sergio Cerutti, Milano, Italy; Richard J. Cohen, Cambridge,
Mass; Philippe Coumel, Paris, France; Ernest L. Fallen, Hamilton,
Canada; Harold L. Kennedy, St Louis, Mo; Robert E. Kleiger, St Louis,
Mo; Federico Lombardi, Milano, Italy; Alberto Malliani, Milano, Italy;
Arthur J. Moss, Rochester, NY; Jeffrey N. Rottman, St Louis, Mo; Georg
Schmidt, München, Germany; Peter J. Schwartz, Pavia, Italy; and
Donald H. Singer, Chicago, Ill.
While the text of this report was contributed to and approved by all members of the Task Force, the structure of the text was designed by the Writing Committee of the Task Force, composed of the following members: Marek Malik (Chairman), J. Thomas Bigger, A. John Camm, Robert E. Kleiger, Alberto Malliani, Arthur J. Moss, and Peter J. Schwartz.
|
Selected Abbreviations and Acronyms |
|---|
|
|
|
Footnotes |
|---|
1 Membership of the Task Force is listed in "Appendix C."
|
References |
|---|
|
TopIntroductionBackground Measurement of HRV Physiological Correlates of HRV...Clinical Use of HRV...Future Possibilities Appendix A Appendix B Appendix C References |
|---|
1. Lown B, Verrier RL. Neural activity and ventricular fibrillation. N Engl J Med. 1976;294:1165-1170. [Medline] [Order article via Infotrieve]
2. Corr PB, Yamada KA, Witkowski FX. Mechanisms controlling cardiac autonomic function and their relation to arrhythmogenesis. In: Fozzard HA, Haber E, Jennings RB, Katz AN, Morgan HE, eds. The Heart and Cardiovascular System. New York, NY: Raven Press; 1986:1343-1403.
3. Schwartz PJ, Priori SG. Sympathetic nervous system and cardiac arrhythmias. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology: From Cell to Bedside. Philadelphia, Pa: WB Saunders Co; 1990:330-343.
4. Levy MN, Schwartz PJ, eds. Vagal Control of the Heart: Experimental Basis and Clinical Implications. Armonk, NY: Futura; 1994.
5. Dreifus LS, Agarwal JB, Botvinick EH, Ferdinand KC, Fisch C, Fisher JD, Kennedy JW, Kerber RE, Lambert CR, Okike ON, Prystowsky EN, Saksena SV, Schroeder JS, Williams DO (American College of Cardiology Cardiovascular Technology Assessment Committee). Heart rate variability for risk stratification of life-threatening arrhythmias. J Am Coll Cardiol. 1993;22:948-950. [Medline] [Order article via Infotrieve]
6. Hon EH, Lee ST. Electronic evaluations of the fetal heart rate patterns preceding fetal death: further observations. Am J Obstet Gynecol. 1965;87:814-826.
7. Sayers BM. Analysis of heart rate variability. Ergonomics. 1973;16:17-32. [Medline] [Order article via Infotrieve]
8. Penaz J, Roukenz J, Van der Waal HJ. In: Drischel H, Tiedt N, eds. Spectral Analysis of Some Spontaneous Rhythms in the Circulation. Leipzig, Germany: Biokybernetik, Karl Marx University; 1968:233-241.
9. Luczak H, Lauring WJ. An analysis of heart rate variability. Ergonomics. 1973;16:85-97. [Medline] [Order article via Infotrieve]
10.
Hirsh JA, Bishop B. Respiratory sinus
arrhythmia in humans: how breathing pattern modulates heart
rate. Am J Physiol. 1981;241:H620-H629.
11. Ewing DJ, Martin CN, Young RJ, Clarke BF. The value of cardiovascular autonomic function tests: 10 years' experience in diabetes. Diabetes Care. 1985;8:491-498. [Abstract]
12. Wolf MM, Varigos GA, Hunt D, Sloman JG. Sinus arrhythmia in acute myocardial infarction. Med J Aust. 1978;2:52-53. [Medline] [Order article via Infotrieve]
13.
Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC,
Cohen RJ. Power spectrum analysis of heart rate
fluctuation: a quantitative probe of beat to beat
cardiovascular control. Science. 1981;213:220-222.
14.
Pomeranz M, Macaulay RJB, Caudill MA, Kutz I, Adam D,
Gordon D, Kilborn KM, Barger AC, Shannon DC, Cohen RJ, Benson M.
Assessment of autonomic function in humans by heart rate spectral
analysis. Am J Physiol. 1985;248:H151-H153.
15.
Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan
R, Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E,
Turiel M, Baselli G, Cerutti S, Malliani A. Power spectral
analysis of heart rate and arterial pressure
variabilities as a marker of sympathovagal interaction in man and
conscious dog. Circ Res. 1986;59:178-193.
16. Kleiger RE, Miller JP, Bigger JT, Moss AJ, and the Multicenter Post-Infarction Research Group. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987;59:256-262. [Medline] [Order article via Infotrieve]
17.
Malik M, Farrell T, Cripps T, Camm AJ. Heart
rate variability in relation to prognosis after myocardial infarction:
selection of optimal processing techniques. Eur Heart
J. 1989;10:1060-1074.
18.
Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM,
Kleiger RE, Rottman JN. Frequency domain measures of heart
period variability and mortality after myocardial infarction.
Circulation. 1992;85:164-171.
19. Saul JP, Albrecht P, Berger RD, Cohen RJ. Analysis of long-term heart rate variability: methods, 1/f scaling and implications. In: Computers in Cardiology 1987. Washington, DC: IEEE Computer Society Press; 1988:419-422.
20. Bjökander I, Held C, Forslund L, Erikson S, Billing E, Hjemdahl P, Rehnqvist N. Heart rate variability in patients with stable angina pectoris. Eur Heart J. 1992;13:379. Abstract.
21. Scherer P, Ohler JP, Hirche H, Höpp H-W. Definition of a new beat-to-beat parameter of heart rate variability. PACE Pacing Clin Electrophysiol. 1993;16:939. Abstract.
22. Malik M, Xia R, Odemuyiwa O, Staunton A, Poloniecki J, Camm AJ. Influence of the recognition artefact in the automatic analysis of long-term electrocardiograms on time-domain measurement of heart rate variability. Med Biol Eng Comput. 1993;31:539-544. [Medline] [Order article via Infotrieve]
23. Kay SM, Marple SL. Spectrum analysis: a modern perspective. Proc IEEE. 1981;69:1380-1419.
24. Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84:1482-1492.
25.
Furlan R, Guzetti S, Crivellaro W, Dassi S, Tinelli
M, Baselli G, Cerutti S, Lombardi F, Pagani M, Malliani A.
Continuous 24-hour assessment of the neural regulation of systemic
arterial pressure and RR variabilities in ambulant
subjects. Circulation. 1990;81:537-547.
26. Berger RD, Akselrod S, Gordon D, Cohen RJ. An efficient algorithm for spectral analysis of heart rate variability. IEEE Trans Biomed Eng. 1986;33:900-904. [Medline] [Order article via Infotrieve]
27. Rottman JN, Steinman RC, Albrecht P, Bigger JT, Rolnitzky LM, Fleiss JL. Efficient estimation of the heart period power spectrum suitable for physiologic or pharmacologic studies. Am J Cardiol. 1990;66:1522-1524. [Medline] [Order article via Infotrieve]
28. Malik M, Camm AJ. Components of heart rate variability: what they really mean and what we really measure. Am J Cardiol. 1993;72:821-822. [Medline] [Order article via Infotrieve]
29. Bendat JS, Piersol AG. Measurement and Analysis of Random Data. New York, NY: Wiley & Sons; 1966.
30. Pinna GD, Maestri R, Di Cesare A, Colombo R, Minuco G. The accuracy of power-spectrum analysis of heart-rate variability from annotated RR list generated by Holter systems. Physiol Meas. 1994;15:163-179. [Medline] [Order article via Infotrieve]
31. Merri M, Farden DC, Mottley JG, Titlebaum EL. Sampling frequency of the electrocardiogram for the spectral analysis of heart rate variability. IEEE Trans Biomed Eng. 1990;37:99-106. [Medline] [Order article via Infotrieve]
32. Bianchi AM, Mainardi LT, Petrucci E, Signorini MG, Mainardi M, Cerutti S. Time-variant power spectrum analysis for the detection of transient episodes in HRV signal. IEEE Trans Biomed Eng. 1993;40:136-144. [Medline] [Order article via Infotrieve]
33. Friesen GM, Jannett TC, Jadalloh MA, Yates SL, Quint SR, Nogle HT. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng. 1990;37:85-98. [Medline] [Order article via Infotrieve]
34. Kamath MV, Fallen EL. Correction of the heart rate variability signal for ectopics and missing beats. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:75-85.
35. De Boer RW, Karemaker JM, Strackee J. Comparing spectra of a series of point events, particularly for heart-rate variability spectra. IEEE Trans Biomed Eng. 1984;31:384-387. [Medline] [Order article via Infotrieve]
36. Harris FJ. On the use of windows for harmonic analysis with the discrete Fourier transform. IEEE Proc. 1978;66:51-83.
37. Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control. San Francisco, Calif: Holden Day; 1976.
38. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Cont. 1974;19:716-723.
39. Kaplan DT. The analysis of variability. J Cardiovasc Electrophysiol. 1994;5:16-19. [Medline] [Order article via Infotrieve]
40.
Katona PG, Jih F. Respiratory sinus
arrhythmia: a noninvasive measure of parasympathetic cardiac
control. J Appl Physiol. 1975;39:801-805.
41.
Eckberg DL. Human sinus arrhythmia as
an index of vagal cardiac outflow. J Appl
Physiol. 1983;54:961-966.
42. Fouad FM, Tarazi RC, Ferrario CM, Fighaly S, Alicandri C. Assessment of parasympathetic control of heart rate by a noninvasive method. Am J Physiol. 1984;15:H838-H842.
43. Schechtman VL, Kluge KA, Harper RM. Time-domain system for assessing variation in heart rate. Med Biol Eng Comput. 1988;26:367-373. [Medline] [Order article via Infotrieve]
44.
Coumel PH, Hermida JS, Wennerblöm B, Leenhardt
A, Maison-Blanche P, Cauchemez B. Heart rate variability in
myocardial hypertrophy and heart failure, and the effects
of beta-blocking therapy: a non-spectral analysis of
heart rate oscillations. Eur Heart J. 1991;12:412-422.
45. Grossman P, Van Beek J, Wientjes C. A comparison of three quantification methods for estimation of respiratory sinus arrhythmia. Psychophysiology. 1990;27:702-714. [Medline] [Order article via Infotrieve]
46. Shin SJ, Tapp WN, Reisman SS, Natelson BH. Assessment of autonomic regulation of heart rate variability by the method of complex demodulation. IEEE Trans Biomed Eng. 1989;36:274-283. [Medline] [Order article via Infotrieve]
47. Kobayashi M, Musha T. 1/f fluctuation of heart beat period. IEEE Trans Biomed Eng. 1982;29:456-457. [Medline] [Order article via Infotrieve]
48.
Yamamoto Y, Hughson RL. Coarse-graining
spectral analysis: new method for studying heart rate
variability. J Appl Physiol. 1991;71:1143-1150.
49. Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator? Biol Cybern. 1988;58:203-211. [Medline] [Order article via Infotrieve]
50. Morfill GE, Demmel V, Schmidt G. Der plötzliche Herztod: Neue Erkenntnisse durch die Anwendung komplexer Diagnoseverfahren. Bioscope. 1994;2:11-19.
51. Schmidt G, Monfill GE. Nonlinear methods for heart rate variability assessment. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:87-98.
52. Kleiger RE, Bigger JT, Bosner MS, Chung MK, Cook JR, Rolnitzky LM, Steinman R, Fleiss JL. Stability over time of variables measuring heart rate variability in normal subjects. Am J Cardiol. 1991;68:626-630.[Medline] [Order article via Infotrieve]
53. Van Hoogenhuyze DK, Weinstein N, Martin GJ, Weiss JS, Schaad JW, Sahyouni XN, Fintel D, Remme WJ, Singer DH. Reproducibility and relation to mean heart rate of heart rate variability in normal subjects and in patients with congestive heart failure secondary to coronary artery disease. Am J Cardiol. 1991;68:1668-1676. [Medline] [Order article via Infotrieve]
54. Kautzner J. Reproducibility of heart rate variability measurement. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:165-171.
55. Bigger JT, Fleiss JL, Rolnitzsky LM, Steinman RC. Stability over time of heart period variability in patients with previous myocardial infarction and ventricular arrhythmias. Am J Cardiol. 1992;69:718-723. [Medline] [Order article via Infotrieve]
56.
Bailey JJ, Berson AS, Garson A Jr, Horan LG,
Macfarlane PW, Mortara DW, Zywietz C. Recommendations for
standardization and specifications in automated
electrocardiography.
Circulation. 1990;81:730-739.
57. Kennedy HN. Ambulatory (Holter) electrocardiography technology. Clin Cardiol. 1992;10:341-356.
58. Malik M, Cripps T, Farrell T, Camm AJ. Prognostic value of heart rate variability after myocardial infarction: a comparison of different data processing methods. Med Biol Eng Comput. 1989;27:603-611.
59. Jalife J, Michaels DC. Neural control of sinoatrial pacemaker activity. In: Levy MN, Schwartz PJ, eds. Vagal Control of the Heart: Experimental Basis and Clinical Implications. Armonk, NY: Futura; 1994:173-205.
60. Noma A, Trautwein W. Relaxation of the ACh-induced potassium current in the rabbit sinoatrial node cell. Pflugers Arch. 1978;377:193-200. [Medline] [Order article via Infotrieve]
61. Osterrieder W, Noma A, Trautwein W. On the kinetics of the potassium channel activated by acetylcholine in the S-A node of the rabbit heart. Pflugers Arch. 1980;386:101-109. [Medline] [Order article via Infotrieve]
62. Sakmann B, Noma A, Trautwein W. Acetylcholine activation of single muscarinic K+ channels in isolated pacemaker cells of the mammalian heart. Nature. 1983;303:250-253. [Medline] [Order article via Infotrieve]
63.
DiFrancesco D, Tromba C. Inhibition of the
hyperpolarizing-activated current If, induced by
acetylcholine in rabbit sino-atrial node myocytes.
J Physiol (Lond). 1988;405:477-491.
64.
DiFrancesco D, Tromba C. Muscarinic control of
the hyperpolarizing activated current If in rabbit
sino-atrial node myocytes. J Physiol
(Lond). 1988;405:493-510.
65.
Irisawa H, Brown HF, Giles WR. Cardiac
pacemaking in the sinoatrial node. Physiol Rev. 1993;73:197-227.
66. Irisawa H, Giles WR. Sinus and atrioventricular node cells: cellular electrophysiology. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology: From Cell to Bedside. Philadelphia, Pa: WB Saunders Co; 1990:95-102.
67.
DiFrancesco D. The contribution of the
pacemaker current (If) to generation of spontaneous activity in rabbit
sino-atrial node myocytes. J Physiol
(Lond). 1991;434:23-40.
68. Trautwein W, Kameyama M. Intracellular control of calcium and potassium currents in cardiac cells. Jpn Heart J. 1986;27:31-50.
69. Brown HF, DiFrancesco D, Noble SJ. How does adrenaline accelerate the heart? Nature. 1979;280:235-236. [Medline] [Order article via Infotrieve]
70.
DiFrancesco D, Ferroni A, Mazzanti M, Tromba
C. Properties of the hyperpolarizing-activated
current (If) in cells isolated from the rabbit sino-atrial
node. J Physiol (Lond). 1986;377:61-88.
71.
Levy MN. Sympathetic-parasympathetic
interactions in the heart. Circ Res. 1971;29:437-445.
72. Chess GF, Tam RMK, Calaresu FR. Influence of cardiac neural inputs on rhythmic variations of heart period in the cat. Am J Physiol. 1975;228:775-780.
73.
Akselrod S, Gordon D, Madwed JB, Snidman NC, Shannon
DC, Cohen RJ. Hemodynamic regulation:
investigation by spectral analysis. Am J
Physiol. 1985;249:H867-H875.
74.
Saul JP, Rea RF, Eckberg DL, Berger RD, Cohen
RJ. Heart rate and muscle sympathetic nerve variability during
reflex changes of autonomic activity. Am J Physiol. 1990;258:H713-H721.
75.
Schwartz PJ, Pagani M, Lombardi F, Malliani A, Brown
AM. A cardiocardiac sympathovagal reflex in the cat.
Circ Res. 1973;32:215-220.
76. Malliani A. Cardiovascular sympathetic afferent fibers. Rev Physiol Biochem Pharmacol. 1982;94:11-74.
77.
Cerati D, Schwartz PJ. Single cardiac vagal
fiber activity, acute myocardial ischemia, and risk for sudden
death. Circ Res. 1991;69:1389-1401.
78. Kamath MV, Fallen EL. Power spectral analysis of heart rate variability: a noninvasive signature of cardiac autonomic function. Crit Rev Biomed Eng. 1993;21:245-311. [Medline] [Order article via Infotrieve]
79.
Rimoldi O, Pierini S, Ferrari A, Cerutti S, Pagani M,
Malliani A. Analysis of short-term
oscillations of R-R and arterial pressure in
conscious dogs. Am J Physiol. 1990;258:H967-H976.
80.
Montano N, Ruscone TG, Porta A, Lombardi F, Pagani M,
Malliani A. Power spectrum analysis of heart rate
variability to assess the changes in sympathovagal balance during
graded orthostatic tilt.
Circulation. 1994;90:1826-1831.
81. Appel ML, Berger RD, Saul JP, Smith JM, Cohen RJ. Beat to beat variability in cardiovascular variables: noise or music? J Am Coll Cardiol. 1989;14:1139-1148. [Abstract]
82.
Malliani A, Lombardi F, Pagani M. Power
spectral analysis of heart rate variability: a tool to explore
neural regulatory mechanisms. Br Heart J. 1994;71:1-2.
83.
Malik M, Camm AJ. Heart rate variability and
clinical cardiology. Br Heart J. 1994;71:3-6.
84.
Casolo GC, Stroder P, Signorini C, Calzolari F,
Zucchini M, Balli E, Sulla A, Lazzerini S. Heart rate
variability during the acute phase of myocardial infarction.
Circulation. 1992;85:2073-2079.
85.
Schwartz PJ, Vanoli E, Stramba-Badiale M, De Ferrari
GM, Billman GE, Foreman RD. Autonomic mechanisms and sudden
death: new insights from the analysis of baroreceptor reflexes
in conscious dogs with and without a myocardial infarction.
Circulation. 1988;78:969-979.
86. Malliani A, Schwartz PJ, Zanchetti A. A sympathetic reflex elicited by experimental coronary occlusion. Am J Physiol. 1969;217:703-709.
87.
Brown AM, Malliani A. Spinal sympathetic
reflexes initiated by coronary receptors. J
Physiol. 1971;212:685-705.
88.
Malliani A, Recordati G, Schwartz PJ.
Nervous activity of afferent cardiac sympathetic fibres with atrial and
ventricular endings. J Physiol
(Lond). 1973;229:457-469.
89. Bigger JT Jr, Fleiss JL, Rolnitzky LM, Steinman RC, Schneider WJ. Time course of recovery of heart period variability after myocardial infarction. J Am Coll Cardiol. 1991;18:1643-1649. [Abstract]
90. Lombardi F, Sandrone G, Pernpruner S, Sala R, Garimoldi M, Cerutti S, Baselli G, Pagani M, Malliani A. Heart rate variability as an index of sympathovagal interaction after myocardial infarction. Am J Cardiol. 1987;60:1239-1245. [Medline] [Order article via Infotrieve]
91. Lombardi F, Sandrone G, Mortara A, La Rovere MT, Colombo E, Guzzetti S, Malliani A. Circadian variation of spectral indices of heart rate variability after myocardial infarction. Am Heart J. 1992;123:1521-1529. [Medline] [Order article via Infotrieve]
92. Kamath MV, Fallen EL. Diurnal variations of neurocardiac rhythms in acute myocardial infarction. Am J Cardiol. 1991;68:155-160. [Medline] [Order article via Infotrieve]
93. Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation. 1992;85:164-171.
94.
Ewing DJ, Neilson JMM, Traus P. New method for
assessing cardiac parasympathetic activity using 24-hour
electrocardiograms. Br Heart J. 1984;52:396-402.
95. Kitney RI, Byrne S, Edmonds ME, Watkins PJ, Roberts VC. Heart rate variability in the assessment of autonomic diabetic neuropathy. Automedica. 1982;4:155-167.
96. Pagani M, Malfatto G, Pierini S, Casati R, Masu AM, Poli M, Guzzetti S, Lombardi F, Cerutti S, Malliani A. Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. J Auton Nerv Syst. 1988;23:143-153. [Medline] [Order article via Infotrieve]
97.
Freeman R, Saul JP, Roberts MS, Berger RD,
Broadbridge C, Cohen RJ. Spectral analysis of heart rate
in diabetic neuropathy. Arch Neurol. 1991;48:185-190.
98.
Bernardi L, Ricordi L, Lazzari P, Soldá P,
Calciati A, Ferrari MR, Vandea I, Finardi G, Frantino P.
Impaired circulation modulation of sympathovagal modulation of
sympathovagal activity in diabetes.
Circulation. 1992;86:1443-1452.
99.
Bernardi L, Salvucci F, Suardi R, Soldá PL,
Calciati A, Perlini S, Falcone C, Ricciardi L. Evidence for an
intrinsic mechanism regulating heart rate variability in the
transplanted and the intact heart during submaximal dynamic
exercise? Cardiovasc Res. 1990;24:969-981.
100.
Sands KE, Appel ML, Lilly LS, Schoen FJ, Mudge GH Jr,
Cohen RJ. Power spectrum analysis of heart rate
variability in human cardiac transplant recipients.
Circulation. 1989;79:76-82.
101. Fallen EL, Kamath MV, Ghista DN, Fitchett D. Spectral analysis of heart rate variability following human heart transplantation: evidence for functional reinnervation. J Auton Nerv Syst. 1988;23:199-206. [Medline] [Order article via Infotrieve]
102. Casolo G, Balli E, Taddei T, Amuhasi J, Gori C. Decreased spontaneous heart rate variability on congestive heart failure. Am J Cardiol. 1989;64:1162-1167. [Medline] [Order article via Infotrieve]
103. Nolan J, Flapan AD, Capewell S, MacDonald TM, Neilson JMM, Ewing DJ. Decreased cardiac parasympathetic activity in chronic heart failure and its relation to left ventricular function. Br Heart J. 1992;69:761-767.
104. Kienzle MG, Ferguson DW, Birkett CL, Myers GA, Berg WJ, Mariano DJ. Clinical hemodynamic and sympathetic neural correlates of heart rate variability in congestive heart failure. Am J Cardiol. 1992;69:482-485. [Medline] [Order article via Infotrieve]
105.
Mortara A, La Rovere MT, Signorini MG, Pantaleo P,
Pinna G, Martinelli L, Ceconi C, Cerutti S, Tavazzi L. Can power
spectral analysis of heart rate variability identify a high
risk subgroup of congestive heart failure patients with excessive
sympathetic activation? A pilot study before and after heart
transplantation. Br Heart J. 1994;71:422-430.
106. Gordon D, Herrera VL, McAlpine L, Cohen RJ, Akselrod S, Lang P, Norwood WI. Heart rate spectral analysis: a noninvasive probe of cardiovascular regulation in critically ill children with heart disease. Pediatr Cardiol. 1988;9:69-77. [Medline] [Order article via Infotrieve]
107.
Inoue K, Miyake S, Kumashiro M, Ogata H, Yoshimura
O. Power spectral analysis of heart rate variability in
traumatic quadriplegic humans. Am J Physiol. 1990;258:H1722-H1726.
108. Koh J, Brown TE, Beightol LA, Ha CY, Eckberg DL. Human autonomic rhythms: vagal cardiac mechanisms in tetraplegic patients. J Physiol (Lond). 1994;474.3:483-495.
109.
Guzzetti S, Cogliati C, Broggi C, Carozzi C, Caldirolo
D, Lombardi F, Malliani A. Heart period and arterial
pressure variabilities in quadriplegic patients. Am J
Physiol. 1994;266:H1112-H1120.
110.
Algra A, Tijssen JGP, Roelandt JRTC, Pool J, Lubsen
J. Heart rate variability from 24-hour
electrocardiography and the 2-year risk for
sudden death. Circulation. 1993;88:180-185.
111. Schwartz PJ, De Ferrari GM. Interventions changing heart rate variability after acute myocardial infarction. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:407-420.
112. De Ferrari GM, Vanoli E, Schwartz PJ. Cardiac vagal activity, myocardial ischemia and sudden death. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology: From Cell to Bedside. Philadelphia, Pa: WB Saunders Co; 1995:422-434.
113. Molgaard H, Mickley H, Pless P, Bjerregaard P, Moller M. Effects of metoprolol on heart rate variability in survivors of acute myocardial infarction. Am J Cardiol. 1993;71:1357-1359. [Medline] [Order article via Infotrieve]
114. Sandrone G, Mortara A, Torzillo D, La Rovere MT, Malliani A, Lombardi F. Effects of beta blockers (atenolol or metoprolol) on heart rate variability after acute myocardial infarction. Am J Cardiol. 1994;74:340-345. [Medline] [Order article via Infotrieve]
115.
Adamson PB, Huang MH, Vanoli E, Foreman RD, Schwartz
PJ, Hull SS Jr. Unexpected interaction between ß-adrenergic
blockade and heart rate variability before and after myocardial
infarction: a longitudinal study in dogs at high and low risk for
sudden death. Circulation. 1994;90:976-982.
116. Zuanetti G, Latini R, Neilson JMM, Schwartz PJ, Ewing DJ, and the Antiarrhythmic Drug Evaluation Group (ADEG). Heart rate variability in patients with ventricular arrhythmias: effect of antiarrhythmic drugs. J Am Coll Cardiol. 1991;17:604-612. [Abstract]
117. Lombardi F, Torzillo D, Sandrone G, Dalla Vecchia L, Finocchiaro ML, Bernasconi R, Cappiello E. Beta-blocking effect of propafenone based on spectral analysis of heart rate variability. Am J Cardiol. 1992;70:1028-1034. [Medline] [Order article via Infotrieve]
118. Bigger JT Jr, Rolnitzky LM, Steinman RC, Fleiss JL. Predicting mortality after myocardial infarction from the response of RR variability to antiarrhythmic drug therapy. J Am Coll Cardiol. 1994;23:733-740. [Abstract]
119.
Casadei B, Pipilis A, Sessa F, Conway J, Sleight
P. Low doses of scopolamine increase cardiac vagal tone in the
acute phase of myocardial infarction.
Circulation. 1993;88:353-357.
120. De Ferrari GM, Mantica M, Vanoli E, Hull SS Jr, Schwartz PJ. Scopolamine increases vagal tone and vagal reflexes in patients after myocardial infarction. J Am Coll Cardiol. 1993;22:1327-1334. [Abstract]
121. Pedretti R, Colombo E, Sarzi Braga S, Car B. Influence of transdermal scopolamine on cardiac sympathovagal interaction after acute myocardial infarction. Am J Cardiol. 1993;72:384-392. [Medline] [Order article via Infotrieve]
122. Vybiral T, Glaser DH, Morris G, Hess KR, Yang K, Francis M, Pratt CM. Effects of low dose scopolamine on heart rate variability in acute myocardial infarction. J Am Coll Cardiol. 1993;22:1320-1326. [Abstract]
123.
La Rovere MT, Mortara A, Pantaneleo P, Maestri R,
Cobelli F, Tavazzi L. Scopolamine improves autonomic balance in
advanced congestive heart failure.
Circulation. 1994;90:838-843.
124.
Hull SS Jr, Vanoli E, Adamson PB, De Ferrari GM,
Foreman RD, Schwartz PJ. Do increase in markers of vagal
activity imply protection from sudden death? The case of
scopolamine. Circulation. 1995;91:2516-2519.
125. Zabel M, Klingenheben T, Hohnloser SH. Changes in autonomic tone following thrombolytic therapy for acute myocardial infarction: assessment by analysis of heart rate variability. J Cardiovasc Electrophysiol. 1994;5:211-218. [Medline] [Order article via Infotrieve]
126.
O'Connor GT, Buring JE, Yusuf S, Goldhaber SZ,
Olmstead EM, Paffenbarger RS Jr, Hennekens CH. An overview of
randomized trials of rehabilitation with exercise after myocardial
infarction. Circulation. 1989;80:234-244.
127.
Furlan R, Piazza D, Dell'Orto S, Gentile E, Cerutti
S, Pagani M, Malliani A. Early and late effects of exercise and
athletic training on neural mechanisms controlling heart rate.
Cardiovasc Res. 1993;27:482-488.
128.
Arai Y, Saul JP, Albrecht P, Hartley LH, Lilly LS,
Cohen RJ, Colucci WS. Modulation of cardiac autonomic activity
during and immediately after exercise. Am J Physiol. 1989;256:H132-H141.
129.
Hull SS Jr, Vanoli E, Adamson PB, Verrier RL, Foreman
RD, Schwartz PJ. Exercise training confers anticipatory
protection from sudden death during acute myocardial
ischemia. Circulation. 1994;89:548-552.
130. La Rovere MT, Mortara A, Sandrone G, Lombardi F. Autonomic nervous system adaptation to short-term exercise training. Chest. 1992;101:299-303.
131. Odemuyiwa O, Malik M, Farrell T, Bashir Y, Poloniecki J, Camm J. Comparison of the predictive characteristics of heart rate variability index and left ventricular ejection fraction for all-cause mortality, arrhythmic events and sudden death after acute myocardial infarction. Am J Cardiol. 1991;68:434-439. [Medline] [Order article via Infotrieve]
132. Greene HL, Richardson DW, Barker AH, Roden DM, Capone RJ, Echt DS, Friedman LM, Gillespie MJ, Hallstrom AP, Verter J, and the CAPS Investigators. Classification of deaths after myocardial infarction as arrhythmic or nonarrhythmic (the Cardiac Arrhythmia Pilot Study). Am J Cardiol. 1989;63:1-6. [Medline] [Order article via Infotrieve]
133. Malik M, Camm AJ. Significance of long-term components of heart rate variability for the further prognosis after acute myocardial infarction. Cardiovasc Res. 1990;24:793-803. [Medline] [Order article via Infotrieve]
134. Malik M, Farrell T, Camm AJ. Circadian rhythm of heart rate variability after acute myocardial infarction and its influence on the prognostic value of heart rate variability. Am J Cardiol. 1990;66:1049-1054. [Medline] [Order article via Infotrieve]
135.
Bigger JT, Fleiss JL, Rolnitzky LM, Steinman
RC. The ability of several short-term measures of RR
variability to predict mortality after myocardial infarction.
Circulation. 1993;88:927-934.
136. Fei L, Malik M. Short- and long-term assessment of heart rate variability for postinfarction risk stratification. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:341-346.
137. Bigger JT, Kleiger RE, Fleiss JL, Rolnitzky LM, Steinman RC, Miller JP, and the Multicenter Post-Infarction Research Group. Components of heart rate variability measured during healing of acute myocardial infarction. Am J Cardiol. 1988;61:208-215. [Medline] [Order article via Infotrieve]
138. Bigger JT, Fleiss JL, Rolnitzky LM, Steinman RC. Frequency domain measures of heart period variability to assess risk late after myocardial infarction. J Am Coll Cardiol. 1993;21:729-736. [Abstract]
139. Camm AJ, Fei L. Risk stratification following myocardial infarction: heart rate variability and other risk factors. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:369-392.
140. Bannister R. Autonomic Failure. A Textbook of Clinical Disorders of the Autonomic Nervous System. Oxford/New York: Oxford University Press; 1988.
141. Ewing DJ, Campbell IW, Clarke BF. The natural history of diabetic autonomic neuropathy. Q J Med. 1980;193:95-108.
142. Smith S. Reduced sinus arrhythmia in diabetic autonomic neuropathy: diagnostic value of an age related normal range. Br Med J. 1982;285:1599-1601.
143.
O'Brien IA, O'Hare P, Corrall RJM. Heart rate
variability in healthy subjects: effect of age and the derivation of
normal ranges for tests of autonomic function. Br Heart
J. 1986;55:348-354.
144.
Ewing DJ, Neilson JMM, Shapiro JA, Reid W.
Twenty four hour heart rate variability: effects of posture, sleep and
time of day in healthy controls and comparison with bedside tests of
autonomic function in diabetic patients. Br Heart J. 1991;65:239-244.
145. Malpas SC, Maling TJB. Heart rate variability and cardiac autonomic function in diabetes. Diabetes. 1990;39:1177-1181. [Abstract]
146. Bianchi A, Bontempi B, Cerutti S, Gianoglio P, Comi G, Natali Sora MG. Spectral analysis of heart rate variability signal and respiration in diabetic subjects. Med Biol Eng Comput. 1990;28:205-211. [Medline] [Order article via Infotrieve]
147. Bellavere F, Balzani I, De Masi G, Carraro M, Carenza P, Cobelli C, Thomaseth K. Power spectral analysis of heart rate variation improves assessment of diabetic cardiac autonomic neuropathy. Diabetes. 1992;41:633-640. [Abstract]
148. Van den Akker TJ, Koelman ASM, Hogenhuis LAH, Rompelman G. Heart rate variability and blood pressure oscillations in diabetics with autonomic neuropathy. Automedica. 1983;4:201-208.
149. Guzzetti S, Dassi S, Pecis M, Casati R, Masu AM, Longoni P, Tinelli M, Cerutti S, Pagani M, Malliani A. Altered pattern of circadian neural control of heart period in mild hypertension. J Hypertens. 1991;9:831-838. [Medline] [Order article via Infotrieve]
150. Langewitz W, Ruddel H, Schachinger H. Reduced parasympathetic cardiac control in patients with hypertension at rest and under mental stress. Am Heart J. 1994;127:122-128. [Medline] [Order article via Infotrieve]
151. Saul JP, Arai Y, Berger RD, Lily LS, Wilson S, Colucci WS, Cohen RJ. Assessment of autonomic regulation in chronic congestive heart failure by the heart rate spectral analysis. Am J Cardiol. 1988;61:1292-1299. [Medline] [Order article via Infotrieve]
152. Binkley PF, Nunziata E, Haas GJ, Nelson SD, Cody RJ. Parasympathetic withdrawal is an integral component of autonomic imbalance in congestive heart failure: demonstration in human subjects and verification in a paced canine model of ventricular failure. J Am Coll Cardiol. 1991;18:464-472. [Abstract]
153. Townend JN, West JN, Davies MK, Littler WA. Effect of quinapril on blood pressure and heart rate in congestive heart failure. Am J Cardiol. 1992;69:1587-1590. [Medline] [Order article via Infotrieve]
154. Binkley PF, Haas GJ, Starling RC, Nunziata E, Hatton PA, Leier CV, Cody JR. Sustained augmentation of parasympathetic tone with angiotensin converting enzyme inhibitor in patients with congestive heart failure. J Am Coll Cardiol. 1993;21:655-661. [Abstract]
155. Woo MA, Stevenson WG, Moser DK, Middlekauff HR. Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure. J Am Coll Cardiol. 1994;23:565-569. [Abstract]
156. Alexopoulos D, Yusuf S, Johnston JA, Bostock J, Sleight P, Yacoub MH. The 24-hour heart rate behavior in long-term survivors of cardiac transplantation. Am J Cardiol. 1988;61:880-884. [Medline] [Order article via Infotrieve]
157.
Stein KM, Bores JS, Hochreites C, Okin PM, Herrold EM,
Devereux RB, Kligfield P. Prognostic value and
physiological correlates of heart rate variability
in chronic severe mitral regurgitation.
Circulation. 1993;88:127-135.
158. Marangoni S, Scalvini S, Mai R, Quadri A, Levi GF. Heart rate variability assessment in patients with mitral valve prolapse syndrome. Am J Noninvas Cardiol. 1993;7:210-214.
159.
Counihan PJ, Fei L, Bashir Y, Farrel TG, Haywood GA,
McKenna WJ. Assessment of heart rate variability in hypertrophic
cardiomyopathy: association with clinical and
prognostic features. Circulation. 1993;88:1682-1690.
160. Dougherty CM, Burr RL. Comparison of heart rate variability in survivors and nonsurvivors of sudden cardiac arrest. Am J Cardiol. 1992;70:441-448. [Medline] [Order article via Infotrieve]
161. Huikuri HV, Linnaluoto MK, Seppanen T, Airaksinen KEJ, Kessler KM, Takkunen JT, Myerburg RJ. Circadian rhythm of heart rate variability in survivors of cardiac arrest. Am J Cardiol. 1992;70:610-615. [Medline] [Order article via Infotrieve]
162. Myers GA, Martin GJ, Magid NM, Barnett PS, Schaad JW, Weiss JS, Lesch M, Singer DH. Power spectral analysis of heart rate variability in sudden cardiac death: comparison to other methods. IEEE Trans Biomed Eng. 1986;33:1149-1156. [Medline] [Order article via Infotrieve]
163. Martin GJ, Magid NM, Myers G, Barnett PS, Schaad JW, Weiss JS, Lesch M, Singer DH. Heart rate variability and sudden death secondary to coronary artery disease during ambulatory ECG monitoring. Am J Cardiol. 1986;60:86-89.
164. Vybiral T, Glaeser DH, Goldberger AL, Rigney DR, Hess KR, Mietus J, Skinner JE, Francis M, Pratt CM. Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation. J Am Coll Cardiol. 1993;22:557-565. [Abstract]
165.
Huikuri HV, Valkama JO, Airaksinen KEJ, Seppanen T,
Kessler KM, Takkunen JT, Myerburg RJ. Frequency domain measures
of heart rate variability before the onset of nonsustained and
sustained ventricular tachycardia in patients
with coronary artery disease.
Circulation. 1993;87:1220-1228.
166.
Hohnloser SH, Klingenheben T, van de Loo A, Hablawetz
E, Just H, Schwartz PJ. Reflex versus tonic vagal activity as a
prognostic parameter in patients with sustained
ventricular tachycardia or
ventricular fibrillation.
Circulation. 1994;89:1068-1073.
167.
Kocovic DZ, Harada T, Shea JB, Soroff D, Friedman
PL. Alterations of heart rate and of heart rate variability
after radiofrequency catheter ablation of
supraventricular tachycardia.
Circulation. 1993;88:1671-1681.
168. Lefler CT, Saul JP, Cohen RJ. Rate-related and autonomic effects on atrioventricular conduction assessed through beat-to-beat PR interval and cycle length variability. J Cardiovasc Electrophysiol. 1994;5:2-15. [Medline] [Order article via Infotrieve]
169. Berger RD, Saul JP, Cohen RJ. Assessment of autonomic response by broad-band respiration. IEEE Trans Biomed Eng. 1989;36:1061-1065. [Medline] [Order article via Infotrieve]
170.
Berger RD, Saul JP, Cohen RJ. Transfer function
analysis of autonomic regulation, I: the canine atrial rate
response. Am J Physiol. 1989;256:H142-H152.
171.
Saul JP, Berger RD, Chen MH, Cohen RJ. Transfer
function analysis of autonomic regulation, II: respiratory
sinus arrhythmia. Am J Physiol. 1989;256:H153-H161.
172.
Saul JP, Berger RD, Albrecht P, Stein SP, Chen MH,
Cohen RJ. Transfer function analysis of the circulation:
unique insights into cardiovascular regulation.
Am J Physiol. 1991;261:H1231-H1245.
173. Baselli G, Cerutti S, Civardi S, Malliani A, Pagani M. Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms. IEEE Trans Biomed Eng. 1988;35:1033-1046. [Medline] [Order article via Infotrieve]
174. Appel ML, Saul JP, Berger RD, Cohen RJ. Closed loop identification of cardiovascular circulatory mechanisms. In: Computers in Cardiology 1989. Los Alamitos, Calif: IEEE Press; 1990:3-7.
175.
Tsuji H, Venditti FJ, Manders ES, Evans JC, Larson MG,
Feldman CL, Levy D. Reduced heart rate variability and mortality
risk in an elderly cohort: the Framingham Heart Study.
Circulation. 1994;90:878-883.
176.
Vanoli E, Adamson PB, Lin B, Pinna GD, Lazzara R, Or
WC. Heart rate variability during specific sleep stages: a
comparison of healthy subjects with patients after myocardial
infarction. Circulation. 1995;91:1918-1922.
177. Singer DH, Ori Z. Changes in heart rate variability associated with sudden cardiac death. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:429-448.
178.
Malfatto, G, Rosen TS, Steinberg SF, Ursell PC, Sun
LS, Daniel S, Danilo P Jr, Rosen MR. Sympathetic neural
modulation of cardiac impulse initiation and repolarization in the
newborn rat. Circ Res. 1990;66:427-437.
179. Hirsch M, Karin J, Akselrod S. Heart rate variability in the fetus. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:517-531.
180. Parati G, Di Rienzo M, Groppelli A, Pedotti A, Mancia G. Heart rate and blood pressure variability and their interaction in hypertension. In: Malik M, Camm AJ, eds. Heart Rate Variability. Armonk, NY: Futura; 1995:465-478.
181.
Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM,
Schneider WJ, Stein PK. RR variability in healthy,
middle-age persons compared with patients with chronic
coronary heart disease or recent acute myocardial
infarction. Circulation. 1995;91:1936-1943.
This article has been cited by other articles:
 |
|
 |
|
  S. Galluzzi, F. Nicosia, C. Geroldi, A. Alicandri, M. Bonetti, G. Romanelli, R. Zulli, and G. B. Frisoni Cardiac Autonomic Dysfunction Is Associated With White Matter Lesions in Patients With Mild Cognitive Impairment J Gerontol A Biol Sci Med Sci, December 1, 2009; 64A(12): 1312 - 1315. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. Z. Soliman, M. A. Elsalam, and Y. Li The relationship between high resting heart rate and ventricular arrhythmogenesis in patients referred to ambulatory 24 h electrocardiographic recording Europace, November 3, 2009; (2009) eup344v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Mermigkis, D. Mermigkis, G. Varouchakis, S. Schiza, and P. Panagou Poincare plot in obstructive sleep apnoea patients before and after CPAP treatment Eur. Respir. J., November 1, 2009; 34(5): 1197 - 1198. [Full Text] [PDF]
|
|
|
|
 |
|
 J. R. Duncan, M. Garland, M. M. Myers, W. P. Fifer, M. Yang, H. C. Kinney, and R. I. Stark Prenatal nicotine-exposure alters fetal autonomic activity and medullary neurotransmitter receptors: implications for sudden infant death syndrome J Appl Physiol, November 1, 2009; 107(5): 1579 - 1590. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. W. Parry, M. Norton, J. Pairman, M. Baptist, K. Wilton, P. Reeve, K. Sutcliffe, and J. L. Newton Impedance cardiography: a role in vasovagal syncope diagnosis? Age Ageing, November 1, 2009; 38(6): 718 - 723. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Weissman, M. Aranovitch, S. Blazer, and E. Z. Zimmer Heel-Lancing in Newborns: Behavioral and Spectral Analysis Assessment of Pain Control Methods Pediatrics, November 1, 2009; 124(5): e921 - e926. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Yamada, N. Yoshida, Y. Murakami, T. Okada, Y. Yoshida, M. Muto, Y. Inden, and T. Murohara The difference in autonomic denervation and its effect on atrial fibrillation recurrence between the standard segmental and circumferential pulmonary vein isolation techniques Europace, October 29, 2009; (2009) eup330v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L.-W. Chien, M.-H. Lin, H.-Y. Chung, and C.-F. Liu Transcutaneous Electrical Stimulation of Acupoints Changes Body Composition and Heart Rate Variability in Postmenopausal Women with Obesity Evid. Based Complement. Altern. Med., October 21, 2009; (2009) nep145v2. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. H. Cooke, C. A. Rickards, K. L. Ryan, T. A. Kuusela, and V. A. Convertino Muscle sympathetic nerve activity during intense lower body negative pressure to presyncope in humans J. Physiol., October 15, 2009; 587(20): 4987 - 4999. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Wensel, C. Jilek, M. Dorr, D. P. Francis, H. Stadler, T. Lange, F. Blumberg, C. Opitz, M. Pfeifer, and R. Ewert Impaired cardiac autonomic control relates to disease severity in pulmonary hypertension Eur. Respir. J., October 1, 2009; 34(4): 895 - 901. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. P. Nolan, S. M. Barry-Bianchi, A. E. Mechetiuc, and M. H. Chen Sex-based differences in the association between duration of type 2 diabetes and heart rate variability Diabetes and Vascular Disease Research, October 1, 2009; 6(4): 276 - 282. [Abstract] [PDF]
|
|
|
|
 |
|
 J. L. Francis, A. A. Weinstein, D. S. Krantz, M. C. Haigney, P. K. Stein, P. H. Stone, J. S. Gottdiener, and W. J. Kop Association Between Symptoms of Depression and Anxiety With Heart Rate Variability in Patients With Implantable Cardioverter Defibrillators Psychosom Med, October 1, 2009; 71(8): 821 - 827. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Ng, S. Sundaram, A. H. Kadish, and J. J. Goldberger Autonomic effects on the spectral analysis of heart rate variability after exercise Am J Physiol Heart Circ Physiol, October 1, 2009; 297(4): H1421 - H1428. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. E. Billman Cardiac autonomic neural remodeling and susceptibility to sudden cardiac death: effect of endurance exercise training Am J Physiol Heart Circ Physiol, October 1, 2009; 297(4): H1171 - H1193. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H.-J. Lee, Y. Chae, H.-J. Park, D.-H. Hahm, K. An, and H. Lee Turo (Qi Dance) Training Attenuates Psychological Symptoms and Sympathetic Activation Induced by Mental Stress in Healthy Women Evid. Based Complement. Altern. Med., September 1, 2009; 6(3): 399 - 405. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Adams, P. Julian, M. Hubbard, J. Hartman, S. Baugh, W. Segrest, J. Russell, J. McDonnell, and K. Wheelan A randomized controlled trial of a controlled breathing protocol on heart rate variability following myocardial infarction or coronary artery bypass graft surgery Clinical Rehabilitation, September 1, 2009; 23(9): 782 - 789. [Abstract] [PDF]
|
|
|
|
 |
|
 S. Munjal, T. Koval, R. Muhammad, Y. Jin, V. Demmel, H. J. Roethig, P. Mendes, and M. Unverdorben Heart Rate Variability Increases With Reductions in Cigarette Smoke Exposure After 3 Days Journal of Cardiovascular Pharmacology and Therapeutics, September 1, 2009; 14(3): 192 - 198. [Abstract] [PDF]
|
|
|
|
 |
|
 H. J.B.H. Beijers, I. Ferreira, B. Bravenboer, J. M. Dekker, G. Nijpels, R. J. Heine, and C. D.A. Stehouwer Microalbuminuria and Cardiovascular Autonomic Dysfunction Are Independently Associated With Cardiovascular Mortality: Evidence for Distinct Pathways: The Hoorn Study Diabetes Care, September 1, 2009; 32(9): 1698 - 1703. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. Kinoshita, K. Kuwahara, M. Takano, Y. Arai, Y. Kuwabara, S. Yasuno, Y. Nakagawa, M. Nakanishi, M. Harada, M. Fujiwara, et al. T-Type Ca2+ Channel Blockade Prevents Sudden Death in Mice With Heart Failure Circulation, September 1, 2009; 120(9): 743 - 752. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Piccirillo, D. Magri, M. Ogawa, J. Song, V. J. Chong, S. Han, B. Joung, E.-K. Choi, S. Hwang, L. S. Chen, et al. Autonomic Nervous System Activity Measured Directly and QT Interval Variability in Normal and Pacing-Induced Tachycardia Heart Failure Dogs. J. Am. Coll. Cardiol., August 25, 2009; 54(9): 840 - 850. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Van Leeuwen, D. Geue, M. Thiel, D. Cysarz, S. Lange, M. C. Romano, N. Wessel, J. Kurths, and D. H. Gronemeyer From the Cover: Influence of paced maternal breathing on fetal-maternal heart rate coordination PNAS, August 18, 2009; 106(33): 13661 - 13666. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Richardson, J. Baker, P.W. Thomas, C. Meckes, A. Rozkovec, and D. Kerr Randomized control trial investigating the influence of coffee on heart rate variability in patients with ST-segment elevation myocardial infarction QJM, August 1, 2009; 102(8): 555 - 561. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X. Bai, J. Li, L. Zhou, and X. Li Influence of the menstrual cycle on nonlinear properties of heart rate variability in young women Am J Physiol Heart Circ Physiol, August 1, 2009; 297(2): H765 - H774. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. F. Folino, M. L. Scapellato, C. Canova, P. Maestrelli, G. Bertorelli, L. Simonato, S. Iliceto, and M. Lotti Individual exposure to particulate matter and the short-term arrhythmic and autonomic profiles in patients with myocardial infarction Eur. Heart J., July 1, 2009; 30(13): 1614 - 1620. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Suzuki, S. Watanabe, T. Hamaguchi, H. Mine, T. Terui, M. Kanazawa, N. Oohisa, M. Maruyama, T. Yambe, M. Itoh, et al. Brain Activation Associated With Changes in Heart Rate, Heart Rate Variability, and Plasma Catecholamines During Rectal Distention Psychosom Med, July 1, 2009; 71(6): 619 - 626. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Buchheit, H. Al Haddad, P. B. Laursen, and S. Ahmaidi Effect of body posture on postexercise parasympathetic reactivation in men Exp Physiol, July 1, 2009; 94(7): 795 - 804. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Legramante, F. Valentini, A. Magrini, G. Palleschi, S. Sacco, I. Iavicoli, M. Pallante, D. Moscone, A. Galante, E. Bergamaschi, et al. Cardiac autonomic regulation after lung exposure to carbon nanotubes Human and Experimental Toxicology, June 1, 2009; 28(6-7): 369 - 375. [Abstract] [PDF]
|
|
|
|
 |
|
 M.-H. Song, Y. Tokuda, T. Nakayama, M. Sato, and K. Hattori Intraoperative heart rate variability of a cardiac surgeon himself in coronary artery bypass grafting surgery Interactive CardioVascular and Thoracic Surgery, June 1, 2009; 8(6): 639 - 641. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Deschamps and A. Denault Autonomic Nervous System and Cardiovascular Disease Seminars in Cardiothoracic and Vascular Anesthesia, June 1, 2009; 13(2): 99 - 105. [Abstract] [PDF]
|
|
|
|
|
|
R. Varadhan, P. H. M. Chaves, L. A. Lipsitz, P. K. Stein, J. Tian, B. G. Windham, R. D. Berger, and L. P. Fried Frailty and Impaired Cardiac Autonomic Control: New Insights From Principal Components Aggregation of Traditional Heart Rate Variability Indices J Gerontol A Biol Sci Med Sci, June 1, 2009; 64A(6): 682 - 687. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. M. M. Licht, E. J. C. de Geus, R. van Dyck, and B. W. J. H. Penninx Association between Anxiety Disorders and Heart Rate Variability in The Netherlands Study of Depression and Anxiety (NESDA) Psychosom Med, June 1, 2009; 71(5): 508 - 518. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. M. Samet, A. Rappold, D. Graff, W. E. Cascio, J. H. Berntsen, Y.-C. T. Huang, M. Herbst, M. Bassett, T. Montilla, M. J. Hazucha, et al. Concentrated Ambient Ultrafine Particle Exposure Induces Cardiac Changes in Young Healthy Volunteers Am. J. Respir. Crit. Care Med., June 1, 2009; 179(11): 1034 - 1042. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. S. Leicht, W. H. Sinclair, M. J. Patterson, S. Rudzki, M. P. Tulppo, A. L. Fogarty, and S. Winter Influence of postexercise cooling techniques on heart rate variability in men Exp Physiol, June 1, 2009; 94(6): 695 - 703. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Manzi, C. Castagna, E. Padua, M. Lombardo, S. D'Ottavio, M. Massaro, M. Volterrani, and F. Iellamo Dose-response relationship of autonomic nervous system responses to individualized training impulse in marathon runners Am J Physiol Heart Circ Physiol, June 1, 2009; 296(6): H1733 - H1740. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Kimhy, P. Delespaul, H. Ahn, S. Cai, M. Shikhman, J. A. Lieberman, D. Malaspina, and R. P. Sloan Concurrent Measurement of "Real-World" Stress and Arousal in Individuals With Psychosis: Assessing the Feasibility and Validity of a Novel Methodology Schizophr Bull, May 8, 2009; (2009) sbp028v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J L Barclay, B G Miller, S Dick, M Dennekamp, I Ford, G S Hillis, J G Ayres, and A Seaton A panel study of air pollution in subjects with heart failure: negative results in treated patients Occup. Environ. Med., May 1, 2009; 66(5): 325 - 334. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. G. Frasch, T. Muller, C. Weiss, K. Schwab, H. Schubert, and M. Schwab Heart Rate Variability Analysis Allows Early Asphyxia Detection in Ovine Fetus Reproductive Sciences, May 1, 2009; 16(5): 509 - 517. [Abstract] [PDF]
|
|
|
|
|
|
M. Louis and N. M. Punjabi Effects of acute intermittent hypoxia on glucose metabolism in awake healthy volunteers J Appl Physiol, May 1, 2009; 106(5): 1538 - 1544. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C L McGowan, J S Swiston, C F Notarius, S Mak, B L Morris, P E Picton, J T Granton, and J S Floras Discordance between microneurographic and heart-rate spectral indices of sympathetic activity in pulmonary arterial hypertension Heart, May 1, 2009; 95(9): 754 - 758. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Blain, O. Meste, A. Blain, and S. Bermon Time-frequency analysis of heart rate variability reveals cardiolocomotor coupling during dynamic cycling exercise in humans Am J Physiol Heart Circ Physiol, May 1, 2009; 296(5): H1651 - H1659. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Kawada, M. Mizuno, S. Shimizu, K. Uemura, A. Kamiya, and M. Sugimachi Angiotensin II disproportionally attenuates dynamic vagal and sympathetic heart rate controls Am J Physiol Heart Circ Physiol, May 1, 2009; 296(5): H1666 - H1674. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K.-J. Bar, S. Berger, M. Metzner, M. K. Boettger, S. Schulz, C. T. Ramachandraiah, J. Terhaar, A. Voss, V. K. Yeragani, and H. Sauer Autonomic Dysfunction in Unaffected First-Degree Relatives of Patients Suffering From Schizophrenia Schizophr Bull, April 14, 2009; (2009) sbp024v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. V Huikuri, J. S Perkiomaki, R. Maestri, and G. D. Pinna Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics Phil Trans R Soc A, April 13, 2009; 367(1892): 1223 - 1238. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Retzlaff, R. Bauernschmitt, H. Malberg, G. Brockmann, C. Uhl, R. Lange, J. Kurths, G. Bretthauer, and N. Wessel Depression of cardiovascular autonomic function is more pronounced after mitral valve surgery: evidence for direct trauma Phil Trans R Soc A, April 13, 2009; 367(1892): 1251 - 1263. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M Di Rienzo, G Parati, A Radaelli, and P Castiglioni Baroreflex contribution to blood pressure and heart rate oscillations: time scales, time-variant characteristics and nonlinearities Phil Trans R Soc A, April 13, 2009; 367(1892): 1301 - 1318. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Cerutti, D. Hoyer, and A. Voss Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation Phil Trans R Soc A, April 13, 2009; 367(1892): 1337 - 1358. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Keissar, L. R Davrath, and S. Akselrod Coherence analysis between respiration and heart rate variability using continuous wavelet transform Phil Trans R Soc A, April 13, 2009; 367(1892): 1393 - 1406. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Bauer, J. Mehilli, P. Barthel, A. Muller, A. Kastrati, K. Ulm, A. Schomig, M. Malik, and G. Schmidt Impact of Myocardial Salvage Assessed by 99mTc-Sestamibi Scintigraphy on Cardiac Autonomic Function in Patients Undergoing Mechanical Reperfusion Therapy for Acute Myocardial Infarction J. Am. Coll. Cardiol. Img., April 1, 2009; 2(4): 449 - 457. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. D. Flouris and S. S. Cheung Influence of thermal balance on cold-induced vasodilation J Appl Physiol, April 1, 2009; 106(4): 1264 - 1271. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. M. CARNEY and K. E. FREEDLAND Depression and heart rate variability in patients with coronary heart disease Cleveland Clinic Journal of Medicine, April 1, 2009; 76(Suppl_2): S13 - S17. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. C. Fang, J. M. Cavallari, E. A. Eisen, J.-C. Chen, M. A. Mittleman, and D. C. Christiani Vascular Function, Inflammation, and Variations in Cardiac Autonomic Responses to Particulate Matter Among Welders Am. J. Epidemiol., April 1, 2009; 169(7): 848 - 856. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. P. Fisher, S. Ogoh, C. Junor, A. Khaja, M. Northrup, and P. J. Fadel Spontaneous baroreflex measures are unable to detect age-related impairments in cardiac baroreflex function during dynamic exercise in humans Exp Physiol, April 1, 2009; 94(4): 447 - 458. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. A. J. L. Scheer, M. F. Hilton, C. S. Mantzoros, and S. A. Shea From the Cover: Adverse metabolic and cardiovascular consequences of circadian misalignment PNAS, March 17, 2009; 106(11): 4453 - 4458. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. A. Whitsel, P. M. Quibrera, S. L. Christ, D. Liao, R. J. Prineas, G. L. Anderson, and G. Heiss Heart Rate Variability, Ambient Particulate Matter Air Pollution, and Glucose Homeostasis: The Environmental Epidemiology of Arrhythmogenesis in the Women's Health Initiative Am. J. Epidemiol., March 15, 2009; 169(6): 693 - 703. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Cruz, J. Sousa, A. G. Oliveira, and L. Silva-Carvalho Effects of endoscopic thoracic sympathectomy for primary hyperhidrosis on cardiac autonomic nervous activity. J. Thorac. Cardiovasc. Surg., March 1, 2009; 137(3): 664 - 669. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. K. Park, K. L Tucker, M. S O'Neill, D. Sparrow, P. S Vokonas, H. Hu, and J. Schwartz Fruit, vegetable, and fish consumption and heart rate variability: the Veterans Administration Normative Aging Study Am. J. Clinical Nutrition, March 1, 2009; 89(3): 778 - 786. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Bai, R. T. Mahon, J. C. White, P. R. Brink, and K. H. Chon Impairment of the autonomic nervous function during decompression sickness in swine J Appl Physiol, March 1, 2009; 106(3): 1004 - 1009. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. J. Rashba, G. A. Lamas, J.-P. Couderc, S. M. Hollist, V. Dzavik, W. Ruzyllo, V. Fridrich, C. E. Buller, S. A. Forman, J. A. Kufera, et al. Electrophysiological Effects of Late Percutaneous Coronary Intervention for Infarct-Related Coronary Artery Occlusion: The Occluded Artery Trial-Electrophysiological Mechanisms (OAT-EP) Circulation, February 17, 2009; 119(6): 779 - 787. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. A. Gray, K. Rylander, N. A. Harrison, B. G. Wallin, and H. D. Critchley Following One's Heart: Cardiac Rhythms Gate Central Initiation of Sympathetic Reflexes J. Neurosci., February 11, 2009; 29(6): 1817 - 1825. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Tamaki, T. Yamada, Y. Okuyama, T. Morita, S. Sanada, Y. Tsukamoto, M. Masuda, K. Okuda, Y. Iwasaki, T. Yasui, et al. Cardiac Iodine-123 Metaiodobenzylguanidine Imaging Predicts Sudden Cardiac Death Independently of Left Ventricular Ejection Fraction in Patients With Chronic Heart Failure and Left Ventricular Systolic Dysfunction Results From a Comparative Study With Signal-Averaged Electrocardiogram, Heart Rate Variability, and QT Dispersion. J. Am. Coll. Cardiol., February 3, 2009; 53(5): 426 - 435. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Milliez, S. Messaoudi, J. Nehme, C. Rodriguez, J.-L. Samuel, and C. Delcayre Beneficial effects of delayed ivabradine treatment on cardiac anatomical and electrical remodeling in rat severe chronic heart failure Am J Physiol Heart Circ Physiol, February 1, 2009; 296(2): H435 - H441. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Buchheit, J. J. Peiffer, C. R. Abbiss, and P. B. Laursen Effect of cold water immersion on postexercise parasympathetic reactivation Am J Physiol Heart Circ Physiol, February 1, 2009; 296(2): H421 - H427. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. T Mainardi On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods Phil Trans R Soc A, January 28, 2009; 367(1887): 255 - 275. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Voss, S. Schulz, R. Schroeder, M. Baumert, and P. Caminal Methods derived from nonlinear dynamics for analysing heart rate variability Phil Trans R Soc A, January 28, 2009; 367(1887): 277 - 296. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Le Gallais Comment on Point:Counterpoint: Sickle cell trait should/should not be considered asymptomatic and as a benign condition during physical activity J Appl Physiol, January 1, 2009; 106(1): 349 - 349. [Full Text] [PDF]
|
|
|
|
 |
|
 J.-P. Couderc, S. Kaab, M. Hinterseer, S. McNitt, X. Xia, A. Fossa, B. M. Beckmann, S. Polonsky, and W. Zareba Baseline Values and Sotalol-Induced Changes of Ventricular Repolarization Duration, Heterogeneity, and Instability in Patients With a History of Drug-Induced Torsades de Pointes J. Clin. Pharmacol., January 1, 2009; 49(1): 6 - 16. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Grant and P. Rainville Pain Sensitivity and Analgesic Effects of Mindful States in Zen Meditators: A Cross-Sectional Study Psychosom Med, January 1, 2009; 71(1): 106 - 114. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. R. Burton, I. Birznieks, P. S. Bolton, L. A. Henderson, and V. G. Macefield Effects of deep and superficial experimentally induced acute pain on muscle sympathetic nerve activity in human subjects J. Physiol., January 1, 2009; 587(1): 183 - 193. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. G. Cosío, J.#x. Palacios, A.#x.;n Pastor, and A. Núñez CHAPTER 2 The Electrocardiogram ESC Textbook of Cardiovascular Medicine, January 1, 2009; 2(1): med-9780199566990-chapter - med-9780199566990-chapter. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. L.S. Ljungman, N. Berglind, C. Holmgren, F. Gadler, N. Edvardsson, G. Pershagen, M. Rosenqvist, B. Sjogren, and T. Bellander Rapid effects of air pollution on ventricular arrhythmias Eur. Heart J., December 1, 2008; 29(23): 2894 - 2901. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. M. M. Licht, E. J. C. de Geus, F. G. Zitman, W. J. G. Hoogendijk, R. van Dyck, and B. W. J. H. Penninx Association Between Major Depressive Disorder and Heart Rate Variability in the Netherlands Study of Depression and Anxiety (NESDA) Arch Gen Psychiatry, December 1, 2008; 65(12): 1358 - 1367. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Van de Louw, C. Medigue, Y. Papelier, and F. Cottin Breathing cardiovascular variability and baroreflex in mechanically ventilated patients Am J Physiol Regulatory Integrative Comp Physiol, December 1, 2008; 295(6): R1934 - R1940. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Ketels, R. Houben, K. Van Beeumen, R. Tavernier, and M. Duytschaever Incidence, timing, and characteristics of acute changes in heart rate during ongoing circumferential pulmonary vein isolation Europace, December 1, 2008; 10(12): 1406 - 1414. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. P. Tan, J. L. Newton, T. J. Chadwick, and S. W. Parry The relationship between carotid sinus hypersensitivity, orthostatic hypotension, and vasovagal syncope: a case-control study Europace, December 1, 2008; 10(12): 1400 - 1405. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Schonauer, A. Thomas, S. Morbach, J. Niebauer, U. Schonauer, and H. Thiele Cardiac autonomic diabetic neuropathy Diabetes and Vascular Disease Research, November 1, 2008; 5(4): 336 - 344. [Abstract] [PDF]
|
|
|
|
 |
|
 S-S. Chiou and C-D. Kuo Effect of chewing a single betel-quid on autonomic nervous modulation in healthy young adults J Psychopharmacol, November 1, 2008; 22(8): 910 - 917. [Abstract] [PDF]
|
|
|
|
|
|
M. R. Bhattacharyya, D. L. Whitehead, R. Rakhit, and A. Steptoe Depressed Mood, Positive Affect, and Heart Rate Variability in Patients With Suspected Coronary Artery Disease Psychosom Med, November 1, 2008; 70(9): 1020 - 1027. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. U. Viola, L. M. James, S. N. Archer, and D.-J. Dijk PER3 polymorphism and cardiac autonomic control: effects of sleep debt and circadian phase Am J Physiol Heart Circ Physiol, November 1, 2008; 295(5): H2156 - H2163. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. A. Brown, C. T. Lee, S. M. J. Hains, and B. S. Kisilevsky Maternal Heart Rate Variability and Fetal Behavior in Hypertensive and Normotensive Pregnancies Biol Res Nurs, October 1, 2008; 10(2): 134 - 144. [Abstract] [PDF]
|
|
|
|
|
|
J. A. Moffitt, A. J. Grippo, T. G. Beltz, and A. K. Johnson Hindlimb unloading elicits anhedonia and sympathovagal imbalance J Appl Physiol, October 1, 2008; 105(4): 1049 - 1059. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L.-F. Zhang, J.-H. Cheng, X. Liu, S. Wang, Y. Liu, H.-B. Lu, and J. Ma Cardiovascular changes of conscious rats after simulated microgravity with and without daily -Gx gravitation J Appl Physiol, October 1, 2008; 105(4): 1134 - 1145. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Malik, K. Hnatkova, J. Ford, and D. Madge Near-Thorough QT Study as Part of a First-In-Man Study J. Clin. Pharmacol., October 1, 2008; 48(10): 1146 - 1157. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. J. Goldberger, M. E. Cain, S. H. Hohnloser, A. H. Kadish, B. P. Knight, M. S. Lauer, B. J. Maron, R. L. Page, R. S. Passman, D. Siscovick, et al. American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society Scientific Statement on Noninvasive Risk Stratification Techniques for Identifying Patients at Risk for Sudden Cardiac Death: A Scientific Statement From the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention J. Am. Coll. Cardiol., September 30, 2008; 52(14): 1179 - 1199. [Full Text] [PDF]
|
|
|
|
|
|
J. J. Goldberger, M. E. Cain, S. H. Hohnloser, A. H. Kadish, B. P. Knight, M. S. Lauer, B. J. Maron, R. L. Page, R. S. Passman, D. Siscovick, et al. American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society Scientific Statement on Noninvasive Risk Stratification Techniques for Identifying Patients at Risk for Sudden Cardiac Death: A Scientific Statement From the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention Circulation, September 30, 2008; 118(14): 1497 - 1518. [Full Text] [PDF]
|
|
|
|
|
|
V. K. Somers, D. P. White, R. Amin, W. T. Abraham, F. Costa, A. Culebras, S. Daniels, J. S. Floras, C. E. Hunt, L. J. Olson, et al. Sleep Apnea and Cardiovascular Disease: An American Heart Association/American College of Cardiology Foundation Scientific Statement From the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing In Collaboration With the National Heart, Lung, and Blood Institute National Center on Sleep Disorders Research (National Institutes of Health) Circulation, September 2, 2008; 118(10): 1080 - 1111. [Full Text] [PDF]
|
|
|
|
 |
|
 B de la Cruz Torres, C L. Lopez, and J N. Orellana Analysis of heart rate variability at rest and during aerobic exercise: a study in healthy people and cardiac patients Br. J. Sports Med., September 1, 2008; 42(9): 715 - 720. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L Bosquet, S Merkari, D Arvisais, and A E Aubert Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature Br. J. Sports Med., September 1, 2008; 42(9): 709 - 714. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||