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Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
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September 2017 | Volume 5 | Article 2581
REVIEW
published: 28 September 2017
doi: 10.3389/fpubh.2017.00258
Frontiers in Public Health | www.frontiersin.org
Edited by:
Joav Merrick,
Ministry of Social Affairs, Israel
Reviewed by:
Angela J. Grippo,
Northern Illinois University,
United States
Gillian Bartlett,
McGill University, Canada
*Correspondence:
Fred Shaffer
fredricshaffer@gmail.com
Specialty section:
This article was submitted to
Family Medicine and Primary Care,
a section of the journal
Frontiers in Public Health
Received: 26June2017
Accepted: 11September2017
Published: 28September2017
Citation:
ShafferF and GinsbergJP (2017)
An Overview of Heart Rate
Variability Metrics and Norms.
Front. Public Health 5:258.
doi: 10.3389/fpubh.2017.00258
An Overview of Heart Rate Variability
Metrics and Norms
Fred Shaffer1* and J. P. Ginsberg2
1 Center for Applied Psychophysiology, Truman State University, Kirksville, MO, United States,
2 William Jennings Bryan Dorn VA Medical Center (VHA), Columbia, SC, United States
Healthy biological systems exhibit complex patterns of variability that can be described by
mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals
between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not
a metronome. The oscillations of a healthy heart are complex and constantly changing,
which allow the cardiovascular system to rapidly adjust to sudden physical and psycho-
logical challenges to homeostasis. This article briefly reviews current perspectives on
the mechanisms that generate 24h, short-term (~5min), and ultra-short-term (<5min)
HRV, the importance of HRV, and its implications for health and performance. The
authors provide an overview of widely-used HRV time-domain, frequency-domain, and
non-linear metrics. Time-domain indices quantify the amount of HRV observed during
monitoring periods that may range from ~2 min to 24 h. Frequency-domain values
calculate the absolute or relative amount of signal energy within component bands.
Non-linear measurements quantify the unpredictability and complexity of a series of IBIs.
The authors survey published normative values for clinical, healthy, and optimal per-
formance populations. They stress the importance of measurement context, including
recording period length, subject age, and sex, on baseline HRV values. They caution that
24h, short-term, and ultra-short-term normative values are not interchangeable. They
encourage professionals to supplement published norms with findings from their own
specialized populations. Finally, the authors provide an overview of HRV assessment
strategies for clinical and optimal performance interventions.
Keywords: biofeedback, complexity, heart rate variability, non-linear measurements, normative values, optimal
performance
HEART RATE VARIABILITY
Heart rate is the number of heartbeats per minute. Heart rate variability (HRV) is the uctuation
in the time intervals between adjacent heartbeats (1). HRV indexes neurocardiac function and is
generated by heart-brain interactions and dynamic non-linear autonomic nervous system (ANS)
processes. HRV is an emergent property of interdependent regulatory systems which operate on
dierent time scales to help us adapt to environmental and psychological challenges. HRV reects
regulation of autonomic balance, blood pressure (BP), gas exchange, gut, heart, and vascular tone,
which refers to the diameter of the blood vessels that regulate BP, and possibly facial muscles (2).
A healthy heart is not a metronome. e oscillations of a healthy heart are complex and non-
linear. A healthy heart’s beat-to-beat uctuations are best described by mathematical chaos (3).
e variability of non-linear systems provides the exibility to rapidly cope with an uncertain and
changing environment (4). While healthy biological systems exhibit spatial and temporal complexity,
disease can involve either a loss or increase of complexity (5).
TABLE 1 | HRV time-domain measures.
Parameter Unit Description
SDNN ms Standard deviation of NN intervals
SDRR ms Standard deviation of RR intervals
SDANN ms Standard deviation of the average NN intervals for
each 5min segment of a 24h HRV recording
SDNN index (SDNNI) ms Mean of the standard deviations of all the NN
intervals for each 5min segment of a 24h HRV
recording
pNN50 % Percentage of successive RR intervals that differ by
more than 50ms
HR MaxHR Min bpm Average difference between the highest and lowest
heart rates during each respiratory cycle
RMSSD ms Root mean square of successive RR interval
differences
HRV triangular index Integral of the density of the RR interval histogram
divided by its height
TINN ms Baseline width of the RR interval histogram
Interbeat interval, time interval between successive heartbeats; NN intervals, interbeat
intervals from which artifacts have been removed; RR intervals, interbeat intervals
between all successive heartbeats.
TABLE 2 | HRV frequency-domain measures.
Parameter Unit Description
ULF power ms2Absolute power of the ultra-low-frequency band (0.003Hz)
VLF power ms2Absolute power of the very-low-frequency band
(0.0033–0.04Hz)
LF peak Hz Peak frequency of the low-frequency band (0.04–0.15Hz)
LF power ms2Absolute power of the low-frequency band (0.04–0.15Hz)
LF power nu Relative power of the low-frequency band (0.04–0.15Hz) in
normal units
LF power % Relative power of the low-frequency band (0.04–0.15Hz)
HF peak Hz Peak frequency of the high-frequency band (0.15–0.4Hz)
HF power ms2 Absolute power of the high-frequency band (0.15–0.4Hz)
HF power nu Relative power of the high-frequency band (0.15–0.4Hz) in
normal units
HF power % Relative power of the high-frequency band (0.15–0.4Hz)
LF/HF % Ratio of LF-to-HF power
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Higher HRV is not always better since pathological condi-
tions can produce HRV. When cardiac conduction abnormalities
elevate HRV measurements, this is strongly linked to increased
risk of mortality (particularly among the elderly). Close examina-
tion of electrocardiogram (ECG) morphology can reveal whether
elevated HRV values are due to problems like atrial brillation (6).
An optimal level of HRV is associated with health and self-
regulatory capacity, and adaptability or resilience. Higher levels
of resting vagally-mediated HRV are linked to performance of
executive functions like attention and emotional processing by
the prefrontal cortex (1). Aerent information processing by the
intrinsic cardiac nervous system can modulate frontocortical
activity and impact higher-level functions (7).
A BRIEF OVERVIEW OF HRV METRICS
We can describe 24h, short-term (ST, ~5 min) or brief, and ultra-
short-term (UST, <5 min) HRV using time-domain, frequency-
domain, and non-linear measurements. Since longer recording
epochs better represent processes with slower uctuations
(e.g., circadian rhythms) and the cardiovascular system’s response
to a wider range of environment stimuli and workloads, short-term
and ultra-short-term values are not interchangeable with 24h values.
Time-domain indices of HRV quantify the amount of variability
in measurements of the interbeat interval (IBI), which is the time
period between successive heartbeats (see Table1). ese values
may be expressed in original units or as the natural logarithm
(Ln) of original units to achieve a more normal distribution (8).
Frequency-domain measurements estimate the distribu-
tion of absolute or relative power into four frequency bands.
e Task Force of the European Society of Cardiology and the
North American Society of Pacing and Electrophysiology (1996)
divided heart rate (HR) oscillations into ultra-low-frequency
(ULF), very-low-frequency (VLF), low-frequency (LF), and
high-frequency (HF) bands (see Table2).
Power is the signal energy found within a frequency band.
Frequency-domain measurements can be expressed in absolute or
relative power. Absolute power is calculated as ms squared divided
by cycles persecond (ms2/Hz). Relative power is estimated as the
percentage of total HRV power or in normal units (nu), which
divides the absolute power for a specic frequency band by the
summed absolute power of the LF and HF bands. is allows us
to directly compare the frequency-domain measurements of two
clients despite wide variation in specic band power and total
power among healthy, age-matched individuals (9).
e ULF band (0.003Hz) indexes uctuations in IBIs with a
period from 5min to 24h and is measured using 24h recordings
(10). e VLF band (0.0033–0.04Hz) is comprised of rhythms with
periods between 25 and 300s. e LF band (0.04–0.15Hz) is com-
prised of rhythms with periods between 7 and 25s and is aected
by breathing from ~3 to 9bpm. Within a 5min sample, there are
12–45 complete periods of oscillation (9). e HF or respiratory
band (0.15–0.40Hz) is inuenced by breathing from 9 to 24bpm
(11). e ratio of LF to HF power (LF/HF ratio) may estimate the
ratio between sympathetic nervous system (SNS) and parasympa-
thetic nervous system (PNS) activity under controlled conditions.
Total power is the sum of the energy in the ULF, VLF, LF, and
HF bands for 24h and the VLF, LF, and HF bands for short-term
recordings (12).
Finally, non-linear measurements (see Tab l e  3 ) allow us to
quantify the unpredictability of a time series (13).
SOURCES OF HRV
is section explores the sources of short-term and 24h HRV
measurements. e authors will not separately discuss ultra-
short-term HRV measurements since they are controversial
proxies for short-term HRV values and there is an absence of
research concerning their physiological origin.
SHORT-TERM HRV
Two distinct but overlapping processes generate short-term
HRV measurements. e rst source is a complex and dynamic
TABLE 3 | HRV non-linear measures.
Parameter Unit Description
S ms Area of the ellipse which represents total HRV
SD1 ms Poincaré plot standard deviation perpendicular the line of
identity
SD2 ms Poincaré plot standard deviation along the line of identity
SD1/SD2 % Ratio of SD1-to-SD2
ApEn Approximate entropy, which measures the regularity and
complexity of a time series
SampEn Sample entropy, which measures the regularity and
complexity of a time series
DFA α1 Detrended fluctuation analysis, which describes short-term
fluctuations
DFA α2 Detrended fluctuation analysis, which describes long-term
fluctuations
D2 Correlation dimension, which estimates the minimum number
of variables required to construct a model of system dynamics
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relationship between the sympathetic and parasympathetic
branches. e second source includes the regulatory mechanisms
that control HR via respiratory sinus arrhythmia (RSA), the baro-
receptor reex (negative-feedback control of BP), and rhythmic
changes in vascular tone (2). RSA refers to the respiration-driven
speeding and slowing of the heart via the vagus nerve (14).
Dynamic Autonomic Relationship
In a healthy human heart, there is a dynamic relationship between
the PNS and SNS. PNS control predominates at rest, resulting in
an average HR of 75bpm. e PNS can slow the heart to 20 or
30bpm, or briey stop it (15). is illustrates the response called
accentuated antagonism (16).
Parasympathetic nerves exert their eects more rapidly (<1s)
than sympathetic nerves (>5s) (17). Since these divisions can
produce contradictory actions, like speeding and slowing the
heart, their eect on an organ depends on their current balance
of activity. While the SNS can suppress PNS activity, it can also
increase PNS reactivity (18). Parasympathetic rebound may occur
following high levels of stress, resulting in increased nighttime
gastric activity (19) and asthma symptoms (20).
e relationship between the PNS and SNS branches is com-
plex (both linear and non-linear) and should not be described as
a “zero sum” system illustrated by a teeter-totter. Increased PNS
activity may be associated with a decrease, increase, or no change
in SNS activity. For example, immediately following aerobic exer-
cise, HR recovery involves PNS reactivation while SNS activity
remains elevated (21, 22).
Likewise, teaching clients to breathe slowly when they experi-
ence high levels of SNS activity can engage both branches and
increase RSA. is is analogous to a Formula 1® driver speeding
through a turn while gently applying the le foot to the brake,
a maneuver called “le-foot braking.” e complex relationship
between SNS and PNS nerve activity means that the ratio between
LF and HF power will not always index autonomic balance (21).
Regulatory Mechanisms
e autonomic, cardiovascular, central nervous, endocrine, and res-
piratory systems, and baroreceptors and chemoreceptors inuence
HRV over a short time period and contribute to the very-low to h igh
frequencies of the HRV spectrum (12). Baroreceptors, which are
BP sensors located in the aortic arch and internal carotid arter-
ies, contribute to short-term HRV (23). When you inhale, HR
increases. BP rises about 4–5s later. Baroreceptors detect this rise
and re more rapidly. When you exhale, HR decreases. BP falls
4–5s later (24, 25). e baroreex makes this acceleration and
deceleration of the heart, called RSA, possible (14).
e baroreex links HR, BP, and vascular tone. Oscillation
in one cardiovascular function causes identical oscillations in
the others (26). Baroreceptor ring due to BP changes activates
mechanisms that change HR and vascular tone. Rising BP trig-
gers decreases in HR and vascular tone, while falling BP causes
increases in both.
TWENTY-FOUR-HOUR HRV
Circadian rhythms, core body temperature, metabolism, the sleep
cycle, and the renin–angiotensin system contribute to 24h HRV
recordings, which represent the “gold standard” for clinical HRV
assessment (12). ese recordings achieve greater predictive
power than short-term measurements (10, 2729). Although we
calculate 24h and short-term HRV measurements using the same
mathematical formulas, they cannot substitute for each other and
their physiological meaning can profoundly dier (9).
TIME-DOMAIN MEASUREMENTS
Heart rate variability time-domain indices quantify the amount
of HRV observed during monitoring periods that may range
from <1min to >24h. ese metrics include the SDNN, SDRR,
SDANN, SDNN Index, RMSSD, NN50, pNN50, HR MaxHR
Min, the HRV triangular index (HTI), and the Triangular
Interpolation of the NN Interval Histogram (TINN, see Ta b l e 1 ).
Where appropriate, the authors reported accepted minimum
short-term and proposed ultra-short-term measurement periods.
SDNN
e standard deviation of the IBI of normal sinus beats (SDNN) is
measured in ms. "Normal" means that abnormal beats, like ectopic
beats (heartbeats that originate outside the right atriums sinoatrial
node), have been removed. While the conventional short-term
recording standard is 5min (11), researchers have proposed ultra-
short-term recording periods from 60s (30) to 240 s (31). e
related standard deviation of successive RR interval dierences
(SDSD) only represents short-term variability (9).
Both SNS and PNS activity contribute to SDNN and it is highly
correlated with ULF, VLF and LF band power, and total power
(32). is relationship depends on the measurement conditions.
When these bands have greater power than the HF band, they
contribute more to SDNN.
In short-term resting recordings, the primary source of the
variation is parasympathetically-mediated RSA, especially with
slow, paced breathing (PB) protocols (12). In 24 h recordings,
LF band power makes a signicant contribution to SDNN (9).
e SDNN is more accurate when calculated over 24h than dur-
ing the shorter periods monitored during biofeedback sessions.
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Longer recording periods provide data about cardiac reactions
to a greater range of environmental stimulation. In addition to
cardiorespiratory regulation, extended measurement periods can
index the heart’s response to changing workloads, anticipatory
central nervous activity involving classical conditioning, and cir-
cadian processes, including sleep-wake cycles. Twenty-four-hour
recordings reveal the SNS contribution to HRV (33).
e SDNN is the "gold standard" for medical stratication of
cardiac risk when recorded over a 24h period (11). SDNN values
predict both morbidity and mortality. Based on 24h monitoring,
patients with SDNN values below 50ms are classied as unhealthy,
50–100 ms have compromised health, and above 100 ms are
healthy (34). Heart attack survivors, whose 24h measurements
placed them in a higher category, had a greater probability of
living during a 31-month mean follow-up period. For example,
patients with SDNN values over 100ms had a 5.3 times lower
risk of mortality at follow-up than those with values under 50ms
(34). Does this mean that training patients to increase SDNN to
a higher category could reduce their risk of mortality?
SDRR
e standard deviation of the IBIs for all sinus beats (SDRR),
including abnormal or false beats, is measured in ms. As with
the SDNN, the SDRR measures how these intervals vary over
time and is more accurate when calculated over 24h because this
longer period better represents slower processes and the cardio-
vascular systems response to more diverse environmental stimuli
and workloads. Abnormal beats may reect cardiac dysfunction
or noise that masquerades as HRV.
SDANN
e standard deviation of the average normal-to-normal (NN)
intervals for each of the 5min segments during a 24h recording
(SDANN) is measured and reported in ms like the SDNN. is
refers to IBIs calculated aer artifacting the data. SDANN is not
a surrogate for SDNN since it is calculated using 5min segments
instead of an entire 24h time series (9) and it does not provide
additional useful information (12).
SDNN Index (SDNNI)
e SDNNI is the mean of the standard deviations of all the
NN intervals for each 5min segment of a 24-h HRV recording.
erefore, this measurement only estimates variability due to the
factors aecting HRV within a 5-min period. It is calculated by
rst dividing the 24h record into 288 5min segments and then
calculating the standard deviation of all NN intervals contained
within each segment. e SDNNI is the average of these 288 val-
ues. e SDNNI primarily reects autonomic inuence on HRV.
e SDNNI correlates with VLF power over a 24-h period (12).
NN50
e number of adjacent NN intervals that dier from each other
by more than 50ms (NN50) requires a 2min epoch.
pNN50
e percentage of adjacent NN intervals that dier from each
other by more than 50ms (pNN50) also requires a 2-min epoch.
Researchers have proposed ultra-short-term periods of 60s (31).
e pNN50 is closely correlated with PNS activity (32). It is cor-
related with the RMSSD and HF power. However, the RMSSD
typically provides a better assessment of RSA (especially in older
subjects) and most researchers prefer it to the pNN50 (35). is
may be a more reliable index than short-term SDNN measure-
ments for the brief samples used in biofeedback.
RMSSD
e root mean square of successive dierences between normal
heartbeats (RMSSD) is obtained by rst calculating each suc-
cessive time dierence between heartbeats in ms. en, each of
the values is squared and the result is averaged before the square
root of the total is obtained. While the conventional minimum
recording is 5min, researchers have proposed ultra-short-term
periods of 10s (30), 30s (31), and 60s (36).
e RMSSD reects the beat-to-beat variance in HR and is
the primary time-domain measure used to estimate the vagally
mediated changes reected in HRV (12). e RMSSD is identical
to the non-linear metric SD1, which reects short-term HRV
(37). Twenty-four-hour RMSSD measurements are strongly cor-
related with pNN50 and HF power (27). Minimum HR is more
strongly correlated with LnSDANN than LnRMSSD (Ln means
the natural logarithm). Maximum HR is weakly and inconsist-
ently correlated with these time-domain measures (38).
While the RMSSD is correlated with HF power (10), the
inuence of respiration rate on this index is uncertain (39, 40).
e RMSSD is less aected by respiration than is RSA across
several tasks (41). e RMSSD is more inuenced by the PNS
than SDNN. Lower RMSSD values are correlated with higher
scores on a risk inventory of sudden unexplained death in
epilepsy (42).
NN50, pNN50, and RMSSD are calculated using the dier-
ences between successive NN intervals. Since their computation
depends on NN interval dierences, they primarily index HF HR
oscillations, are largely unaected by trends in an extended time
series, and are strongly correlated (9).
HR MaxHR Min
e average dierence between the highest and lowest HRs
during each respiratory cycle (HR MaxHR Min) is especially
sensitive to the eects of respiration rate, independent of vagus
nerve trac. At least a 2-min sample is required to calculate
HR MaxHR Min. Instead of directly indexing vagal tone, it
reects RSA. Since longer exhalations allow greater acetylcholine
metabolism, slower respiration rates can produce higher RSA
amplitudes that are not mediated by changes in vagal ring.
HRV Triangular Index
e HTI is a geometric measure based on 24h recordings which
calculates the integral of the density of the RR interval histogram
divided by its height (11). A 5-min epoch is conventionally used
to represent this metric (43). HTI and RMSSD can jointly dis-
tinguish between normal heart rhythms and arrhythmias. When
HTI 20.42 and RMSSD0.068, the heart rhythm is normal.
When HTI>20.42, the pattern is arrhythmic (43).
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Shaffer and Ginsberg An Overview of HRV Metrics and Norms
Frontiers in Public Health | www.frontiersin.org September 2017 | Volume 5 | Article 258
Triangular Interpolation of the
NN Interval Histogram
e TINN is the baseline width of a histogram displaying NN
intervals (11). Like SDNN and RMSSD, contamination by only
two artifacts within a 5-min segment can signicantly distort its
value (8).
FREQUENCY-DOMAIN MEASUREMENTS
Analogous to the electroencephalogram, we can use Fast Fourier
Transformation (FFT) or autoregressive (AR) modeling to sepa-
rate HRV into its component ULF, VLF, LF, and HF rhythms that
operate within dierent frequency ranges. is is analogous to
a prism that refracts light into its component wavelengths (11).
ULF BAND
e ultra-low-frequency (ULF) band (0.003 Hz) requires a
recording period of at least 24h (12) and is highly correlated with
the SDANN time-domain index (44). While there is no consensus
regarding the mechanisms that generate ULF power, very slow-
acting biological processes are implicated. Circadian rhythms
may be the primary driver of this rhythm (12). Core body tem-
perature, metabolism, and the renin–angiotensin system operate
over a long time period and may also contribute to these frequen-
cies (11, 45). ere is disagreement about the contribution by the
PNS and SNS to this band. Dierent psychiatric disorders show
distinct circadian patterns in 24h HRs, particularly during sleep
(46, 47).
VLF BAND
e VLF band (0.0033–0.04Hz) requires a recording period of at
least 5min, but may be best monitored over 24h. Within a 5-min
sample, there are about 0–12 complete periods of oscillation (9).
While all low values on all 24h clinical HRV measurements pre-
dict greater risk of adverse outcomes, VLF power is more strongly
associated with all-cause mortality than LF or HF power (4851).
e VLF rhythm may be fundamental to health (12).
Low VLF power has been shown to be associated with
arrhythmic death (44) and PTSD (52). Low power in this band
has been associated with high inammation in several studies
(53, 54). Finally, low VLF power has been correlated with low
levels of testosterone, while other biochemical markers, such
as those mediated by the hypothalamic–pituitary–adrenal axis
(e.g., cortisol), have not (55).
Very-low-frequency power is strongly correlated with the
SDNNI time-domain measure, which averages 5min standard
deviations for all NN intervals over a 24-h period. ere is
uncertainty regarding the physiological mechanisms responsible
for activity within this band (10). e heart’s intrinsic nervous
system appears to contribute to the VLF rhythm and the SNS
inuences the amplitude and frequency of its oscillations (12).
Very-low-frequency power may also be generated by physical
activity (56), thermoregulatory, renin–angiotensin, and endothe-
lial inuences on the heart (57, 58). PNS activity may contribute
to VLF power since parasympathetic blockade almost completely
abolishes it (59). In contrast, sympathetic blockade does not aect
VLF power and VLF activity is seen in tetraplegics, whose SNS
innervation of the heart and lungs is disrupted (11, 60).
Based on work by Armour (61) and Kember etal. (62, 63), the
VLF rhythm appears to be generated by the stimulation of aerent
sensory neurons in the heart. is, in turn, activates various levels
of the feedback and feed-forward loops in the heart’s intrinsic
cardiac nervous system, as well as between the heart, the extrinsic
cardiac ganglia, and spinal column. is experimental evidence
suggests that the heart intrinsically generates the VLF rhythm and
eerent SNS activity due to physical activity and stress responses
modulates its amplitude and frequency.
LF BAND
e LF band (0.04–0.15Hz) is typically recorded over a mini-
mum 2min period (12). is region was previously called the
baroreceptor range because it mainly reects baroreceptor activ-
ity during resting conditions (1). LF power may be produced
by both the PNS and SNS, and BP regulation via baroreceptors
(11, 57, 64, 65), primarily by the PNS (66), or by baroreex activ-
ity alone (67). e SNS does not appear to produce rhythms much
above 0.1Hz, while the parasympathetic system can be observed
to aect heart rhythms down to 0.05Hz (20s rhythm). In rest-
ing conditions, the LF band reects baroreex activity and not
cardiac sympathetic innervation (12).
During periods of slow respiration rates, vagal activity can
easily generate oscillations in the heart rhythms that cross over
into the LF band (6870). erefore, respiratory-related eerent
vagally mediated inuences are particularly present in the LF
band when respiration rates are below 8.5bpm or 7s periods
(70, 71) or when one sighs or takes a deep breath.
HF BAND
e HF or respiratory band (0.15–0.40 Hz) is conventionally
recorded over a minimum 1min period. For infants and children,
who breathe faster than adults, the resting range can be adjusted
to 0.24–1.04 Hz (72). e HF band reects parasympathetic
activity and is called the respiratory band because it corresponds
to the HR variations related to the respiratory cycle. ese phasic
HR changes are known as RSA and may not be a pure index of
cardiac vagal control (73).
Heart rate accelerates during inspiration and slows during
expiration. During inhalation, the cardiovascular center inhibits
vagal outow resulting in speeding the HR. Conversely, during
exhalation, it restores vagal outow resulting in slowing the HR
via the release of acetylcholine (74). Total vagal blockage virtually
eliminates HF oscillations and reduces power in the LF range (12).
High-frequency power is highly correlated with the pNN50
and RMSSD time-domain measures (10). HF band power may
increase at night and decrease during the day (1). Lower HF power
is correlated with stress, panic, anxiety, or worry. e modulation
of vagal tone helps maintain the dynamic autonomic regulation
important for cardiovascular health. Decient vagal inhibition is
implicated in increased morbidity (75).
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HF Power and RSA do not Represent
Vagal Tone
In healthy individuals, RSA can be increased by slow, deep breath-
ing. Respiration rate changes can produce large-scale shis in
RSA magnitude without aecting vagal tone, which is mean HR
change across conditions (e.g., rest to exercise) (76). Grossman
(76) proposed an experiment. If you slow your breathing to
6bpm, you should observe increased HR uctuations compared
with 15bpm. During this time, mean HR should not appreciably
change because vagal tone did not change.
While HF power indexes vagal modulation of HR, it does not
represent vagal tone. If shis in HF power mirrored shis in vagal
tone, they should produce corresponding changes in average HR.
But, breathing at dierent rates within the 9–24bpm range, which
changes HF power, does not change mean HR. RSA and vagal
tone are dissociated during large-scale changes in SNS activity,
chemical blockade of the SA node, and when intense vagal eerent
trac dramatically slows HR during inhalation and exhalation
(73). Shis in respiration rate and volume can markedly change
HRV indices (HF power, RSA, pNN50, RMSSD) without actually
aecting vagal tone.
LnHF can Estimate Vagal Tone under
Controlled Conditions
e natural logarithm (Ln) is the logarithm to the base e of a
numeric value. Under controlled conditions while breathing at
normal rates, we can use LnHF power to estimate vagal tone (77).
LF/HF RATIO
e ratio of LF to HF power (LF/HF ratio) was originally based
on 24 h recordings, during which both PNS and SNS activity
contribute to LF power, and PNS activity primarily contributes
to HF power. e intent was to estimate the ratio between SNS
and PNS activity (12).
e assumptions underlying the LF/HF ratio is that LF power
may be generated by the SNS while HF power is produced by the
PNS. In this model, a low LF/HF ratio reects parasympathetic
dominance. is is seen when we conserve energy and engage in
tend-and-befriend behaviors. In contrast, a high LF/HF ratio
indicates sympathetic dominance, which occurs when we engage
in ght-or-ight behaviors or parasympathetic withdrawal.
Billman (21) challenged the belief that the LF/HF ratio meas-
ures “sympatho-vagal balance” (78, 79). First, LF power is not a
pure index of SNS drive. Half of the variability in this frequency
band is due to the PNS and a smaller proportion is produced
by unspecied factors. Second, PNS and SNS interactions are
complex, non-linear, and frequently non-reciprocal. ird,
confounding by respiration mechanics and resting HR creates
uncertainty regarding PNS and SNS contributions to the LF/HF
ratio during the measurement period.
Shaer etal. (12) warned that the LF/HF ratio is controversial
because dierent processes appear to generate 24h and 5min
values, and these values correlate poorly. Furthermore, the SNS
contribution to LF power varies profoundly with testing condi-
tions. For example, when LF is calculated while sitting upright
during resting conditions, the primary contributors are PNS activ-
ity and baroreex activity—not SNS activity (63, 80). erefore,
interpretation of 5min resting baseline LF/HF ratios depends on
specic measurement conditions.
NON-LINEAR MEASUREMENTS
From Schrödinger’s (81) perspective, life is aperiodic (e.g., oscillations
occur without a xed period) and operates between randomness
and periodicity. Twenty-four-hour ECG monitoring yields a time
series of R–R intervals (time period between successive heart-
beats). Non-linearity means that a relationship between variables
cannot be plotted as a straight line. Non-linear measurements
index the unpredictability of a time series, which results from
the complexity of the mechanisms that regulate HRV. Non-linear
indices correlate with specic frequency- and time-domain
measurements when they are generated by the same processes.
While stressors and disorders like diabetes can depress some
non-linear measurements, elevated values do not always signal
health. For example, in postmyocardial infarction (post-MI)
patients, increased non-linear HRV is an independent risk fac-
tor for mortality (82). is section reviews S, SD1, SD2, SD1/
SD2, approximate entropy (ApEn), sample entropy (SampEn),
detrended uctuation analysis (DFA) α1 and DFA α2, and D2
non-linear measures (see Table3).
POINCARÉ PLOT
A Poincaré plot (return map) is graphed by plotting every R–R
interval against the prior interval, creating a scatter plot. Poincaré
plot analysis allows researchers to visually search for patterns
buried within a time series (a sequence of values from succes-
sive measurements). Unlike frequency-domain measurements,
Poincaré plot analysis is insensitive to changes in trends in the
R–R intervals (83).
S, SD1, SD2, AND SD1/SD2
We can analyze a Poincaré plot by tting an ellipse (curve which
resembles a squashed circle) to the plotted points. Aer tting
the ellipse, we can derive three non-linear measurements, S, SD1,
and SD2. e area of the ellipse which represents total HRV (S)
correlates with baroreex sensitivity (BRS), LF and HF power,
and RMSSD.
e standard deviation (hence SD) of the distance of each
point from the y=x axis (SD1), species the ellipse’s width. SD1
measures short-term HRV in ms and correlates with baroreex
sensitivity (BRS), which is the change in IBI duration per unit
change in BP, and HF power. e RMSSD is identical to the non-
linear metric SD1, which reects short-term HRV (37). SD1 pre-
dicts diastolic BP, HR MaxHR Min, RMSSD, pNN50, SDNN,
and power in the LF and HF bands, and total power during 5min
recordings (84, 85).
e standard deviation of each point from the y=x+average
R–R interval (SD2) species the ellipse’s length. SD2 measures
short- and long-term HRV in ms and correlates with LF power
and BRS (8689). e ratio of SD1/SD2, which measures the
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unpredictability of the RR time series, is used to measure auto-
nomic balance when the monitoring period is suciently long
and there is sympathetic activation. SD1/SD2 is correlated with
the LF/HF ratio (83, 90).
APPROXIMATE ENTROPY
Approximate entropy measures the regularity and complexity of
a time series. ApEn was designed for brief time series in which
some noise may be present and makes no assumptions regarding
underlying system dynamics (9). Applied to HRV data, large
ApEn values indicate low predictability of uctuations in succes-
sive RR intervals (91). Small ApEn values mean that the signal is
regular and predictable (8).
SAMPLE ENTROPY
Sample entropy was designed to provide a less biased and more
reliable measure of signal regularity and complexity (92). SampEn
values are interpreted and used like ApEn and may be calculated
from a much shorter time series of fewer than 200 values (9).
DETRENDED FLUCTUATION ANALYSIS
Detrended uctuation analysis extracts the correlations between
successive RR intervals over dierent time scales. is analysis
results in slope α1, which describes brief uctuations, and slope
α2, which describes long-term uctuations. e short-term
correlations extracted using DFA reect the baroreceptor reex,
while long-term correlations reect the regulatory mechanisms
that limit uctuation of the beat cycle. DFA is designed to analyze
a time series that spans several hours of data (9).
CORRELATION DIMENSION (CD, D2)
e CD (D2) estimates the minimum number of variables required
to construct a model of system dynamics. e more variables
required to predict the time series, the greater its complexity. An
attractor is a set of values toward which a variable in a dynamic
system converges over time. CD measures a system’s attractor
dimension, which can be an integer or fractal (9).
CONTEXT IS CRUCIAL WHEN
INTERPRETING HRV MEASUREMENTS
Awareness of the context of recording and subject variables can
aid interpretation of both 24h and short-term HRV measure-
ments. Important contextual factors include recording period
length, detection or recording method, sampling frequency,
removal of artifacts, respiration, and whether or not there is
PB. Important subject variables are age, sex, HR, and health
status. In addition, inuences of position, movement, recency
of physical activity, tasks, demand characteristics, and rela-
tionship variables can all aect measurements subtly or even
greatly by changing ANS activation, breathing mechanics, and
emotions.
CONTEXTUAL FACTORS
Period Length
e length of the recording period signicantly aects both HRV
time-domain and frequency-domain measurements (93). Since
longer recordings are associated with increased HRV, it is inap-
propriate to compare metrics like SDNN when they are calculated
from epochs of dierent length (11, 94). Generally, resting values
obtained from short-term monitoring periods correlate poorly
with 24h indices and their physiological meanings may dier (9).
Detection Method
Electrocardiogram and PPG methods yielded discrepancies of
less than 6% for most HRV measures and 29.9% for pNN50 in
one study (95).
Sampling Frequency
While a minimum sampling frequency of 500 Hz may be
required to detect the R-spike ducial point of the ECG when
RSA amplitude is low, a sampling rate of 125Hz (93) or 200Hz
(9) may be sucient when RSA amplitude is normal. Very low
RR interval variability, which characterizes some heart failure
patients, requires higher sampling rates for adequate temporal
resolution (9). Lower sampling rates may threaten the validity of
HRV frequency-domain and non-linear indices (96).
Removal of Artifacts
Visual inspection of the raw BVP or ECG signal can help detect
artifacts (e.g., missed or spurious beats). Artifacts can signi-
cantly distort both time- and frequency-domain measurements
(97). Artifacts increase power in all frequency bands. Missed
beats produce greater increases than extra beats since deviation
from a missed beat equals the mean heart period versus half
the mean heart period for extra beats. e bias introduced by
even a single artifact can easily eclipse the 0.5–1.0 Ln eect sizes
typically found in psychophysiological research (98). When
artifacts are present, researchers can select an artifact-free epoch
or manually edit the aected RR intervals (99). When a clean
segment is shorter than the recommended length for calculating
power within a frequency band, values should be valid as long as
it contains at least six full periods of oscillations. For example,
estimation of LF power requires at least 2.5min of clean data (9).
Researchers can replace technical artifacts like missed beats
through interpolation based on QRS intervals that precede and
follow the contaminated segment. Data analysis soware like
Kubios (8) can help visualize the raw signal and preserve the
original record length and synchrony with other physiological
signals (e.g., respiration). e editing of ectopic beats and arrhyth-
mias can be challenging because the resulting changes in stroke
volume and cardiac output can aect 10–30 beats instead of the
two RR intervals that bracket the abnormal heartbeat (9).
Respiration
Greater tidal volumes and lower respiration rates increase RSA
(12, 100). Increasing respiration depth raised HR Max HR
Min and did not reduce time-domain, frequency-domain, or
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non-linear HRV measures (101, 102). Increasing or decreasing
respiration rate from a client’s resonance frequency, the breathing
rate that best stimulates the cardiovascular system, may lower
short-term time-domain measurements and LF band power,
while raising or lowering HF power, respectively.
e eect of inhalation-to-exhalation (I/E) ratio on HRV
time- and frequency-domain measurements remains unclear. Lin
etal. (103) reported that breathing at 5.5bpm with a 5:5 I/E ratio
resulted in higher LF power than with a 4:6 ratio. However, the
authors failed to conrm that their subjects actually breathed at
the required rates and ratios. Zerr etal. (84, 85) studied dierent
I/E ratios (1:2 and 1:1) at 6bpm and performed manipulation
checks on respiration rate and I/E ratio. ey found that HRV
time- or frequency-domain values were comparable when
healthy undergraduates breathed 6bpm at 1:2 or 1:1 I/E ratios.
A replication study by Meehan etal. (101, 102) also found that
HRV time- and frequency-domain values were comparable when
healthy undergraduates breathed at 6bpm at 1:2 or 1:1 I/E ratios.
Paced Breathing
Values obtained during normal breathing and PB can vary sig-
nicantly (17).
SUBJECT VARIABLES
Age
Heart rate variability time-domain measurements decline with
age (17, 104106). Bonnemeier etal. (104) obtained 24h record-
ings from 166 healthy volunteers (85 men and 81 women) ages
20–70. ey found the most dramatic HRV parameter decrease
between the second and third decades. Almeida-Santos etal. (106)
obtained 24h ECG recordings of 1,743 subjects 40–100years of
age. ey found a linear decline in SDNN, SDANN, and SDNN
index. However, they discovered a U-shaped pattern for RMSSD
and pNN50 with aging, decreasing from 40 to 60 and then
increasing aer age 70.
Sex
A meta-analysis of 296,247 healthy participants examined 50
HRV measures (107). Women had higher mean HR (smaller RR
intervals) and lower SDNN and SDNN index values, especially in
24h studies, compared to men. ey showed lower total, VLF, and
LF power, but greater HF power. While women showed relative
vagal dominance, despite higher mean HR, men showed relative
SNS dominance, despite their lower HR.
Heart Rate
Faster HRs reduce the time between successive beats and the
opportunity for the IBIs to vary. is lowers HRV. Conversely,
slower HRs increase the time between adjacent heartbeats and
the chance for IBIs to vary. is raises HRV. is phenomenon
is called cycle length dependence (1). Resting HRs that exceed
90bpm are associated with elevated risk of mortality (108).
Health
Time-domain measurements rise with increased aerobic fit-
ness (109, 110). In general, HRV time-domain measurements
decline with decreased health (111, 112). Autonomic cardiac
dysregulation is a critical process that underlies the manifesta-
tion and perpetuation of symptoms broad spectrum symptoms
of poor health. HRV has been shown to be useful in predicting
morbidities from common mental (e.g., stress, depression,
anxiety, PTSD) and physical disorders (e.g., inflammation,
chronic pain, diabetes, concussion, asthma, insomnia, fatigue),
all of which increase sympathetic output and create a self-
perpetuating cycle that produces autonomic imbalance and
greater allostatic load (113121). Thus, ANS dysfunction is a
systemic common denominator of poor health and associated
with acute and chronic illness and a risk factor for such serious
health issues as cancer survivorship, cardiovascular disease
and myocardial infarction, stroke, and overall mortality (49,
75, 122125).
HRV NORMS
Ultra-Short-Term (UST)
Measurement Norms
Ultra-short-term HRV measurements are based on less than
5 min of data (Ta b l e  4 ). Four studies reviewed in this section
(31, 126128) measured HRV during resting baselines while sit-
ting upright or lying supine. One study (30) monitored subjects
during resting baseline and Stroop test conditions.
e use of ultra-short-term recording to estimate HRV status
is important because of its obvious eciency in both clinical and
research settings. However, many of the reviewed ultra-short-
term studies (30, 31, 126, 130) suered from serious methodo-
logical limitations. Since only one of the studies (128) specied
their minimum criterion for acceptable concurrent validity
(e.g., r= 0.9), we cannot know the percentage of variability in
5 min values for which their ultra-short-term measurements
accounted. Since correlation between measurements doesn’t
ensure agreement, the authors recommend that investigators
utilize the more rigorous Bland-Altman Limits of Agreement
(LoA) method (131, 132) like Munoz etal. (129). is procedure
calculates the 95% limits of agreement between two methods of
measurement for repeated measures.
Review of this emerging literature suggests that dierences
in contextual factors such as recording method (BVP vs. ECG),
age, health, measurement condition, artifacting procedures, and
the concurrent-validity criteria used may have greater impact
on ultra-short-term measurements than on longer recordings.
Nonetheless, for healthy individuals, resting baselines as short as
1min may be sucient to measure HR, SDNN, and RMSSD as long
as professionals carefully remove artifacts. e standardization
of ultra-short-term measurement protocols and establishment of
normative values for healthy non-athlete, optimal performance,
and clinical populations remain important challenges to their use
in place of conventional 5min and 24h values.
McNames and Aboy (130) compared 10s to 10 min resting
ECG recordings compared to 5 min recordings using archival
data from PhysioNet. e strongest correlations were achieved
with HF ms2, SDSD, and RMSSD. Salahuddin etal. (30) obtained
5min of resting ECG data from 24 healthy students and noted
TABLE 4 | Ultra-short-term (UST) norms.
Studies Subjects HRV
monitor
Metrics and minimum epoch
required to estimate short-
term values
Salahuddin
etal. (30)
24 healthy
students
Age 22–31
ECG HR and RMSSD-10s; pNN50,
HF (ms2 and nu), LF/HF, and LF
nu-20s; LF ms2 and VLF ms2-
50s; SDNN and the coefficient of
variation-60s; HTI and TINN-90s
to estimate 150s values
Nussinovitch
etal. (126)
70 healthy
volunteers
Age
42.5±16.1
ECG 10s and 1min resting RMSSD
values correlated with 5min
RMSSD values, but 10s and
1min resting SDNN did not
correlate with 5min SDNN values
Baek
etal. (31)
467 healthy
volunteers
Age 8–69
PPG HR-10s; HF ms2-20s;
RMSSD-30s; pNN50-60s; LF
(ms2 and nu) and HF nu-90s;
SDNN-240s; VLF ms2-270s to
estimate 5min values. Minimum
values differed by age group
Munoz
etal. (129)
3,387 adults
(1,727W and
1,660M)
Mean age 53
Portapres®Near-perfect agreement of
120s RMSSD and SDNN values
with 240–300s values. UST
RMSSD values achieved stronger
agreement with 240–300s values
than UST SDNN for all record
lengths and agreement metrics
(Pearson r, Bland-Altman, and
Cohen’s d)
Shaffer
etal. (128)
38 healthy
students
Age 18–23
ECG HR-10s; NN50, and pNN50-
60s; TINN, LF ms2, SD1, and
SD2-90s; HTI and DFA ɑ1-120s;
LF nu, HF ms2, HF nu, LF/HF,
SampEn, DFA ɑ2, and DET-180s;
ShanEn-240s; VLF ms2-270s to
estimate 5min values. No epoch
estimated CD
Coefficient of variation, ratio of the standard deviation to the mean; CD (also D2),
correlation dimension, which is the minimum number of variables required to construct
a model of system dynamics; DET, determinism of a time series; DFAɑ1, detrended
fluctuation analysis, which describes short-term fluctuations; DFA ɑ2, detrended
fluctuation analysis, which describes long-term fluctuations; ECG, electrocardiogram;
HF ms2, absolute power of the high-frequency band; HF nu, relative power of the high-
frequency band in normal units; HR, heart rate; HTI, HRV triangular index or integral of
the density of the NN interval histogram divided by its height; LF ms2, absolute power
of the low-frequency band; LF nu, relative power of the low-frequency band in normal
units; LF/HF, ratio of LF-to-HF power; NN interval, time between adjacent normal
heartbeats; nu, normal units calculated by dividing the absolute power for a specific
frequency band by the summed absolute power of the LF and HF bands; pNN50,
percentage of successive interbeat intervals that differ by more than 50ms; RMSSD,
root mean square of successive RR interval differences; RR interval, time between all
adjacent heartbeats; SampEn, sample entropy, which measures signal regularity and
complexity; SD1, Poincaré plot standard deviation perpendicular to the line of identity;
SD2, Poincaré plot standard deviation along the line of identity; SDNN, standard
deviation of NN intervals; ShanEn, Shannon entropy, which measures uncertainty in a
random variable; TINN, triangular interpolation of the RR interval histogram or baseline
width of the RR interval histogram; VLF ms2, absolute power of the very-low-frequency
band.
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SDNN; the coecient of variance required 60s; HRV Index and
TINN required 90s; and the Stress Index required 100s.
Similarly, Baek et al. (31) estimated 5 min resting PPG
HRV values from 467 healthy volunteers with ultra-short-term
recordings. HR required 10s, HF ms2 required 20 s, RMSSD
required 30s, pNN50 required 60s, LF (ms2 and nu), HF nu,
and LF/HF ms2 required 90s, SDNN required 240s, and VLF
ms2 required 270s. ese minimum periods also diered by
age group.
When Nussinovitch etal. (126) compared 10s and 1 min
resting ECG recordings with 5min recordings from 70 healthy
volunteers, ultra-short-term RMSSD measurements achieved
acceptable correlations, but SDNN did not achieve acceptable
correlations with the longer short-term recordings.
Munoz et al. (129) measured SDNN and RMSSD in 3,387
adults and analyzed data using Pearsons correlation coecients,
the Bland-Altman LoA method, and Cohen’s d. At 120s, record-
ings achieved nearly perfect agreement with 240–300s values
(r=0.956, bias=0.406 for SDNN and 0.986, bias= 0.014 for
RMSSD).
Shaer etal. (128) recorded 5min of resting ECG data from
38 healthy undergraduates and manually artifacted the IBIs. ey
correlated 10, 20, 30, 60, 90, 120, 180, and 240s HRV metrics
with 5min metrics. e authors selected a conservative criterion
of r=0.90 to ensure that ultra-short-term values would account
for at least 81% of the variability in 5min values. A 10s segment
estimated mean HR. A 60s segment measured SDNN, RMSSD,
NN50, and pNN50. A 90s segment calculated TINN, LF ms2,
SD1, and SD2. A 120s segment approximated HTI and DFA ɑ1.
A 180s segment computed LF nu, HF ms2, HF nu, LF/HF ms2,
SampEn, DFA ɑ2, and DET. A 240s segment assessed ShanEn. No
UST measurement successfully estimated CD.
SHORT-TERM MEASUREMENT NORMS
Short-term measurement norms are based on ~5min of HRV
data (Ta b l e 5 ). Because of their relative ease of recording, short-
term measurements have been widely used and studied for
many years, and appear to be the most commonly found source
of published HRV data (11, 60). Short-term values are only
appropriate when clients breathe at normal rates (~11–20bpm).
During resonance frequency biofeedback, the only relevant
metrics are LF ms2 or peak frequency since breathing from 4.5
to 7.5bpm concentrates HR oscillations around 0.1Hz in the
LF band.
Berko etal. (133) reported short-term norms from 145 elite
track-and-eld athletes (87 men and 58 women), 18–33years,
who were measured before the 2004 USA Olympic Trials. e
investigators monitored the athletes in the supine position for
2.5min using ECG aer up to 5min of rest to stabilize HR. ese
authors used the Fast Fourier transformation (FFT) method
to perform power spectral analysis. ey reported mean and
standard deviation values by sex for the time-domain measures
of SDNN, RMSSD, and pNN50 and the frequency-domain meas-
ures of LF (ms2 and nu), HF (ms2 and nu), LF/HF (LF/HF and LF/
HF nu), and total power. Female athletes showed greater values
that valid estimation of values from ultra-short-term recordings
required diering lengths for dierent HRV variables: mean HR
and RMSSD required 10s; PNN50, HF (ms2 and nu), LF/HF, and
LF nu required 20s; LF ms2 required 30s; VLF ms2 required 50s;
TABLE 6 | Nunan etal. (17) short-term norms.
HRV measure Mean (SD) Range Studies
IBI (ms) 926 (90) 785–1,160 30
SDNN (ms) 50 (16) 32–93 27
RMSSD (ms) 42 (15) 19–75 15
LF (ms2) 519 (291) 193–1,009 35
LF (nu) 52 (10) 30–65 29
HF (ms2) 657 (777) 83–3,630 36
HF (nu) 40 (10) 16–60 30
LF/HF (ms2) 2.8 (2.6) 1.1–11.6 25
IBI, interbeat interval; SDNN, standard deviation of NN intervals; RMSSD, root mean
square of successive RR interval differences; LF ms2, absolute power of the low-
frequency band; LF nu, relative power of the low-frequency band in normal units; HF
ms2, absolute power of the high-frequency band; HF nu, relative power of the high-
frequency band in normal units; LF/HF, ratio of LF-to-HF power.
Reproduced with permission of John Wiley and Sons.
TABLE 5 | Short-term ECG norms.
Studies Subjects Spectral
analysis
Breathing Sample Position Metrics
Berkoff
etal. (133)
145 elite athletes (87M and 58W)
age 18–33
FFT Free 2.5min Supine SDNN, RMSSD, pNN50, LF (ms2 and nu), HF (power and nu),
LF/HF (% and nu), and total power
Nunan
etal. (17)
21,438 healthy adults (12,960M
and 8,474W) age40
AR and FFT Free/paced Varied Varied RR, SDNN, RMSSD, LF (ms2 and nu), HF (ms2 and nu),
and LF/HF
Abhishekh
etal. (105)
189 healthy adults (114M and
75W) age 16–60
Free 5min Supine SDNN, RMSSD, LF (ms2and nu), HF (ms2 and nu), LF/HF,
and total power (ms2)
Seppälä
etal. (134)
465 prepubertal children (239 B)
and 226G age 6–8
FFT Free 5min Supine RR, HR, SDNN, RMSSD, pNN50, HTI, TINN, LF (peak, ms2,
%), HF (peak, ms2, %), LF/HF, SD1, SD2, SD1/SD2, SampEn,
D2, DFA (α1 and α2) for 5th, 25th, 50th, 75th, and 95th percentiles
D2 (also CD), correlation dimension, which estimates the minimum number of variables required to construct a model of a studied system; DFA ɑ1, detrended fluctuation analysis,
which describes short-term fluctuations; DFA ɑ2, detrended fluctuation analysis, which describes long-term fluctuations; ECG, electrocardiogram; HF ms2, absolute power of the
high-frequency band; HF nu, relative power of the high-frequency band in normal units; HF peak, highest amplitude frequency in the HF band; HF%, HF power as a percentage of
total power; HR, heart rate; HTI, HRV triangular index or integral of the density of the NN interval histogram divided by its height; LF ms2, absolute power of the low-frequency band;
LF nu, relative power of the low-frequency band in normal units; LF peak, highest amplitude frequency in the LF band; LF%, LF power as a percentage of total power; LF/HF, ratio
of LF-to-HF power; NN interval, time between adjacent normal heartbeats; nu, normal units calculated by dividing the absolute power for a specific frequency band by the summed
absolute power of the LF and HF bands; pNN50, percentage of successive interbeat intervals that differ by more than 50ms; RMSSD, root mean square of successive RR interval
differences; RR interval, time between all adjacent heartbeats; SampEn, sample entropy, which measures signal regularity and complexity; SD1, Poincaré plot standard deviation
perpendicular to the line of identity; SD2, Poincaré plot standard deviation along the line of identity; SD1/SD2, ratio of SD1 to SD2 that measures the unpredictability of the R–R
time series and autonomic balance under appropriate monitoring conditions; SDNN, standard deviation of NN intervals; TINN, triangular interpolation of the RR interval histogram or
baseline width of the RR interval histogram; total power, sum of power (ms2) in VLF, LF, and HF bands.
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for pNN50 and HF nu than male athletes. Male athletes showed
greater values for LF nu and LF/HF ratio than female athletes.
Type of sport (distance runners, eld athletes, power athletes,
sprinters, and strength athletes) did not aect HRV measures.
Normative data from short-term HRV studies published
aer the Task Force Report (11) were reviewed by Nunan
etal. (17) (Ta b l e  6 ). e 44 selected studies meeting their cri-
teria involved 21,438 healthy adult participants. is analysis
included three large populations with a minimum age of 40
(135137) which may explain their comparatively lower HRV
values. e authors reported HRV values according to whether
breathing was free or paced, sex, and spectral power analysis,
autoregressive (AR) or FFT. ey reported mean absolute and
mean log-transformed values for mean RR, SDNN, RMSSD,
LF (ms2 and nu), HF (ms2 and nu), and the LF/HF ratio. e
selected studies showed greater agreement on time-domain
measures (SDNN had the lowest coecient of variation) than
did frequency-domain measures (HF ms2 and log-transformed
HF showed the largest variation). e FFT method resulted in
lower LF power, greater HF power (ms2 and log-transformed),
and greater LF/HF ratio than the AR method. PB resulted in
higher values on all HRV indices except LF ms2, which was
greatest during free breathing.
More recently, Abhishekh et al. (105) studied 189 healthy
participants (114 men and 75 women) who ranged from 16 to
60years of age. ey analyzed 5min artifact-free supine ECG
recordings obtained while participants breathed between 12
and 15 bpm. ey reported SDNN and RMSSD time-domain
measures, and LF (ms2 and nu), HF (ms2 and nu), the LF/HF
ratio, and total power frequency-domain measures. e authors
found a negative correlation of RMSSD, SDNN, and total power
with age. While HF nu was negatively correlated with age, LF/HF
ratio was positively correlated. ese correlations suggested that
sympathetic tone increases with age.
Seppälä etal. (134) monitored 465 prepubertal children (239
boys and 226 girls) 6–8years of age. ey obtained 1 and 5min
resting ECG recordings. ey performed power spectral analysis
using the FFT method. ey reported mean RR interval and HR,
SDNN, RMSSD, pNN50, HTI, and TINN HRV time-domain
measures, LF (peak, ms2, and %), HF (peak, ms2, and %), LF/HF
ms2, and SD1, SD2, SD1/SD2, SampEn, D2, and DFA (α1 and α2)
non-linear measures. e authors reported 1 and 5min reference
values for these parameters for the 5th, 25th, 50th, 75th, and 95th
percentiles and concluded that the same values could be used for
both boys and girls since there were no gender dierences. ey
argued that HRV parameters that reect parasympathetic HR
modulation (RMSSD, pNN50, HF ms2, and SD1) could be reliably
measured using 1min recordings. However, HTI, TINN, LF ms2,
SD2, and relative LF and HF power, and SD1/SD2, require 5min
recordings due to the longer rhythms that comprise LF-band
activity.
TABLE 7 | Twenty-four-hour HRV norms.
Studies Subjects Metrics
Task Force
Report (11)
274 healthy subjects
(202M and 72F), age
40–69
24h SDNN, SDANN, RMSSD, HTI
and 5min supine LF power (ms2
and nu), HF power (HF ms2 and HF
nu), LF/HF power, and total power
Umetani
etal. (32)
260 healthy subjects
(122M and 148W), age
10–99
SDNN, SDANN, SDNNI, RMSSD,
pNN50, and HR by decade
Beckers
etal. (4)
276 healthy subjects
(141M and 135W), age
18–71
SDNN, RMSSD, and pNN50, total
power, LF (ms2 and %), HF (ms2
and %), and LF/HF ratio, and non-
linear measures, 1/f, FD, DFA α1
and α2, CD, S, and LE
Bonnemeier
etal. (139)
166 healthy subjects
(85M and 81W), age
20–70
RMSSD, SDNN, SDNNI, SDANN,
NN50, and HTI
Aeschbacher
etal. (140)
2,079 subjects (972M
and 1,107W), age 25–41
HR, SDNN, LF ms2 and HF ms2
Almeida-Santos
etal. (106)
1,743 subjects (616M
and 1,127W), age
40–100
SDNN, SDANN, SDNNI, RMSSD,
and pNN50
1/f, 1 divided by frequency slope, which characterizes the shape of the HRV frequency
spectrum; CD, correlation dimension, which estimates the minimum number of
variables required to construct a model of a studied system; DFA ɑ1, detrended
fluctuation analysis, which describes short-term fluctuations; DFA ɑ2, detrended
fluctuation analysis, which describes long-term fluctuations; ECG, electrocardiogram;
FD, signal regularity; HF ms2, absolute power of the high-frequency band; HF nu,
relative power of the high-frequency band in normal units; HF peak, highest amplitude
frequency in the HF band; HF%, HF power as a percentage of total power; HR, heart
rate; HTI, HRV triangular index or integral of the density of the NN interval histogram
divided by its height; LE, Lyapunov exponent, which measures a non-linear system’s
sensitive dependence on starting conditions; LF ms2, absolute power of the low-
frequency band; LF nu, relative power of the low-frequency band in normal units; LF
peak, highest amplitude frequency in the LF band; LF%, LF power as a percentage of
total power; LF/HF, ratio of LF-to-HF power; NN interval, time between adjacent normal
heartbeats; nu, normal units calculated by dividing the absolute power for a specific
frequency band by the summed absolute power of the LF and HF bands; pNN50,
percentage of successive interbeat intervals that differ by more than 50ms; RMSSD,
root mean square of successive RR interval differences; RR interval, time between all
adjacent heartbeats; S, area of an ellipse fitting a Poincaré plot, which represents total
HRV; SampEn, sample entropy, which measures signal regularity and complexity; SD1,
Poincaré plot standard deviation perpendicular to the line of identity; SD2, Poincaré
plot standard deviation along the line of identity; SD1/SD2, ratio of SD1 to SD2 that
measures the unpredictability of the RR time series and autonomic balance under
appropriate monitoring conditions; SDNN, standard deviation of NN intervals; TINN,
triangular interpolation of the RR interval histogram or baseline width of the RR interval
histogram; total power, sum of power (ms2) in ULF, VLF, LF, and HF bands.
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TWENTY-FOUR HOUR MEASUREMENT
NORMS
Twenty-four-hour norms are obtained using ambulatory HRV
monitoring (Ta b l e  7 ). e technology for recording and interpret-
ing long-term “naturalistic” HR adjustments is rapidly advancing
and the subject of another article in this issue (138). In the classic
paper on the subject, e Task Force Report (11) reported 24h
norms for 144 healthy subjects that included cutos for moder-
ately depressed and highly depressed HRV and for increased risk
of mortality. e authors reported 24h time-domain measures of
SDNN, SDANN, RMSSD, and the HRV HTI, and supine 5min
frequency-domain measures for LF power (LF ms2 and nu),
HF power (HF ms2 and nu), LF/HF power, and total power (ms2).
Umetani et al. (32) published 24 h norms for 260 healthy
participants (112 men and 148 women) who ranged from 10 to
99years old. e authors reported means, standard deviations,
and 95% condence intervals for 24h HRV time-domain meas-
ures of SDNN, SDANN, SDNN index, RMSSD, and pNN50, and
HR by decade. ey analyzed the relationship between each HRV
time-domain measure with HR and age, compared HR and HRV
measures between decades and two-decade spans. ey reported
that several HRV time-domain indices declined with age. Aer
age 65, subjects fell below cutos for increased threat of mortal-
ity. Before age 30, female subjects had lower HRV measurements
than their male counterparts. is gender dierence vanished
aer 50years of age.
Beckers etal. (4) obtained 24h ECG recordings of 276 healthy
participants (141 men and 135 women) 18–71years of age. ey
performed power spectral analysis using the FFT method and
divided the 24 h recordings into daytime and nighttime. e
authors reported day and night time-domain measures, SDNN,
RMSSD, and pNN50, frequency-domain measures, total power
(ms2), LF (ms2 and %), HF (ms2 and %), and LF/HF ratio, and non-
linear measures, 1/frequency slope (1/f), fractal dimension (FD),
DFA α1 and α2, CD, % of CD dierence, S value, and Lyapunov
exponent. Both linear and non-linear metrics decreased with age.
e authors found that non-linear values were higher at night, did
not dier by sex, and decreased with age.
Bonnemeier etal. (139) recorded 24h ECG for 166 healthy
volunteers (85 men and 81 women) aged 20–70. ey obtained
hourly and 24h RMSSD, SDNN. SDNNI, SDANN, NN50, and
HTI values. All 24h HRV values declined with age. e attenua-
tion of HRV parameters with age mainly occurred during night-
time. e largest decrease occurred during the second and third
decades. Following this drop, the decline was gradual. SDNNI,
NN50, and RMMSD correlated most strongly with aging. Mean
24h RR interval, SDNN, SDNNI (SD for all 5min intervals) and
SDANN were signicantly higher in men. Gender dierences
diminished with age.
Aeschbacher et al. (140) recorded 24 h ambulatory ECGs
and assessed the lifestyles of 2,079 subjects (972 men and 1,107)
aged 25–41. ey obtained HR, SDNN, and LF and HF power.
SDNN was 160±40 (men) and 147±36 (women). LF power was
1,337ms2 (men) and 884ms2 (women). HF power was 289ms2
(men) and 274ms2 (women). e authors reported that only a
minority of their sample had healthy lifestyles and that lifestyle
scores were associated with 24h SDNN values.
Almeida-Santos et al. (106) obtained 22–24 h ambulatory
ECGs from 1,743 participants (616 men and 1,127 women)
aged 40–100. While their sample included comorbidities like
dyslipidemia and hypertension, they were capable of perform-
ing the activities of daily living. e authors calculated HRV
time-domain measures of SDNN, SDANN, SDNNI, RMSSD,
and pNN50. HRV linearly declined with age. SDNN, SDANN,
SDNNI, RMSSD, and PNN50 were higher in men than women.
RMSSD and pNN50 showed a U-shaped pattern with aging,
decreasing from 40 to 60 and then increasing from 70. e
authors concluded that global autonomic regulation decreases
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Frontiers in Public Health | www.frontiersin.org September 2017 | Volume 5 | Article 258
linearly with aging and is lower in men, diabetics, and obese
individuals.
ASSESSMENT IN CLINICAL AND
OPTIMAL PERFORMANCE
INTERVENTIONS
e selection of HRV time-domain, frequency-domain, and non-
linear measurements and norms to assess progress in clinical and
optimal performance interventions should be informed by peer-
reviewed studies. Professionals training specialized populations
(e.g., chronic pain patients) might supplement published norms
for the general population with values from their own clients. e
rigorous data reporting guidelines proposed by Laborde etal. (93)
could guide their eorts to publish their norms to remedy gaps in
the literature. e metrics most strongly correlated with clinical
improvement and athlete performance gains in these reports
could be incorporated in pretreatment/posttreatment, within-
session, and across-session assessment. While a full treatment of
HRV variables in relation to the HRV biofeedback intervention is
beyond the scope of this article, we will briey touch on the issues
that seem to us to be the key ones (141).
In addition to the primary literature, the Association for Applied
Psychophysiology and Biofeedback has published two references
that identify metrics associated with clinical and optimal per-
formance outcomes, Evidence-Based Practice in Biofeedback and
Neurofeedback (3rd ed.) and Foundations of Heart Rate Variability
Biofeedback: A Book of Readings (142, 143). Further, readers
might consult Gevirtz, Lehrer, and Schwartz’s excellent chapter on
Cardiorespiratory Biofeedback in Schwartz and Andrasik’s (Eds.)
Biofeedback: A Practitioners Guide (4th ed.).
PRETREATMENT/POSTTREATMENT
ASSESSMENT
Twenty-four-hour HRV monitoring before and aer a series of
HRV biofeedback training sessions provides the most valid meas-
urements of ULF, VLF, total power, and LF/HF-domain indices
(12). Moreover, 24 h time-domain measurements like SDNN
achieve prognostic power that ultra-short-term and short-term
measurements cannot. Successful HRV biofeedback should result
in increased power in all individual frequency bands, total power,
and LF/HF ratio, and relevant time-domain and non-linear
values.
Where 24 h HRV assessment is not feasible, short-term
(~5min) resting measurements without feedback or pacing, and
while breathing at normal rates can help evaluate physiological
change. Successful HRV biofeedback should increase LnHF
(which may index vagal tone under controlled conditions), RSA,
and possibly LF and total power, and relevant time-domain and
non-linear values. Autonomic (nger temperature and skin con-
ductance/potential) and respiratory (end-tidal CO2 and respira-
tion depth, rate, and rhythmicity) indices can complement HRV
measurements. Successful HRV biofeedback may increase nger
temperature, decrease skin conductance/potential, increase end-
tidal CO2 to between 35 and 45torr, increase respiration depth,
slow respiration rate below 16bpm, and increase rhythmicity in
respirometer and HR waveforms (144).
WITHIN- AND ACROSS-SESSION
ASSESSMENT
Short-term resting HRV, autonomic, and respiratory measure-
ments without feedback or pacing, and while breathing at normal
rates can be obtained during pre- and posttraining baselines for
within-session assessment or across the pretraining baselines of
successive sessions. For both within- and across-session assess-
ment, successful HRV biofeedback training should result in the
same pattern of physiological change as described the previous
section on short-term resting pretreatment/posttreatment assess-
ment. While increased VLF power in 24h HRV assessment is
consistent with improved health, this change during short-term
assessment may indicate training diculty, vagal withdrawal, due
to excessive eort (56). Where short-term assessment does not
involve physical exercise or stress trials, the LF/HF ratio may not
index of autonomic balance since there will be no signicant SNS
activation to measure (12).
ASSESSMENT DURING HRV
BIOFEEDBACK TRIALS
During HRV biofeedback training, adults may be instructed to
engage in paced abdominal breathing between 4.5 and 7.5bpm
guided by a real-time display of instantaneous HR and respiration.
As clients’ breathing approaches their resonance frequency, the
rate that most strongly stimulates their baroreceptor reex, RSA
will increase (141). Since respiration rate helps to determine the
peak HRV frequency (the frequency with the highest amplitude),
successful training should produce a lower peak frequency and
greater LF power than a resting baseline obtained when clients
breathe from 12 to 15bpm. PB at 6bpm should result in a spectral
peak at 0.1Hz, while breathing at 7.5bpm should create a peak
at 0.125Hz. Both 6 and 7.5bpm rates will also increase power in
the LF, which ranges from 0.04 to 0.15Hz.
SUMMARY
Autonomic eerent neurons and circulating hormones modulate
SA node initiation of heartbeats. e interdependent regula-
tory systems that generate the complex variability of a healthy
heart operate over dierent time scales to achieve homeostasis
and optimal performance. Circadian oscillations in circadian
variations in core body temperature, metabolism, sleep–wake
cycles, and the renin–angiotensin system contribute to 24h HRV
measurements. e complex dynamic relationship between the
sympathetic and parasympathetic branches, and homeostatic
regulation of HR via respiration and the baroreceptor reex are
responsible for short-term and ultra-short-term HRV measure-
ments. Since slower regulatory mechanisms contribute to HRV
metrics recorded over longer measurement periods, 24h, short-
term, and ultra-short-term values are not interchangeable.
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Frontiers in Public Health | www.frontiersin.org September 2017 | Volume 5 | Article 258
Clinicians and researchers measure HRV using time-domain,
frequency-domain, and non-linear indices. Time-domain values
measure how much HRV was observed during the monitoring
period. Recording period length strongly inuences time-domain
values. Shorter epochs are associated with smaller values and
poorly estimate 24h values (17). For example, where 24h SDNN
values predict future heart attack risk, 5min SDNN values do
not (12).
Frequency-domain values calculate absolute or relative signal
power within the ULF, VLF, LF, and HF bands. Recording period
length limits HRV frequency-band measurement. Minimum
recommended periods include: ULF (24h), VLF (5 min, 24 h
preferred), LF (2min), and HF (1min). Again, short-term epochs
(~5min) lack the prognostic power of 24h measurements for
morbidity and mortality.
Non-linear indices measure the unpredictability and com-
plexity of a series of IBIs. e relationship between non-linear
measurements and illness is complex. While stressors and disease
lower some non-linear indices, in cases like myocardial infarc-
tion, higher non-linear HRV predicts a greater risk of mortality.
e expanding literature on ultra-short-term, short-term, and
24h HRV norms requires careful interpretation. Due to the lack
of standardization of ultra-short-term measurement protocols,
concurrent validity criteria, and normative values for healthy
non-athlete, optimal performance, and clinical populations,
clinicians should not use ultra-short-term interchangeably with
5min and 24h values.
Short-term measurement norms can contribute to assessment
before, during, and aer HRV biofeedback training for both
clinical and optimal performance. Since short-term measure-
ment norm studies vary in detection method (ECG or PPG),
frequency-band cutos, power spectral analysis method (AR or
FFT), position (sitting upright or lying supine), respiration rate,
and breathing pacing (paced or free breathing) and subject sex,
age, and aerobic tness, the selection of appropriate norms is
crucial. Likewise, 24h HRV norms can guide HRV biofeedback
training for clinical and optimal performance. As with short-term
measurement norms, frequency-band cutos, power spectral
analysis method (AR or FFT), and subject sex, age, and aerobic
tness can help to determine the selection of reference values.
e selection of HRV time-domain, frequency-domain, and
non-linear metrics to assess progress in clinical and optimal per-
formance interventions can be guided by peer-reviewed studies
and supplemented by values from specialized populations. e
HRV metrics most strongly correlated with clinical improve-
ment and athlete performance gains in these reports might be
incorporated in pretreatment/posttreatment, within-session, and
across-session assessment. Finally, LF-band power and RSA will
increase during successful HRV biofeedback trials due to PB in
the 4.5–7.5bpm range.
AUTHOR CONTRIBUTIONS
FS reviewed the literature, revised JG’s article outline, wrote
the initial abstract, manuscript, and table dras, and revised
the second dras following feedback from JG. JG reviewed the
literature, proposed an article outline, created and maintained an
EndNote database, contributed sections of the initial dras, and
made editorial suggestions for the second dras. Both FS and JG
discussed the conceptual issues and themes for the review article.
ACKNOWLEDGMENTS
e authors want to express their profound thanks to Richard
Gevirtz, Paul Lehrer, Zachary Meehan, Donald Moss, and
Christopher Zerr for their generous contributions to this article.
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17
Shaffer and Ginsberg An Overview of HRV Metrics and Norms
Frontiers in Public Health | www.frontiersin.org September 2017 | Volume 5 | Article 258
144. Zerr C, Kane A, Vodopest T, Allen J, Fluty E, Gregory J, etal. HRV biofeedback
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Conict of Interest Statement: e authors declare that the research was
conducted in the absence of any commercial or nancial relationships that could
be construed as a potential conict of interest.
Copyright © 2017 Shaer and Ginsberg. is is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). e
use, distribution or reproduction in other forums is permitted, provided the
original author(s) or licensor are credited and that the original publication in
this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these
terms.
... The following parameters were measured: SDNN, r-MSSD, pNN50, total power and log LF/HF. SDNN: standard deviation of the NN intervals in ms; pNN50: percentage of the intervals with at least 50 ms deviation from the previous interval; r-MSSD: root mean square of successive differences between normal heartbeats; log LF/HF: quotient of the logarithm of low frequency to high frequency; total power: total number of all frequency ranges [15,16]. ...
... Persons with values between 50 and 100 ms are considered somewhat restricted, and those with values above 100 ms are considered normal or healthy [15]. Patients with SDNN values above 100 ms had a 5.3fold lower risk of mortality than those with an SDNN of 50 ms [15]. ...
... Persons with values between 50 and 100 ms are considered somewhat restricted, and those with values above 100 ms are considered normal or healthy [15]. Patients with SDNN values above 100 ms had a 5.3fold lower risk of mortality than those with an SDNN of 50 ms [15]. In our study, baseline values were reduced in both groups: 45.7 ms in the NW&EB group and 41.7 ms in the G group. ...
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Objectives This open comparative study aimed to analyze the effects of a one-week vacation with various activity programs on well-being, heart rate variability (HRV) and sleep quality in healthy vacationers. Methods 52 healthy untrained vacationers spent a one-week vacation with regular exercise in East Tyrol. Exercise was performed on six of seven days. The study participants were divided into a) Group 1, playing golf (G), and b) Group 2 performing Nordic walking or e-biking (NW&EB). Well-being was measured with the WHO-5 well-being-index; stress and recovery status was obtained with the EBF-24-questionnaire (recovery-stress questionnaire). HRV-parameters were measured with a 24-hour-ECG (electrocardiogram). Sleep quality was derived from the EBF-24 questionnaire and sleep architecture from HRV-analysis. Examinations were performed one day before and after the vacation. Results Well-being significantly improved in the G group (+ 40%, p < 0.001) and NW&EB group (+ 19%, p = 0.019). The stress and recovery profile also improved significantly in both groups (stress-decrease: -43.7% G group; -44.7% NW&EB group; recovery-increase: +23.6% G group; +21.5% NW&EB group). Except for the SDNN (standard deviation of the NN interval), no significant change was noted in HRV-parameters. SDNN improved significantly only in the NW&EB group (+ 9%, p < 0.05). Sleep quality (+ 21% G group, p = 0.029; +19% NW&EB group, p = 0.007) and architecture (-10% G group, p = 0.034; -23% NW&EB group, p = 0.012) significantly improved in both groups. Conclusion A short-term vacation with regular exercise was well tolerated by the study participants and improved well-being, sleep quality, HRV and autonomic regulation. Trial registration Registry and the registration no. of the study/trial: Approval was received from the ethics committee of the Leopold Franzens University of Innsbruck (AN2013-0059 332/4.8)
... Spectral analysis [8]-also called frequency domain-is composed of three frequency ranges: low frequency (LF, 0.04 ± 0.15 Hz), high frequency (HF, 0.15 ± 0.4 Hz), and very low frequency (VLF, 0.003 ± 0.04 Hz). Power is the energy found in a frequency band [20]. The LF and HF powers are absolute powers, reported in units of ms² (square milliseconds). ...
... Spectral analysis [8]-also called frequency domain-is composed of three frequency ranges: low frequency (LF, 0.04 ± 0.15 Hz), high frequency (HF, 0.15 ± 0.4 Hz), and very low frequency (VLF, 0.003 ± 0.04 Hz). Power is the energy found in a frequency band [20]. The LF and HF powers are absolute powers, reported in units of ms 2 (square milliseconds). ...
... LFnu and HFnu are relative powers, called normalized, in the LF and HF bands, a derived index calculated by dividing LF or HF by an appropriate denominator representing the relevant total power: LFnu = LF/(LF + HF) and HFnu = HF/(LF + HF) [21]. HF power and HFnu represent parasympathetic activity [22] and are associated with RMSSD and pNN50 [20]. LF power is associated with SDNN [22] and represents both sympathetic and parasympathetic activity, but LFnu emphasizes control of sympathetic activity [8]. ...
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The reversibility of HRV abnormalities in hyperthyroidism remains contradictory. The design of this study involves conducting a systematic review and meta-analysis on the effect of antithyroid treatments on HRV in hyperthyroidism. PubMed, Cochrane, Embase, and Google Scholar were searched until 4 April 2022. Multiple reviewers selected articles reporting HRV parameters in treated and untreated hyperthyroidism. Independent data extraction by multiple observers was stratified by degree of hyperthyroidism for each HRV parameter: RR intervals, SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50 ms of variation), total power (TP), LFnu (low-frequency normalized unit) and HFnu (high-frequency), VLF (very low-frequency), and LF/HF ratio. We included 11 studies for a total of 471 treated hyperthyroid patients, 495 untreated hyperthyroid patients, and 781 healthy controls. After treatment, there was an increase in RR, SDNN, RMSSD, pNN50, TP, HFnu, and VLF and a decrease in LFnu and LF/HF ratio (p < 0.01). Overt hyperthyroidism showed similar results, in contrast to subclinical hyperthyroidism. Compared with controls, some HRV parameter abnormalities persist in treated hyperthyroid patients (p < 0.05) with lower SDNN, LFnu, and higher HFnu, without significant difference in other parameters. We showed a partial reversibility of HRV abnormalities following treatment of overt hyperthyroidism. The improvement in HRV may translate the clinical cardiovascular benefits of treatments in hyperthyroidism and may help to follow the evolution of the cardiovascular morbidity.
... One mechanism that has been proposed to contribute to the increased prevalence of CVD in the aging population is autonomic imbalance [13]. Functioning of the autonomic nervous system (ANS) can be assessed by various measurements of heart rate variability (HRV), which are indicative of the balance between sympathetic and parasympathetic nervous system activity [14]. Chronological aging has been linked to a progressive decline in HRV [15][16][17][18] and a decreased HRV is associated with a number of adverse cardiovascular outcomes such as heart failure, hypertension, and sudden cardiac death [19][20][21][22][23][24]. ...
... Parameters of HR and HRV are often investigated during a short electrocardiogram (ECG) measurement at the study center or in the hospital, but not continuously over a longer period while individuals continue with their daily lives. In addition, HRV is often measured using short-term (~5 min) time-domain metrics such as the SDNN [14]. However in the current study, we collected continuous ambulatory ECG measurements over a time period of 24 to 90 hours (h) in young and middle-aged participants from the Switchbox Leiden Study [36]. ...
... Additionally, we compare this group of middle-aged participants with younger controls from the general population to investigate the associations between parameters of HR, HRV, and 24-h HR rhythms with chronological age. We used detrended fluctuation analysis (DFA) as a measure for HRV, which is a nonlinear measurement to quantify the unpredictability of a time-series and novel for this type of study [14,37,38]. ...
... The mean spectral power was obtained for the low frequency (LF) band (0.04 to 0.15 Hz) and the high frequency (HF) band (0.15 to 0.4 Hz) and the ratio between them (LF/HF). The LF and HF indices were transformed to normalized units (LFn and HFn) [20]. Whereas LF is considered to reflect the cardiac response to both sympathetic and parasympathetic activities, HF is regarded as a reliable parameter of the vagal cardiac influence. ...
... frequency (HF) band (0.15 to 0.4 Hz) and the ratio between them (LF/HF). The LF and HF indices were transformed to normalized units (LFn and HFn) [20]. Whereas LF is considered to reflect the cardiac response to both sympathetic and parasympathetic activities, HF is regarded as a reliable parameter of the vagal cardiac influence. ...
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The aim of this work was to obtain insights of the participation of the autonomic nervous system in different stages of calcific aortic valve disease (CAVD) by heart rate variability (HRV) analysis. Studying subjects with no valve impairments and CAVD patients, we also sought to quantify the independent contribution or explanatory capacity of the aortic valve echocardiographic parameters involved in the HRV changes caused by active standing using hierarchical partitioning models to consider other variables or potential confounders. We detected smaller adjustments of the cardiac autonomic response at active standing caused specifically by the aortic valve deterioration. The highest association (i.e., the highest percentage of independent exploratory capacity) was found between the aortic valve area and the active standing changes in the short-term HRV scaling exponent α1 (4.591%). The valve’s maximum pressure gradient echocardiographic parameter was present in most models assessed (in six out of eight models of HRV indices that included a valve parameter as an independent variable). Overall, our study provides insights with a wider perspective to explore and consider CAVD as a neurocardiovascular pathology. This pathology involves autonomic-driven compensatory mechanisms that seem generated by the aortic valve deterioration.
... The time-domain parameters of HRV include mean R-R interval, the standard deviation of all normal R-R intervals (SDNN), the square root of the mean squared differences between consecutive R-R intervals (RMSSD), the standard deviation of successive differences (SDSD), and percentage of consecutive normal R-R intervals that differ by >50 ms (pNN50); in which, mean R-R interval & SDNN denotes overall HRV which represents both sympathetic and parasympathetic activity, and RMSSD & pNN50 correlates the HF component hence represents the parasympathetic activity (10). Recording of electrocardiography: Resting 12 Lead ECG was recorded in lying position using Clarity Med ECG 100D-3 channel electrocardiograph. ...
... Reduction in HRV is the earliest clinical indicator of CAN and a strong & independent predictor of increased mortality after acute myocardial ischemia. A rise in resting heart rate is another independent predictor of all-cause mortality (10). The decreasing trend of frequency-domain parameters of HRV has been noted right from the diagnosis of T2DM (17). ...
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Introduction: Cardiac autonomic neuropathy (CAN) is a frequent and intractable complication of diabetes. Reduction in heart rate variability (HRV) is the first sign of CAN in its subclinical stage. Autonomic imbalance and hyperglycaemia in diabetes are associated with cardiovascular structural and functional modifications which lead to left ventricular hypertrophy (LVH). The study was undertaken to assess the changes in HRV and left ventricular mass in type 2 diabetes mellitus (T2DM) patients. Material and methods: The case-control study was conducted on 78 T2DM subjects and 78 age & sex-matched healthy controls. CAN was assessed by frequency and time-domain parameters of HRV and LVH was measured using various ECG criteria including Cornell voltage, Cornell product, Sokolow-Lyon voltage, and Romhilt-Estes point score. Results: All the frequency and time-domain parameters of HRV except resting heart rate, normalized LF, and LF/HF ratio were significantly reduced in T2DM patients compared to healthy controls. The prevalence of ECG-LVH was 25.7% using any single criteria and 12.2% with all the criteria. The highest prevalence (24.3%) was noted with Cornell product and Sokolow-Lyon voltage criteria followed by Romhilt-Estes point score (17.6%), and Cornell voltage criteria (16.2%). Conclusion: Reduction in overall HRV with less high-frequency power and high LF/HF ratio are suggestive of parasympathetic dysfunction and sympathetic predominance. A significant LVH was noted with ECG-based electric criteria in T2DM patients. The study suggests that T2DM patients should be subjected to diagnostic HRV and ECG to identify the early occurrence of CAN and LVH.
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Post-traumatic stress disorder (PTSD) has been associated with cardiovascular disease (CVD), but the mechanisms remain unclear. Autonomic dysfunction, associated with higher CVD risk, may be triggered by acute PTSD symptoms. We hypothesized that a laboratory-based trauma reminder challenge, which induces acute PTSD symptoms, provokes autonomic dysfunction in a cohort of veteran twins. We investigated PTSD-associated real-time physiologic changes with a simulation of traumatic experiences in which the twins listened to audio recordings of a one-minute neutral script followed by a one-minute trauma script. We examined two heart rate variability metrics: deceleration capacity (DC) and logarithmic low frequency (log-LF) power from beat-to-beat intervals extracted from ambulatory electrocardiograms. We assessed longitudinal PTSD status with a structured clinical interview and the severity with the PTSD Symptoms Scale. We used linear mixed-effects models to examine twin dyads and account for cardiovascular and behavioral risk factors. We examined 238 male Veteran twins (age 68 ± 3 years old, 4% black). PTSD status and acute PTSD symptom severity were not associated with DC or log-LF measured during the neutral session, but were significantly associated with lower DC and log-LF during the traumatic script listening session. Long-standing PTSD was associated with a 0.38 (95% confidence interval, -0.83,-0.08) and 0.79 (-1.30,-0.29) standardized unit lower DC and log-LF, respectively, compared to no history of PTSD. Traumatic reminders in patients with PTSD lead to real-time autonomic dysregulation and suggest a potential causal mechanism for increased CVD risk, based on the well-known relationships between autonomic dysfunction and CVD mortality.
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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify properties of epileptic brain networks. In this study we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed from EEG recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
Chapter
This investigation was done on 10 healthy subjects which includes both males and females in the age set of 20‐30 years (mean: 24± 1.944 years). The examination was performed to survey the impact of graded HUT and HRT on the sympathetic and parasympathetic nervous systems. The tilt angles taken to record the data were 0 0 , 20 0 , 40 0 , 60 0 , 40 0 R, 20 0 R, and 0 0 R. After that, time domain and frequency domain parameters were analysed. On evaluated head‐up tilt (0 0 to 60 0 ), the mean heart rate, LF, and LF/HF showed a growing trend and the mean RR, NN50, pNN50, and HF showed a declining trend. On reversal of the tilt (60 0 to 0 0 R), all the parameters showed an inverse trend as that of the trend from 0 0 to 60 0 . These trends clearly depict that graded HUT leads to an escalation in sympathetic activity but a decrease in parasympathetic activity, which is clearly seen at higher tilt angles (20 0 to 60 0 ). When the tilt‐table was reversed, both the parasympathetic and sympathetic activity of ANS came back to regular pre‐tilt level. The tilt‐table test is basic and non‐invasive and can be easily utilized clinically for the analysis and affirmation of syncope dysfunction of the ANS.
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Limits to the usefulness of homeostasis as a guiding physiological principle are revealed by new mechanisms derived from study of nonlinear systems that generate a type of variability called chaos. Loss of complex physiological variability may occur in certain pathological conditions including heart rate dynamics before sudden death and with aging.
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Heart rate variability (HRV) is a medical index for morbidity and wellness. Lower HRV accompanies many illnesses; high HRV accompanies healthy states, resilience, and optimal functioning. Heart rate variability biofeedback (HRVB) uses real-time electronic feedback of the moment-to-moment changes in HRV to train patients to produce increases in HRV. Outcome studies on HRVB have shown therapeutic benefit for a wide variety of medical and mental health disorders. Lehrer and colleagues have published evidence-based protocols for HRV assessment and HRV treatment. Here, the authors review outcome studies on a sampling of common disorders: asthma, chronic muscle pain, depression, heart failure, hypertension, and posttraumatic stress disorder. HRVB offers promising therapeutic benefit for any medical or mental health disorder known to be accompanied by autonomic nervous system dysregulation.
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Assessment of heart rate variability (HRV) is a common approach to examine cardiac autonomic nervous system modulation that has been employed in a variety of settings. Frequently, both the root mean square of successive differences (RMSSD) and SD1, which is a Poincaré plot component, have been used to quantify short term heart rate variability. It is not typically appreciated however, that RMSSD and SD1 are identical metrics of HRV. As a reminder to clinicians and researchers who use and study HRV, we show both empirically and mathematically that RMSSD and SD1 are identical metrics. Because the homology between RMSSD and SD1 is not commonly known, the inclusion of both measures has been reported in many recent publications. The inappropriate use of such redundant data may affect the interpretation of HRV studies. This article is protected by copyright. All rights reserved.