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
Source: Adv Physiol Educ 43: 270–276, 2019; doi:10.1152/advan.00019.2019
SDNN
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 5minute 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 shortterm variation estimate highfrequency variations in heart rate and thus are highly correlated.
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 5minute 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 5minute 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 intervals^{21} 
Spectral analysis (autoregressive model, order 12) of RR interval variability in a healthy subject at rest and during 90° headup 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 Shortterm Recordings (5 min) 

5min total power 
ms^{2 } 
The variance of NN intervals over the temporal segment 
≈≤0.4 Hz 
VLF 
ms^{2 } 
Power in VLF range 
≤0.04 Hz 
LF 
ms^{2 } 
Power in LF range 
0.040.15 Hz 
LF norm 
nu 
LF power in normalized units LF/(total power−VLF)×100 

HF 
ms^{2 } 
Power in HF range 
0.150.4 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 
ms^{2 } 
Variance of all NN intervals 
≈≤0.4 Hz 
ULF 
ms^{2 } 
Power in the ULF range 
≤0.003 Hz 
VLF 
ms^{2 } 
Power in the VLF range 
0.0030.04 Hz 
LF 
ms^{2 } 
Power in the LF range 
0.040.15 Hz 
HF 
ms^{2 } 
Power in the HF range 
0.150.4 Hz 
α 
Slope of the linear interpolation of the spectrum in a loglog scale 
≈≤0.04 Hz 
Normal Values of Standard Measures of HRV
Variable 
Units 
Normal Values (mean±SD) 

Time Domain Analysis of Nominal 24 hours^{181} 

SDNN 
ms 
141±39 
SDANN 
ms 
127±35 
RMSSD 
ms 
27±12 
HRV triangular index 
37±15 

Spectral Analysis of Stationary Supine 5min Recording 

Total power 
ms^{2} 
3466 ±1018 
LF 
ms^{2} 
1170±416 
HF 
ms^{2} 
975±203 
LF 
nu 
54±4 
HF 
nu 
29±3 
LF/HF ratio 
1.52.0 
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, 1991^{149} 
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, 1988^{151} 
25 chronic CHF NYHA III,IV 21 normals 
Spectral BlackmanTukey 15min 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, 1989^{102} 
20 CHF NYHA II,III,IV 20 normals 
Time domain RR interval histogram with 24h Holter 
Low HRV 
Reduced vagal activity in CHF patients 

Binkley et al, 1991^{152} 
10 dilated cardiomyopathy (EF 14% to 40%) 10 normals 
Spectral FFT 4min 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, 1992^{104} 
23 CHF NYHA II,III,IV 
Spectral FFT Time domain 2448h Holter 
Alterations of HRV not tightly linked to severity of CHF ⇓HRV was related to sympathetic excitation 

Townend et al, 1992^{153} 
12 CHF NYHA III,IV 
Time domain 24h Holter 
HRV⇑ during ACE inhibitor treatment 

Binkley et al, 1993^{154} 
13 CHF NYHA II,III 
Spectral FFT 4min supine acquisition 
12 weeks of ACE inhibitor treatment ⇑HF HRV 
Significant augmentation of parasympathetic tone was associated with ACE inhibitor therapy 

Woo et al, 1994^{155 } 
21 CHF NYHA III 
Poincaré plots Time domain 24h 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, 1988^{156} 
19 transplant 10 normals 
Time domain 24h Holter 
Reduced HRV in denervated donor hearts; recipient innervated hearts had more HRV 

Sands et al, 1989^{100} 
17 transplant 6 normals 
Spectral FFT 15min 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, 1993^{157} 
38 chronic mitral regurgitation 
Spectral FFT Time domain 24h 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, 1993^{158} 
39 female mitral valve prolapse 24 female controls 
Spectral AR 10min supine acquisition 
MVP patients had ⇓HF 
MVP patients had low vagal tone 
Cardiomyopathies 
Counihan et al, 1993^{159} 
104 HCM 
Spectral FFT Time domain 24h 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, 1992^{160} 
16 CA survivors 5 CA nonsurvivors 5 normals 
Spectral AR Time domain 24h Holter 
HRV as measured by LF power and SDNN were significantly related to 1y mortality 
HRV is clinically useful to risk stratify CA survivors for 1y mortality 
Huikuri et al, 1992^{161} 
22 CA survivors 22 control 
Spectral AR Time domain 24h Holter 
⇓HF power in CA survivors; LF power did not discriminate CA survivors Circadian pattern of HRV found in all patients 

Algra et al, 1993^{110} 
193 SD cases 230 symptomatic patients 
Time domain 24h Holter 
⇓Shortterm variation (0.050.50 Hz) independently increased the risk of SD by a factor of 2.6 ⇓Longterm variation (0.020.05 Hz) increased the risk of SD by a factor of 2 
HRV may be used to estimate the risk of SD 
ACE indicates angiotensinconverting 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, 1986^{162 } 
6 normals 12 patients with structural heart disease (6 with and 6 without SD) 
Time and frequency domain 24h Holter 
Both time and frequency domain indices separated normals from SD patients ⇓HF power (0.350.5 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, 1986^{163} 
20 normals 5 patients experiencing SD during Holter monitoring 
Time domain 24h Holter 
SDNN index significantly lower in SD patients 
Time domain indices may identify increased risk of SD 

Ventricular arrhythmias 
Vybiral et al, 1993^{164} 
24 VF 
Time domain 24h Holter 
HRV indices do not change consistently before VF 

Huikuri et al, 1993^{165 } 
18 VT or CA 
Spectral AR 24h 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, 1994^{166} 
14 postMI with VF or sustained 14 postMI (matched) 
Spectral FFT Time domain 24h Holter 
HRV of post MICA survivors do not differ from other post MI patients; they differ strikingly in terms of baroreflex sensitivity 
Baroreflex sensitivity, not HRV, distinguished postMI patients with and without VF and VT 

Supraventricular arrhythmias 
Kocovic et al, 1993^{167} 
64 SVT 
Spectral FFT Time domain 5min supine acquisition 24h Holter 
⇑HR,⇓HRV, and ⇓parasympathetic components after radiofrequency ablation 
Parasympathetic ganglia and fibers may be more dense in the mid and anterior low septum 
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