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European Heart Journal

(1996) 17, 354-381

Guidelines

Heart rate variability

Standards of measurement, physiological interpretation, and

clinical use

Task Force of The European Society of Cardiology and The North American

Society of Pacing and Electrophysiology (Membership of the Task Force listed in

the Appendix)

Introduction

The last two decades have witnessed the recognition of a

significant relationship between the autonomic nervous

system and cardiovascular mortality, including sudden

cardiac death'

. Experimental evidence for an associ-

ation between a propensity for lethal arrhythmias and

signs of either increased sympathetic or reduced vagal

activity has encouraged the development of quantitative

markers of autonomic activity.

Heart rate variability (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 automated

measurement of HRV, the cardiologist has been pro-

vided with a seemingly simple tool for both research and

clinical studies'

. 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

Key Words:

Heart rate, electrocardiograph^, computers,

autonomic nervous system, risk factors.

The Task Force was established by the Board of the European

Society of Cardiology and co-sponsored by the North American

Society of Pacing and Electrophysiology. It was organised jointly

by the Working Groups on Arrhythmias and on Computers of

Cardiology of the European Society of Cardiology. After ex-

changes of written views on the subject, the main meeting of a

writing core of the Task Force took place on May 8-10, 1994, on

Necker Island. Following 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.

Published simultaneously in

Circulation.

Correspondence

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.

0195-668X/96/030354 + 28 $18.00/0

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: standardize nomenclature and develop

definitions of terms; specify standard methods of

measurement; define physiological and pathophysio-

logical correlates; describe currently appropriate clinical

applications, and identify areas for future research.

In order to achieve these goals, the members of

the Task Force were drawn from the fields of mathemat-

ics, engineering, physiology, and clinical medicine. The

standards and proposals offered in this text should

not limit further development but, rather, 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

heart beats as well as the oscillations between consecu-

tive instantaneous heart rates. 'Heart Rate Variability'

has become the conventionally accepted term to describe

variations of both instantaneous heart rate and RR

intervals. In order to describe oscillation in consecutive

cardiac cycles, other terms have been used in the litera-

ture, 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

analysed rather than the heart rate per se. However,

these terms have not gained as wide acceptance as HRV,

thus we will use the term HRV in this document.

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Background

The clinical relevance of heart rate variability was first

appreciated in 1965 when Hon and Lee'

' noted that fetal

distress was preceded by alterations in interbeat intervals

before any appreciable change occurred in the heart rate

itself. Twenty years ago, Sayers and others focused

attention on the existence of physiological rhythms

imbedded in the beat-to-beat heart rate signal'

101

1996 American Heart Association Inc.; European Society of Cardiology

Standards of heart rate variability 355

During the 1970s, Ewing

a/.

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 post-infarction mortality

with reduced HRV was first shown by Wolf

et al.

1977

[12)

. In 1981, Akselrod

et al.

introduced power

spectral analysis of heart rate fluctuations to quantita-

tively evaluate beat-to-beat cardiovascular control'

131

These frequency-domain analyses contributed to

the understanding of the autonomic background of RR

interval fluctuations in the heart rate record'

151

. The

clinical importance of HRV became apparent in the late

1980s when it was confirmed that HRV was a strong and

independent predictor of mortality following an acute

myocardial infarction'

181

. With the availability of new,

digital, high frequency, 24-h multi-channel electro-

cardiographic recorders, HRV has the potential to

provide additional valuable insight into physiological

and pathological conditions and to enhance risk

stratification.

Measurement of heart rate variability

Time domain methods

Variations in heart rate may be evaluated by a number

of methods. Perhaps the simplest to perform are the time

domain measures. With these methods either the heart

rate at any point in time or the intervals between

successive normal complexes are determined. In a con-

tinuous electrocardiographic (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 depolariza-

tions), 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, etc.

Other time-domain measurements that can be used are

variations in instantaneous heart rate secondary to

respiration, tilt, Valsalva manoeuvre, or secondary to

phenylephrine infusion. These differences can be de-

scribed 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 h, more complex

statistical

time-domain measures

can be calculated. These may be

divided into two classes, (a) those derived from direct

measurements of the NN intervals or instantaneous

heart rate, and (b) those derived from the differences

between NN intervals. These variables may be derived

from analysis of the total electrocardiographic 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, e.g. rest,

sleep, etc.

The simplest variable to calculate is the

standard

deviation of the NN interval

(SDNN), i.e. 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 calcu-

lated over a 24-h period and thus encompasses both

short-term high frequency variations, as well as the

lowest frequency components seen in a 24-h period. As

the period of monitoring decreases, SDNN estimates

shorter and shorter cycle lengths. It should also be noted

that the total variance of HRV increases with the length

of analysed recording

1191

. Thus, on arbitrarily selected

ECGs, SDNN is not a well defined statistical quantity

because of its dependence on the length of recording

period. Thus, in practice, it is inappropriate to compare

SDNN measures obtained from recordings of different

durations. However, 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-min recordings and

nominal 24 h long-term recordings seem to be

appropriate options.

Other commonly used statistical variables calcu-

lated from segments of the total monitoring period

include

SDANN,

the standard deviation of the average

NN interval calculated over short periods, usually 5 min,

which is an estimate of the changes in heart rate due to

cycles longer than 5 min, and the

SDNN index,

the mean

of the 5-min standard deviation of the NN interval

calculated over 24 h, which measures the variability due

to cycles shorter than 5 min.

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

pNNSO

the pro-

portion derived by dividing NN50 by the total number

of NN intervals. All these measurements of short-term

variation estimate high frequency variations in heart

rate and thus are highly correlated (Fig. 1).

Geometrical methods

The series of NN intervals can also be converted into a

geometric pattern,

such as the sample density distribu-

tion of NN interval durations, sample density distribu-

tion of differences between adjacent NN intervals,

Lorenz plot of NN or RR intervals, etc., and a simple

formula is used which judges the variability based on the

geometric and/or graphic properties of the resulting

pattern. Three general approaches are used in geometric

methods: (a) a basic measurement of the geometric

pattern (e.g. the width of the distribution histogram at

the specified level) is converted into the measure of

HRV, (b) the geometric pattern is interpolated by a

mathematically defined shape (e.g. 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 (c) the geometric shape is classified into several

Eur Heart J, Vol. 17, March 1996

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356

Task Force

100,

0.1

0.01

0.001 '

RMSSD(ms)

100

120

100 000

(b)

10 000

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1000

100

0.001

0.01

0.1

100

pNN50C7

)

Figure 1

Relationship between the RMSSD and pNN50 (a), and pNN50 and

NN50 (b) measures of HR V assessed from 857 nominal 24-h Holter tapes recorded

in survivors of acute myocardial infarction prior to hospital discharge. The NN50

measure used in panel (b) was normalized in respect to the length of the recording

(Data of St. George's Post-infarction Research Survey Programme.)

pattern-based categories which represent different

classes of HRV (e.g. elliptic, linear and triangular shapes

of Lorenz plots). Most geometric methods require the

(or NN) interval sequence to be measured on or

converted to a discrete scale which is not too fine or too

coarse and which permits the construction of smoothed

histograms. Most experience has been obtained with

bins approximately 8 ms long (precisely 7-8125 ms =

1/128 s) which corresponds to the precision of current

commercial equipment.

The

HRV triangular index

measurement is the

integral of the density distribution (i.e. the number of all

Eur Heart J, Vol. 17, March 1996

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, i.e. 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 to the

most frequent sampling of 128 Hz, the size of the bins

should be quoted. The

triangular interpolation of NN

Standards of heart rate variability 357

- V

Sample

density

distribution

Duration of normal RR intervals

Figure 2

To perform geometrical measures on the NN interval

histogram, the sample density distribution D is constructed which

assigns the number of equally long NN intervals to each value of

their lengths. The most frequent NN interval length

is established,

that is

Y= D(X)

is the maximum of the sample density distribution

D. The HRV triangular index is the value obtained by dividing the

area integral of D by the maximum

When constructing the

distribution D with a discrete scale on the horizontal axis, the value

is obtained according to the formula

HRV index = (total number of all NN intervals)/

For the computation of the TINN measure, the values

and

are established on the time axis and a multilinear function q

constructed such that q(?)=0 for

<,N

and

t^M

and

q(X)= Y,

and

such that the integral

}

0 + c

°(D(t)-q(t))

is the minimum among all selections of all values /V and

The

TINN measure is expressed in ms and given by the formula

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interval histogram

(TINN) is the baseline width of the

distribution measured as a base of a triangle, approxi-

mating the NN interval distribution (the minimum

square difference is used to find such a triangle). Details

of computing the HRV triangular index and TINN are

shown in Fig. 2. Both these measures express overall

HRV measured over 24 h and are more influenced by

the lower than by the higher frequencies'

171

. Other

geometric methods are still in the phase of exploration

and explanation.

The major advantage of geometric methods lies

in their relative insensitivity to the analytical quality of

the series of NN intervals'

201

. The major disadvantage is

the need for a reasonable number of NN intervals to

construct the geometric pattern. In practice, recordings

of at least 20 min (but preferably 24 h) should be used

to ensure the correct performance of the geometric

methods, i.e. the current geometric methods are in-

appropriate to assess short-term changes in

HRV.

Summary and recommendations

The variety of time-domain measures of HRV is sum-

marized in Table 1. Since many of the measures correlate

closely with others, the following four are recommended

for time-domain HRV assessment: SDNN (estimate of

overall HRV); HRV triangular index (estimate of overall

HRV); SDANN (estimate of long-term components of

HRV), and RMSSD (estimate of short-term compo-

nents of HRV). Two estimates of the overall HRV are

recommended because the HRV triangular index

permits only casual pre-processing 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 method selected should correspond to the

aim of each study. Methods that might be recommended

for clinical practices are summarized in the Section

entitled Clinical use of heart rate variability.

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

the Section on Recording requirements together with

suggestions related to the frequency analysis of HRV.

Frequency domain methods

Various spectral methods'

' for the analysis of the

tachogram have been applied since the late 1960s. Power

Eur Heart J, Vol. 17, March 1996

358

Task Force

Table 1 Selected time-domain measures of HR V

Variable

Units

Statistical measures

SDNN

SDANN

RMSSD

SDNN index

SDSD

NN50 count

Standard deviation of all NN intervals.

Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording

The square root of the mean of the sum of the squares of differences between adjacent NN

intervals.

Mean of the standard deviations of all NN intervals for all 5 min segments of the entire recording

Standard deviation of differences between adjacent NN intervals.

Number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording.

Three variants are possible counting all such NN intervals pairs or only pairs in which the first or

the second interval is longer.

NN50 count divided by the total number of all NN intervals.

Geometric measures

HRV triangular index

TINN

Differential index

Logarithmic index

Total number of all NN intervals divided by the height of the histogram of all NN intervals

measured on a discrete scale with bins of 7-8125 ms (1/128 s) (Details in Fig. 2)

Baseline width of the minimum square difference triangular interpolation of the highest peak of the

histogram of all NN intervals (Details in Fig. 2.)

Difference between the widths of the histogram of differences between adjacent NN intervals

measured at selected heights (e.g. at the levels of 1000 and 10 000 samples)

Coefficient

of the negative exponential curve

k • e~

which is the best approximation of the

histogram of absolute differences between adjacent NN intervals

1221

Description

pNN50

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spectral density (PSD) analysis provides the basic infor-

mation of how power (i.e. variance) distributes as a

function of frequency. Independent of the method

employed, only an estimate of the true PSD of the

signals can be obtained by proper mathematical

algorithms.

Methods for the calculation of PSD may be

generally classified as

non-parametric

and

parametric.

most instances, both methods provide comparable

results. The advantages of the non-parametric methods

are: (a) the simplicity of the algorithm employed (Fast

Fourier Transform — FFT — in most of the cases) and

(b) the high processing speed, whilst the advantages of

parametric methods are: (a) smoother spectral compo-

nents which can be distinguished independently of pre-

selected frequency bands, (b) easy post-processing of the

spectrum with an automatic calculation of low and high

frequency power components and easy identification of

the central frequency of each component, and (c) 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 to verify the suitability of the

chosen model and its complexity (i.e. 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 min'

7101315-24]

: very low fre-

quency (VLF), low frequency (LF), and high frequency

(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

Eur Heart J, Vol. 17, March 1996

the heart period

24-25

l 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 non-harmonic component which does not have

coherent properties and which 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 (e.g.

5 min) is a dubious meas-

ure and should be avoided when interpreting the PSD of

short-term ECGs.

Measurement of VLF, LF and HF power com-

ponents is usually made in absolute values of power

(ms

), but LF and HF may also be measured in normal-

ized units (n.u.)

[l5

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 n.u. emphasizes the controlled and

balanced behaviour of the two branches of the auto-

nomic nervous system. Moreover, normalization tends

to minimize the effect on the values of LF and HF

components of the changes in total power (Fig. 3).

Nevertheless, n.u. should always be quoted with abso-

lute values of LF and HF power in order to describe in

total the distribution of power in spectral components.

Long-term recordings

Spectral analysis may also be

used to analyse the sequence of NN intervals in the

entire 24-h period. The result then includes an ultra-low

frequency component (ULF), in addition to VLF, LF

and HF components. The slope of the 24-h spectrum can

also be assessed on a log-log scale by linear fitting the

spectral values. Table 2 lists selected frequency-domain

measures.

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