Heart Rate Variability

July 2, 2021, by Naveen Acharya, MD, FACC

Introduction:

Summarized from: Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology Originally published 1 Mar 1996

What is Heart Rate Variability?

  • “Heart rate variability” is the term to describe variations of both instantaneous heart rate and RR intervals ie. the oscillation in the interval between consecutive heartbeats as well as the oscillations between consecutive instantaneous heart rates.
  • 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
  • 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 (heart attack).

Source: Adv Physiol Educ 43: 270–276, 2019; doi:10.1152/advan.00019.2019

Measuring HRV

1. Statistical Methods:

SDNN

  • 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.
  • 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.

  • Apple Health uses the SDNN method to calculate HRV from the Apple Watch.
    SDNN is more accurate over a 24 h recording rather than short time intervals.
  • Hernando et al. 3 validated the Apple Watch’s reliability for HRV measurements.
  • They noted the existence in Apple Watch measurements of missing beats.
  • Around 10% of the beats could not be detected by the Apple Watch in both the relaxed and stress states.
  • This could be problematic because they usually were consecutive beats, which lead to gaps in the RR series.
  • The shorter gaps were longer than 3 s, being easily identifiable as abnormally large RR intervals.
  • The origin of these gaps could be varied, from bad skin contact to fast arm movement
  • Kushal et al. 4 also reported missing values in the Apple Watch, with the proportion of heart rate values actually measured by the Apple Watch decreasing with increasing exercise intensity.
  • They also reported that these missing heart rate values were higher in the first minute of exercise (between 20 and 40% of RR intervals), particularly at higher intensities of exercise. In our data, however, we did not find more missing beats in the stress stage compared to the relax stage, and the total missing RR intervals was about 10% of total intervals.

SDANN

Calculated from segments of the total monitoring period, 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.

RMSSD


Derived from interval differences RMSSD is the square root of the mean squared differences of successive NN intervals. Used by Fitbit devices.

NN50 and pNN50

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.

2. 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.

The HRV triangular index

This 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.

The triangular interpolation of NN interval histogram (TINN)

This 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).

To perform geometric 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 X 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 Y.
For the computation of the TINN measure, the values N and M are established on the time axis and a multilinear function q constructed such that q(t)=0 for t≤N and t≥M and q(X)=Y, and such that the integral ∫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 1.

Variable

Units

Description

Statistical Measures

SDNN

ms

Standard deviation of all NN intervals

SDANN

ms

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

RMSSD

ms

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

SDNN index

ms

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

SDSD

ms

Standard deviation of differences between adjacent NN intervals

NN50 count

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

pNN50

%

NN50 count divided by the total number of all NN intervals

Geometric Measures

HRV triangular 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 seconds) (details in Fig 2)

TINN

ms

Baseline width of the minimum square difference triangular interpolation of the highest peak of the histogram of all NN intervals (details in Fig 2)

Differential index

ms

Difference between the widths of the histogram of differences between adjacent NN intervals measured at selected heights (eg, at the levels of 1000 and 10 000 samples)20

Logarithmic index

ms

Coefficient φ of the negative exponential curve k · e−φt, which is the best approximation of the histogram of absolute differences between adjacent NN intervals21

3. Frequency Domain Methods:

  • Power spectral density (PSD) analysis provides the basic information of how power (variance) distributes as a function of frequency.
  • 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.
  • 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 (LF and HF) 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).
  • 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. 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 different) provide averages of the modulations attributable to the LF and HF components. Such averages obscure the detailed information about autonomic modulation of RR intervals that is available in shorter recordings.
  • 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, and averages of modulations do not represent an averaged level of tone.


Spectral analysis (autoregressive model, order 12) of RR interval variability in a healthy subject at rest and during 90° head-up tilt. At rest, two major components of similar power are detectable at low and high frequencies. During tilt, the LF component becomes dominant, but as total variance is reduced, the absolute power of LF appears unchanged compared with rest. Normalization procedure leads to predominant LF and smaller HF components, which express the alteration of spectral components due to tilt

Variable

Units

Description

Frequency Range

Analysis of Short-term Recordings (5 min)

5-min total power

ms2

The variance of NN intervals over the temporal segment

≈≤0.4 Hz

VLF

ms2

Power in VLF range

≤0.04 Hz

LF

ms2

Power in LF range

0.04-0.15 Hz

LF norm

nu

LF power in normalized units LF/(total power−VLF)×100

HF

ms2

Power in HF range

0.15-0.4 Hz

HF norm

nu

HF power in normalized units HF/(total power−VLF)×100

LF/HF

Ratio LF [ms2]/HF[ms2]

Analysis of Entire 24 Hours

Total power

ms2

Variance of all NN intervals

≈≤0.4 Hz

ULF

ms2

Power in the ULF range

≤0.003 Hz

VLF

ms2

Power in the VLF range

0.003-0.04 Hz

LF

ms2

Power in the LF range

0.04-0.15 Hz

HF

ms2

Power in the HF range

0.15-0.4 Hz

α

Slope of the linear interpolation of the spectrum in a log-log scale

≈≤0.04 Hz

Normal Values of Standard Measures of HRV

Variable

Units

Normal Values (mean±SD)

Time Domain Analysis of Nominal 24 hours181

SDNN

ms

141±39

SDANN

ms

127±35

RMSSD

ms

27±12

HRV triangular index

37±15

Spectral Analysis of Stationary Supine 5-min Recording

Total power

ms2

3466 ±1018

LF

ms2

1170±416

HF

ms2

975±203

LF

nu

54±4

HF

nu

29±3

LF/HF ratio

1.5-2.0

Summary of Selected Studies Investigating Clinical Value of HRV in Cardiological Diseases Other Than Myocardial Infarction

Disease State

Author of Study

Population (No. of Patients)

Investigation Parameter

Clinical Finding

Potential Value

Hypertension

Guzzetti et al, 1991149

49 hypertensive 30 normals

Spectral AR

⇑LF found in hypertensives compared with normals with blunting of circadian patterns

Hypertension is characterized by a depressed circadian rhythmicity of LF

Langewitz et al, 1994 150

41 borderline hypertensive 34 hypertensive 54 normals

Spectral FFT

Reduced parasympathetic in hypertensive patients

Support the use of nonpathological therapy of hypertension that ⇑ vagal tone (eg, exercise)

CHF

Saul et al, 1988151

25 chronic CHF NYHA III,IV 21 normals

Spectral Blackman-Tukey 15-min acquisition

⇓Spectral power all frequencies, especially >0.04 Hz in CHF patients

In CHF, there is ⇓ vagal but relatively preserved sympathetic modulation of HR

Casolo et al, 1989102

20 CHF NYHA II,III,IV 20 normals

Time domain RR interval histogram with 24-h Holter

Low HRV

Reduced vagal activity in CHF patients

Binkley et al, 1991152

10 dilated cardiomyopathy (EF 14% to 40%) 10 normals

Spectral FFT 4-min supine acquisition

⇓HF power (>0.1 Hz) in CHF ⇑LF/HF

Withdrawal of parasympathetic tone observed in CHF. CHF has imbalance of autonomic tone with ⇓ parasympathetic and a predominance of sympathetic tone

Kienzle et al, 1992104

23 CHF NYHA II,III,IV

Spectral FFT Time domain 24-48-h Holter

Alterations of HRV not tightly linked to severity of CHF ⇓HRV was related to sympathetic excitation

Townend et al, 1992153

12 CHF NYHA III,IV

Time domain 24-h Holter

HRV⇑ during ACE inhibitor treatment

Binkley et al, 1993154

13 CHF NYHA II,III

Spectral FFT 4-min supine acquisition

12 weeks of ACE inhibitor treatment ⇑HF HRV

Significant augmentation of parasympathetic tone was associated with ACE inhibitor therapy

Woo et al, 1994155

21 CHF NYHA III

Poincaré plots Time domain 24-h Holter

Complex plots are associated with ⇑norepinephrine levels and greater sympathetic activation

Poincaré plots may assist analysis of sympathetic influences

Heart transplantation

Alexopoulos et al, 1988156

19 transplant 10 normals

Time domain 24-h Holter

Reduced HRV in denervated donor hearts; recipient innervated hearts had more HRV

Sands et al, 1989100

17 transplant 6 normals

Spectral FFT 15-min supine acquisition

HRV from 0.02 to 1.0 Hz; 90% reduced

Patients with rejection documented biopsy show significantly more variability

Chronic mitral regurgitation

Stein et al, 1993157

38 chronic mitral regurgitation

Spectral FFT Time domain 24-h Holter

HR and measures of ULF by SDANN correlated with ventricular performance and predicted clinical events

May be prognostic indicator of atrial fibrillation, mortality, and progression to valve surgery

Mitral valve prolapse

Marangoni et al, 1993158

39 female mitral valve prolapse 24 female controls

Spectral AR 10-min supine acquisition

MVP patients had ⇓HF

MVP patients had low vagal tone

Cardiomyopathies

Counihan et al, 1993159

104 HCM

Spectral FFT Time domain 24-h Holter

Global and specific vagal tone measurements of HRV were ⇓ in symptomatic patients

HRV does not add to the predictive accuracy of known risk factors in HCM

SD or CA

Dougherty et al, 1992160

16 CA survivors 5 CA nonsurvivors 5 normals

Spectral AR Time domain 24-h Holter

HRV as measured by LF power and SDNN were significantly related to 1-y mortality

HRV is clinically useful to risk stratify CA survivors for 1-y mortality

Huikuri et al, 1992161

22 CA survivors 22 control

Spectral AR Time domain 24-h Holter

⇓HF power in CA survivors; LF power did not discriminate CA survivors Circadian pattern of HRV found in all patients

Algra et al, 1993110

193 SD cases 230 symptomatic patients

Time domain 24-h Holter

⇓Short-term variation (0.05-0.50 Hz) independently increased the risk of SD by a factor of 2.6 ⇓Long-term variation (0.02-0.05 Hz) increased the risk of SD by a factor of 2

HRV may be used to estimate the risk of SD

ACE indicates angiotensin-converting enzyme; AR, autoregressive; CA, cardiac arrest; CAD, coronary artery disease; CHF, congestive heart failure; EF, ejection fraction; FFT, fast Fourier transform; HCM, hypertrophic cardiomyopathy; HF, high frequency; HRV heart rate variability; LF, low frequency; MVP, mitral valve prolapse; NYHA, New York Heart Association classification; SD, sudden death; SVT, supraventricular tachycardia; VF, ventricular fibrillation; and VT, ventricular tachycardia.

Continued

Disease State

Author of Study

Population (No. of Patients)

Investigation Parameter

Clinical Finding

Potential Value

Myers et al, 1986162

6 normals 12 patients with structural heart disease (6 with and 6 without SD)

Time and frequency domain 24-h Holter

Both time and frequency domain indices separated normals from SD patients ⇓HF power (0.35-0.5 Hz) was the best separator between heart disease patients with and without SD

HF power may be a useful predictor of SD

Martin et al, 1986163

20 normals 5 patients experiencing SD during Holter monitoring

Time domain 24-h Holter

SDNN index significantly lower in SD patients

Time domain indices may identify increased risk of SD

Ventricular arrhythmias

Vybiral et al, 1993164

24 VF

Time domain 24-h Holter

HRV indices do not change consistently before VF

Huikuri et al, 1993165

18 VT or CA

Spectral AR 24-h Holter

All power spectra of HRV were significantly ⇓ before the onset of sustained VT than before nonsustained VT

A temporal relation exists between the decrease of HRV and the onset of sustained VT

Hohnloser et al, 1994166

14 post-MI with VF or sustained 14 post-MI (matched)

Spectral FFT Time domain 24-h Holter

HRV of post MI-CA survivors do not differ from other post MI patients; they differ strikingly in terms of baroreflex sensitivity

Baroreflex sensitivity, not HRV, distinguished post-MI patients with and without VF and VT

Supraventricular arrhythmias

Kocovic et al, 1993167

64 SVT

Spectral FFT Time domain 5-min supine acquisition 24-h Holter

⇑HR,⇓HRV, and ⇓parasympathetic components after radiofrequency ablation

Parasympathetic ganglia and fibers may be more dense in the mid and anterior low septum

References:

  • 1. Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology Originally published 1 Mar 1996 Circulation. 1996;93:1043–1065
  • 2. Adv Physiol Educ 43: 270–276, 2019; doi:10.1152/advan.00019.2019
  • 3. Hernando D, Roca S, Sancho J, Alesanco Á, Bailón R. Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects. Sensors (Basel). 2018;18(8):2619. Published 2018 Aug 10. doi:10.3390/s18082619
  • Khushhal A., Nichols S., Evans W., Gleadall-Siddall D., Page R., O’Doherty A., Carroll S., Ingle L., Abt G. Validity and reliability of the AppleWatch for measuring heart rate during exercise. Sports Med. Int. Open. 2017;1:206–211.

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