Hayano and Yuda Journal of Physiological Anthropology
https://doi.org/10.1186/s40101-019-0193-2(2019) 38:3

REVIEW

Open Access

Pitfalls of assessment of autonomic
function by heart rate variability
Junichiro Hayano*and Emi Yuda

Abstract
Although analysis of heart rate variability is widely used for the assessment of autonomic function, its fundamental
framework linking low-frequency and high-frequency components of heart rate variability with sympathetic andparasympathetic autonomic divisions has developed in the 1980s. This simplified framework is no longer able todeal with much evidence about heart rate variability accumulated over the past half-century. This review addresses
the pitfalls caused by the old framework and discusses the points that need attention in autonomic assessment byheart rate variability.
Keywords: Heart rate variability, Autonomic nervous system, Respiratory sinus arrhythmia, Cardiorespiratory
coupling, Spectral analysis

Background
Analysis of heart rate variability (HRV) is widely used as astandard method for assessing autonomic nervous functions. Particularly, low-frequency (LF, 0.04–0.15 Hz) andhigh-frequency (HF, 0.15–0.4 Hz) spectral components of
HRV are used as the separate metrics of sympathetic andvagal (parasympathetic) functions [1, 2]. The association
between HRV and autonomic function was first reported in
the 1970s [3–6], and the currently prevailing frameworks
of interpreting the HRV frequency components (LF and
HF) in relation to the autonomic divisions were establishedin the 1980s [7–9]. Over the next half-century, more than
28,000 HRV studies were published, including a lot of evidence indicating important limitations of HRV frequency
components as autonomic indices [10–12]. However, it
does not seem that enough attention has been paid to such
alarming evidence even by recent studies using HRV.
Analysis of HRV frequency components (LF and HF)
provides a powerful tool for evaluating autonomic nervous function by simply recording an electrocardiogram(ECG). Furthermore, devices that automatically analyze
HRV from ECGs, and also from pulse waves, are widelyused in recent years. As a result, using the autonomic
nervous index output there without criticism raises the* Correspondence: hayano@med.nagoya-cu.ac.jp
Department of Medical Education, Nagoya City University Graduate School of
Medical Sciences, 1 Kawasumi Mizuho-cho Mizuho-ku, Nagoya 467-8602,
Japan

risk of leading a wrong conclusion or a judgment that may
harm health. Considering the recent knowledge of themechanisms that generate HRV, the framework directly
associating the HRV frequency components with thedivisions of the autonomic nervous system (sympathetic
and parasympathetic) has become too simplistic and it
may be the time of reconstruction. This review will discuss
the pitfalls where studies using HRV for autonomic assessment may fall based on the accumulated evidence of HRV.

Main text
General problems in the assessment of autonomic
functions by HRV
Locality of autonomic neural regulations

The problem often seen in the studies of autonomic nerve
function by HRV is interpretation as if HRV reflects theautonomic state of the whole body. The signals generating
HRV are basically originated in the brain and mediatedthrough the sympathetic and vagal nerves that innervate
the sinoatrial node [13, 14]. Thus, the autonomic functions reflected in HRV are those regulating the pace-making function of the sinoatrial node. The autonomic
nervous functions of other organ systems cannot beknown from HRV. For example, food intake enhances
parasympathetic (vagal) activity to stimulate digestive
functions but it suppresses vagal activity to the heart to increase heart rate to a level that meets circulatory demandfor digestion and absorption [15]. Although sympathetic© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Hayano and Yuda Journal of Physiological Anthropology(2019) 38:3

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regulation may parallel among cardiac, renal, and muscle
sympathetic nerves [16, 17], parasympathetic regulation
is by function and therefore HRV cannot be used for estimating parasympathetic regulation other than that for
cardiac pace-making function.

Short-term and long-term HRV analyses

Analysis of HRV is classified into two categories by thelength of data recording [2]. Short-term HRV is typicallycalculated over 5 min, and the magnitude of frequency
components obtained by spectral analysis is used for autonomic function assessment. Long-term HRV is computed
over nominal 24 h and time-domain [18] and frequencydomain [19] indices, nonlinear indices [20–23], andindices obtained by signal averaging [24–26] are usedmainly for mortality risk prediction.
However, there is a more important difference between
short-term and long-term HRV than the length of thedata. For short-term HRV, data are usually recorded
under monitoring while keeping subjects and measurement environment in specified conditions, while longterm HRV data are usually recorded by wearable sensors
in subjects who are freely moving under daily activities.
This difference is important for the interpretation of
long-term HRV and the mechanisms by which it relates
to clinical risks such as mortality.
For example, decreased 24-h standard deviation of
normal-to-normal R-R interval (SDNN), representative
long-term time-domain HRV, is an increased mortality
risk after acute myocardial infarction [18, 20]. This relationship is often described as cardiac vagal dysfunction
to increase the vulnerability of the heart to ventriculartachycardia and fibrillation, but the autonomic nervous
function may not be a sole determinant of 24-h SDNN. In
a recent study using the big data of simultaneously
recorded 24-h ECG and tri-axial accelerograms, we foundthat the major determinant of 24-h SDNN is day/nightdifference in R-R interval that was associated with thelevel of daytime physical activity [27]. This suggests thatthe association between reduced SDNN and adverse prognosis may be partly due to health conditions that restrict
daily physical activity. Also, LF-to-HF power ratio (LF/HF)
has been widely used as autonomic index, and it is wellknown that LF/HF increases with standing [7, 8, 28]. The
major studies of the association between long-term HRV
and disease prognosis reported that a decrease in LF/HF
is an increased mortality risk [20, 22, 29]. In recent studies
using big data of long-term HRV and accelerogram, weobserved that the LF/HF reduces with the increasing lying
period during 24 h (Fig. 1) [30, 31]. Decreased LF/HF inpatients with adverse prognosis may reflect their daily
life spending a long time in lying position due tohealth problems.

Fig. 1 Association between LF/HF and lying ratio during 24-hmonitoring in 18,944 men and 23,539 women who underwent 24-h
Holter ECG monitoring with tri-axial accelerogram to assess physicalactivity and body position. Data were obtained from 24-h long-term
HRV database of the Allostatic State Mapping by Ambulatory ECG
Repository (ALLSTAR) project. (Revised figure in reference [31])These indicate that long-term HRV is affected by physical activity and posture during the recording period andthat the relationships with autonomic functions known
for short-term HRV cannot be extended to the interpretation of long-term HRV. To use long-term HRV toevaluate autonomic nervous function during daily life, it isnecessary to monitor at least physical activity and postureand analyze HRV in relation to them.
Separation of autonomic divisions by frequency
Why 0.15 Hz?

LF and HF component of HRV is divided at 0.15 Hz.
Classification at this frequency was proposed by Sayers[5] and supported by animal experiments [32, 33].
Among those, a study by Berger et al. [33] performed
the transfer function analysis of cardiac sinus rate response to the broad-frequency electric stimulation of thesympathetic and vagal nerves in dogs. They found thatthe sinus node responds as a low-pass filter to fluctuations in either sympathetic or vagal tone, while the filter
for sympathetic stimulations has a lower corner frequency (0.15 Hz) than for vagal stimulations (> 0.5 Hz).
It is believed that the difference in frequency characteristics between sympathetic and vagal heart rate controls
arises from the difference in the signaling mechanisms
between beta-adrenergic and cholinergic receptors.
Changes in sympathetic activity cause the effects throughseveral phosphorylating enzymatic processes in the intracellular signal transduction mechanisms existing downstream

Hayano and Yuda Journal of Physiological Anthropology(2019) 38:3

of beta-adrenergic receptors. Consequently, the sympathetic modulation of heart rate cannot transfer fast fluctuations (> 0.15 Hz). On the other hand, changes in vagal
activity cause the effects simply by the conformation
change of the membrane potassium channels with Ach.
Consequently, the vagal modulation of heart rate can transfer fluctuations up to a higher frequency range. The cutoff
frequency of 0.15 Hz, however, is the value obtained fromdogs, and there is no convincing evidence as to whether thevalue applies to humans of all ages uner all conditions.
Sinus arrhythmias constituting frequency component of
HRV

The message brought about by studies showing the difference in frequency characteristics between sympathetic
and vagal heart rate regulations is often over-interpreted.
It is not saying that the magnitude of HF component of
HRV reflects cardiac vagal function but is only sayingthat when heart rate fluctuations mediated by autonomic
nerves are observed in HF band (> 0.15 Hz), it is mediated by the cardiac vagus. Indeed, even if HRV in HF
band decreased or disappeared, it may not necessarilymean a withdrawal of cardiac vagal activity or cardiac
vagal dysfunction. This could happen when the respiration frequency is out of the HF band, such as slow
breathing < 0.15 Hz (9 breath/min) like during deep
breathing, or fast breathing > 0.4 Hz (24 breath/min)during exercise and in children. Also, even when heart
rate fluctuation is observed in HF band (> 0.15 Hz), if it
is not mediated by autonomic nerves (as discussed in
the next section), the framework of frequency separationof the autonomic heart rate regulation cannot be used.
Considering the knowledge of the mechanisms thatgenerate HRV, the framework to directly associate the
HRV frequency with the divisions of the autonomic nervous system (sympathetic and vagal) is too simplistic
and already classical. HRV should be described at least
dividing it into individual fluctuations from the corresponding physiological mechanisms. It is well known
that there are two major components in short-term
HRV; respiratory sinus arrhythmia (RSA) and a fluctuation at ~ 0.1 Hz called Mayer wave sinus arrhythmia(MWSA) [2, 7, 8]. The autonomic mechanisms of RSA are
discussed in later sections. The mechanism mediating theheart rate fluctuation at ~ 0.1 Hz is believed to be thebaroreceptor reflex cardiovascular regulation system, bywhich the fluctuation of arterial blood pressure at ~ 0.1
Hz that are known as Mayer wave [34] is reflected in
the fluctuation of heart rate [12, 35, 36]. Therefore,
we named this HRV component “Mayer wave sinus
arrhythmia [15, 37].”
As mentioned above, when respiration frequency is< 0.15 Hz, RSA becomes a part of the LF component andthe association between HF component and cardiac vagal

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function is lost. Additionally, when consciously breathingat ~ 0.1 Hz, it may cause resonance among respiration,
heart rate, and blood pressure through the baroreceptor
reflex mechanism, by which RSA and MWSA are merged
into a large single oscillation at ~ 0.1 Hz [38, 39].
Non-vagal HRV component in HF band

Even if HRV is observed in the HF band, it may notnecessarily be mediated by autonomic nerves. Thisphenomenon has been known for a long time and has
been reported in different terms including complex
HRV [40, 41], sinus node alternans [42], erratic heart
rate [43, 44], and heart rate fragmentation (HRF; wewill use this term in this review) [23, 45, 46]. This is a
type of sinoatrial instability characterized by frequent(sometimes every beat) appearance of peak and valley in
R-R interval time series despite that ECG shows sinus
rhythm. By this phenomenon, switching between increases
and decreases in heart rate occurs more frequently thanthose expected for that mediated by the vagus.
To quantify the level of HRF, Costa et al. [45, 46] developed a set of metrics including the percentage of theinflection point (PIP) and symbol dynamics [45]. Using
these measures, they demonstrated that the level of HRF
increases with advancing age and in patients with coronary artery disease [45] and that its increase is anincreased risk of cardiovascular events and death [23].
Because the existence of this phenomenon appears asincreases in the conventional HRV metrics such as thepercentage of pairs of adjacent normal-to-normal R-R
intervals differing by > 50 ms (pNN50), the square rootof the mean of squared differences between adjacentnormal-to-normal R-R intervals (RMSSD), and HF
power, it may confound their associations with cardiac
vagal function and with disease prognosis. In fact, thisphenomenon is considered a potential cause of lesser
prognostic relevance of the HF component than that ofother HRV frequency components such as very-low-frequency (0.0033–0.04 Hz) and LF components [43].
Although the exact mechanisms for this phenomenon are
unclear, alternative pacemaker shift within the sinus nodecaused by sinoatrial degeneration or disorganization may
be a potential mechanism.
RSA and cardiac vagal function
Non-vagal factors influencing RSA

Although the association between HF component ofshort-term HRV and cardiac vagal function depends on
the neural mechanisms of RSA [47, 48], many factors
are known to influence the magnitude of RSA besides
the cardiac vagal function. An increase in respiratory
frequency decreases the amplitude of RSA (the range of
excursion of R-R interval with respiration) [49–51]. Also,an increase in tidal volume increases RSA amplitude

Hayano and Yuda Journal of Physiological Anthropology(2019) 38:3

[49–53]. These effects of respiratory parameters on the
RSA operate independently of the level of cardiac vagal
activity [54]. Therefore, when using RSA amplitude toevaluate cardiac vagal function, it is desirable to control
respiration parameters in experimental protocol or toadjust their effects statistically.
Also, RSA amplitude may be affected by sympathetic
nervous activity. Even though the sympathetic nervous
system cannot transfer HRV > 0.15 Hz [33], it may restrict
the magnitude of cardiac vagal modulations of heart rate[55, 56]. Taylor et al. [55] observed that beta-adrenergic
blockade increases RSA amplitude over a wide frequency
range including > 0.15 Hz and concluded that cardiac
sympathetic outflow reduces heart rate oscillations at all
frequencies including RSA.
Neural mechanisms of RSA

Despite that RSA amplitude is widely used to estimate
cardiac vagal activity that is controlling the level of heartrate, RSA amplitude and the level of heart rate are
known to be controlled by different groups of vagal
motor neurons [57]. Preganglionic cardiac vagal motor
neurons are known to locate separately in the dorsal
motor nucleus of the vagus (DVN) and in the nucleus
ambiguous (nA) [57]. In cats and rats, C-fiber cardiac
preganglionic neurons in the DVN shows regular ongoing activity that is unaffected by respiratory rhythm,
while those in the nA fire with respiratory rhythm [58].
During embryonic development, the cardiac preganglionic neurons forming nA migrate ventrolaterally froma dorsomedial position, possibly of the DVN [59]. In
axolotl, this neuron relocation occurs with the metamorphosis of the onset of air-breathing [60]. These suggestthat during the evolution of air-breathing vertebrate, thenA was developed to “generate” RSA in addition to the
DVN to control the level of heart rate.
Biological function of RSA

Beside its relationship to cardiac vagal function, RSA
itself is recognized as a physiological mechanism toensure optimal ventilation-perfusion matching within
the lungs of air-breathing mammals [11, 57, 61–63]. A
similar matching of the counter-perfusions with waterand blood of the gills is also known in aquatic animals
without the nA, but this matching is accomplished simply by adjusting the level of heart rate with ventilatory
gill movements [64]. In air-breathing animals with reciprocal respiration, the presence of RSA may improve pulmonary gas exchange efficiency by matching the timingof alveolar air volume and alveolar perfusion within each
respiratory cycle (Fig. 2) [61]. Because this mechanism
could reduce the intrapulmonary shunt ratio by reducing
ineffective heartbeats when the alveoli are contracting
and it could also suppress the functional dead space byPage 4 of 8

reducing wasted inspiration when alveolar perfusion issmall, RSA seems a biological function to save theenergy for circulation and respiration at rest—the period
when the energy saving of the pulmonary circulation ismore important to survive against hunger than responding
to the demand for systemic circulation [11].
Possible reason why RSA is associated with cardiac vagal
activity

If RSA and heart rate are regulated by different vagal
motor neurons, direct relationship may not be expected
between the magnitude of RSA and cardiac vagal activity
to control heart rate. In fact, Goldberger et al. [65, 66]
demonstrated the evidence of dissociation between RSA
magnitude and cardiac vagal activity. They observed thatbradycardia caused by cardiac vagal activation throughbaroreceptor stimulation with phenylephrine that increases arterial blood pressure is accompanied by aparadoxical decrease in RSA amplitude. This indicatesthat the degree of respiratory modulation of cardiac vagal
outflow is not always parallel to cardiac vagal tone (meanactivity level).
RSA as an indicator of intrinsic resting function

Nevertheless, why is the RSA amplitude widely used as
an indicator of cardiac vagal activity? This is probably
because RSA is an intrinsic resting function that appears
when an individual is about to rest. In such situations,the nA and DVN are expected to work cooperatively forthe common purpose—energy saving (Fig. 3). Thisapparently indicates that RSA amplitude is a limited indicator of cardiac vagal activity, but at the same time, itsuggests that RSA amplitude may be a useful indicatorof resting function. The latter assumption seems supported by much evidence about the factors affecting RSA
amplitude; i.e., RSA increases with supine posture [7],relaxation [67], and sleep [68], decreases with standing[7, 8, 28], physical exercise [69–71], mental stress [72],cardiovascular stress including baroreceptor stimulation [65, 66], advancing age [73], and compromised cardiac function [37, 74].
LF component and sympathetic function

Although the power of the LF component and its derived
indices (normalized LF power and LF/HF) have been
widely used as an indicator of sympathetic functions(activity, predominance, sympatho-vagal balance, etc.), thevalidity of this use has been denied by a lot of evidence
accumulated by numerous studies [12]. Unlike the studies
specific to RSA in the HF component, this conclusion
does not change, even if the discussion is specified to
MWSA. Earlier studies proposed the LF as sympathetic
component [8], but the power of LF spectrum is not eliminated completely by beta-adrenergic blockade and rather

Hayano and Yuda Journal of Physiological Anthropology(2019) 38:3

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Fig. 2 Schema of the effects of physiological RSA and its inversion (inverse RSA) on the relationship between alveolar gas volume and capillary
blood flow during inspiration and expiration. Horizontal red bows and vertical green arrows indicate the volume of blood flow and the directionof gas flow, respectively. Physiological RSA improves respiratory gas exchange efficiency through matching between alveolar ventilation andcapillary perfusion throughout the respiratory cycle, while the inversion of the relationship (inverse RSA) results in increased alveolar dead space(wasted ventilation) and increased intrapulmonary shunt. (Revised figure in reference [61])Fig. 3 Model of central regulations RSA and the level of heart rate by preganglionic cardiac vagal motor neurons. RSA is generated by the phasic
control system located in the nucleus ambiguous (nA) that regulates the amplitude of respiratory modulation of cardiac vagal outflow, while
bradycardia is derived by the tonic control system located in the dorsal motor nucleus of the vagus (DVN) that regulates average cardiac vagal
tone. These systems work independently of HRV of each other and are stimulated (solid arrows) or inhibited (broken arrows) by different kinds of
inputs. However, both systems appear to work in parallel and to link with each other, whenever the cardiac autonomic state changes along withthe rest-strain axis. (Revised figure in reference [61])Hayano and Yuda Journal of Physiological Anthropology(2019) 38:3

largely reduced by sinoatrial parasympathectomy [75].
Although the increase in LF power with standing orhead-up tilting is often cited as the evidence for its association with sympathetic activity, postural increase in LF
power is observed only in one-third of healthy subjects,
no significant change in other third, and rather decreases
in the remaining third [28]. While LF/HF increases withstanding, this is mainly due to the postural decrease in HF
power. Finally, LF power is not related to cardiac sympathetic innervation quantified by positron emission tomographic neuroimaging [36, 76]. There is no longer a
convincing physiological basis justifying the assessment ofcardiac sympathetic nervous functions whether by
absolute or normalized LF power or LF/HF.

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Authors’ contributions
EY conceived the framework of the review and JH drafted manuscript. Bothauthors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicableCompeting interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Received: 12 February 2019 Accepted: 5 March 2019Conclusions
This paper described the major pitfalls of the assessmentof autonomic function by HRV. Overall, HRV tends to be
used beyond the limit of its capacity as a quantitative
measure of autonomic function. Particularly, the framework to associate the HRV frequency components (LF andHF) with the divisions of the autonomic nervous system(sympathetic and parasympathetic) is already too simplistic, and it is the major cause of the pitfalls.
On the other hand, even if HRV is a limited measureof autonomic functions, it is still a fact that HRV is
there. Except for some exception such as HRF, HRV isthought to originate from the brain and to transfer tothe heart through the autonomic nervous system. Like
the radio that extracts information from radio waves
modulated by the broadcasting station, HRV analysis canintercept the information for circulatory regulation
generated by the brain. We will be able to expect thecontinued development of HRV researches by aiming
for a faithful and unbiased extraction of the information contained in HRV beyond the classic framework of
LF and HF.
Abbreviations
Ach: Acetylcholine; DVN: Dorsal motor nucleus of the vagus;
ECG: Electrocardiogram; HF: High frequency; HRF: Heart rate fragmentation;
HRV: Heart rate variability; LF: Low frequency; LF/HF: LF-to-HF ratio;
MWSA: Mayer wave sinus arrhythmia; nA: Nucleus ambiguous;
PIP: Percentage of inflection point; pNN50: Percentage of pairs of adjacentnormal-to-normal R-R intervals differing by > 50 ms; RMSSD: Square root of
the mean of squared differences between adjacent normal-to-normal R-R intervals; RSA: Respiratory sinus arrhythmia; SDNN: Standard deviation of
normal-to-normal R-R interval
Acknowledgements
None.
Funding
There is no funding source for this paper.
Availability of data and materials
Not applicable

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