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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