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Épilepsie et coeur
Épilepsies 2010 ; 22 (3) : 194-200
Heart rate variability
as measurement
of heart-brain interactions
Mario Valderrama1, Vincent Navarro1,2, Michel Le Van Quyen1
Copyright © 2017 John Libbey Eurotext. Téléchargé par un robot venant de 88.99.165.207 le 06/05/2017.
1
Centre de recherche de l’Institut du cerveau et de la moelle épinière (CRICM)
INSERM UMRS 975 - CNRS UMR 7225-UPMC, hôpital de la Pitié-Salpêtrière, 47, Bd de l’Hôpital,
75651 Paris Cedex 13, France
<[email protected]>
2
AP-HP, Epilepsy Unit, groupe hospitalier Pitié-Salpêtrière, 47, Bd de l’Hôpital,
75651 Paris Cedex 13, France
Abstract.
Autonomic mediated heart-brain interactions are implicated in the regulation of different physiological and pathological situations. They reflect the influence of parasympathetic and sympathetic systems over the cardiac activity. In
past years, a considerable number of methodological approaches applied to non-invasive electrocardiogram (ECG)
recordings have been developed for the assessment of autonomic function. They are based on quantitative analyses of
the heart rate variability (HRV), which, through different measurements in time and frequency domains or through
non-linear estimations, have revealed useful information in diverse clinical contexts. In the present article, we provide a
review of the general aspects related to the analysis of HRV including a brief summary of the physiological bases and
methodological approaches together with some reported applications.
Key words : autonomic modulation, heart-brain interactions, heart rate variability
Résumé.
Les variabilités du rythme cardiaque, témoins d’interactions coeur/cerveau
Les interactions cœur/cerveau sont régulées par le système nerveux autonome et sont impliquées dans différentes
situations physiologiques ou pathologiques. Ces interactions sont médiées par l’influence des systèmes sympathiques
et parasympathiques sur l’activité cardiaque. Durant ces dernières années, un nombre considérable d’approches
méthodologiques, appliquées aux enregistrements non invasifs de l’électrocardiogramme (ECG), ont été développées
pour caractériser la fonction du système autonome. Ces approches sont fondées sur des analyses quantitatives de la
variabilité du rythme cardiaque (Heart rate Variability [HRV]), lesquelles, à travers différentes mesures dans le temps et
la fréquence ou à travers des estimations non linéaires, ont permis d’extraire des informations utiles dans divers
contextes cliniques. Dans cet article, nous présentons une revue des aspects généraux liés à l’analyse du HRV incluant un
bref rappel des bases physiologiques et des approches méthodologiques ainsi que quelques applications.
Mots clés: modulation du système autonome, interactions cœur-cerveau, variabilité du rythme cardiaque
Épilepsies, vol. 22, n° 3, juillet-août-septembre 2010
194
brain, the action of the second influences
and/or controls the behavior of all same parts
including the heart. Indeed, although the
activity of the heart depends on the intrinsic
rhythm of its peacemaker, the heart operation
doi: 10.1684/epi.2010.0323
Tirés à part :
M. Le Van Quyen
The heart and the brain are two of the most
important entities of the human body. While,
in general terms, the action of the first sustains
the supply of nutrients necessary for the functioning of all parts of the body including the
Copyright © 2017 John Libbey Eurotext. Téléchargé par un robot venant de 88.99.165.207 le 06/05/2017.
Heart rate variability as measurement of heart-brain interactions
Parasympathetic and sympathetic control centers in the
brain are located principally in the brainstem, hypothalamus
and spinal cord. Higher levels of the cortex can also activate
the autonomous system via signals to lower centers. Similarly,
visceral organs can induce autonomic reflex responses by a
complex mechanism of afferent-efferent signals. Contrarily to
sympathetic terminals that are distributed to all parts of the
heart, parasympathetic ones innervate principally the sinoatrial (SA) and atrioventricular (AV) nodes (Guyton and Hall,
2006). Both systems are constituted by preganglionic and postganglionic neurons, however, conversely to preganglionic
sympathetic fibers, preganglionic parasympathetic ones reach
directly the wall of the heart where they make synaptic junctions with postganglionic neurons. Parasympathetic fibers are
supplied to the heart by the vagus nerves. Cell bodies of vagal
neurons are located predominantly in the nucleus ambiguous,
the dorsal motor nucleus, and the intermediate zone between
these brainstem’s regions (Taylor et al., 1999). The action
of the parasympathetic cardiac fibers is mediated by the acetylcholine neurotransmitter which, when released, decreases,
among other effects, the frequency of the heart peacemaker.
Sympathetic cell bodies of neurons influencing the behavior
of the heart are located mostly in the medulla, from
where respective axons travel down the spinal cord, exit
at specific thoracolumbar levels and terminate upon the
postganglionic sympathetic neurons (Klabunde, 2004). The
sympathetic action to the heart is mediated primarily by
norepinephrine neurotransmitter. Sympathetic stimulation
causes, among others effects, an increase in the discharge rate
of SA node.
rate or simply the heart rate (HR) is determined by a balance
between influences coming from external sources located
principally at the level of the brain. The external impulses
innervating the heart correspond to two main branches of
the autonomic nervous system: the parasympathetic and
sympathetic systems. The action of these systems can induce
modifications of the heart activity in different ways and circumstances. For example, situations of mental stress (Murata
et al., 1999; Jouven et al., 2009), fear or anxiety (Baduí et al.,
1996; Berntson et al., 1998) can rapidly increase the HR. Similarly, normal physiological states like rest, exercise (Perini and
Veicsteinas, 2003) or sleep stages (Elsenbruch et al., 1999) are
correlated to different changes in the autonomic modulation of
the cardiac response. In addition, neuropathological conditions
such as epilepsy or major depressive disorder that may be associated with autonomic dysfunctions can also chronologically
modify the normal activity of the heart and increase the risk
of sudden and unexpected death (Davis and Natelson, 1993;
Carney et al., 2001; Nashef and Tomson, 2008).
The regulatory function of the autonomic system can be
quantitatively assessed by the analysis of the heart rate variability (HRV). HRV makes reference to the study of both the
variability of intervals between consecutive heart beats and
the variability between consecutive instantaneous heart rates
(Task Force, 1996). Due to its relatively simple implementation
methodology and the fact that it derived from a non-invasive
technique, the analysis of different HRV measurements has
became one of the most accepted and used procedures for the
study of the fluctuating balance between the sympathetic and
parasympathetic systems.
In the following sections we will review the main physiological and analytical aspects related to the study of HRV. We
will first shortly consider the physiological background of the
autonomic modulation of the HR, we will continue next with
the principal steps and methods for the quantitative assessment of the HRV, and finally, we will consider the relation of
some HRV measurements and sleep and particularly the fluctuations of autonomic activity during specific sleep stages.
The chronotropic effects (i.e., those that change the heart
rate) of sympathetic and vagus nerve electrical stimulation
have been experimentally studied in animals (Henning and
Khalil, 1989; Brack et al., 2004) and humans (Lewis et al.,
2001). Vagal stimulation experiments have shown that the
gradual increase of the stimulation frequency from ~ 0 – 20 Hz,
while hyperbolically decreases the heart rate (Hondeghem
et al., 1975; Parker et al., 1984), linearly increases the
pulse interval between consecutive beats (Parker et al., 1984),
suggesting that vagal activity instead of directly controlling
the heart rate, rather influences it by the regulation of the
time interval between successive beats (Hainsworth, 2004).
Similarly, experiments where cardiac sympathetic nerves have
been electrically stimulated have confirmed their positive
chronotropic effect of increasing the heart rate but, conversely
to vagal stimulation which can change the heart rate within
one cardiac cycle (Levy et al., 1993), sympathetic stimulation
entails a delay or latency before appreciating a change in the
heart rate response (Ng et al., 2001). In addition, experiments
where both autonomic branches have been simultaneously
stimulated have indicated that while the positive chronotropic
effect of sympathetic stimulation is reduced in the presence of
background vagal stimulation, the negative chronotropic effect
of vagal stimulation is on the contrary enhanced when
Autonomic regulation of heart rate
As mentioned above, the heart is innervated with parasympathetic and sympathetic nerves. The effects of both autonomic branches on the HR are, in general terms, opposite:
while the sympathetic stimulation tends to increase the HR,
the parasympathetic one tends to decrease it. In absence of
the external influence of any of these systems, the cardiac
rhythm is marked, in normal cases, by the intrinsic rhythmicity of the sinoatrial node (the heart peacemaker), which varies
from 100 to 120 beats per minute (bpm) (Hainsworth, 2004).
In resting state, although both systems are activated, there is a
predominance of parasympathetic influence which is more
important in supine positions (Perini and Veicsteinas, 2003)
and in trained individuals (Yamamoto et al., 2001).
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M. Valderrama, et al.
depolarization and repolarization of the atria and ventricles
(figure 1A). Among these waves, those forming the so-called
QRS complex, and more specifically the R wave, are usually
selected for the extraction of beat-to-beat intervals or R-R signal. Within a single cardiac cycle, the QRS complex represents
the depolarization of ventricles and so its detection can be useful for estimating the time interval between consecutive cycles.
Due to its high amplitude and sharpness, the time of occurrence of each cardiac cycle (tR) is commonly associated with
the maximal peak of the R wave and therefore a considerable
number of online and offline methods have been developed
for its detection (for review see Köhler et al., 2002). After
having detected all successive tR, the R-R intervals signal, also
known as R-R tachogram, is obtained by calculating the successive differences between consecutive tR (i.e., tR(i) - tR(i - 1)). An
example of an ECG signal and the extracted R-R intervals is
presented in figure 1. The R-R signal represents thus the evolution in time of the time duration between consecutive cardiac
cycles. These successive intervals are usually measured in seconds or milliseconds. From this R-R signal, a corresponding
instantaneous heart rate signal, with values in beats per minute,
can be obtained through the following conversion:
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background sympathetic stimulation is present, thus evidencing a dominant vagal role on chronotropic effects (Brack et al.,
2004).
As mentioned above, the activity of autonomic system is
regulated by a complex mechanism of efferent reflexes in
response to internal visceral afferent inputs. In the case of the
heart, there are different types of reflexes that induce chronotropic effects as a consequence of an increment or decrement
of the sympathetic or parasympathetic activity. Some of the
reflexes accompanied by an increased parasympathetic activity
are for example baroreceptors, which regulate the arterial blood
pressure and are generated in response to an increased arterial
pressure; cardiac reflexes are mediated by receptors in atria and
ventricles and are generated under an increased chamber
pressure which increases the parasympathetic activity and in
consequence reduces the HR (Klabunde, 2004). Additional to
these afferent-efferent system of visceral reflexes, other, diverse
reflex mechanisms related to common activities such as
breathing, physical exercise, changes in body position, etc.,
can also induce changes in HR (Hainsworth, 2004). In the
case of breathing, the respiratory sinus arrhythmia is a normal
arrhythmia associated with a synchrony between the respiration rate and the HR variability in which the beat-to-beat
interval is shortened during inspiration and prolonged during
expiration as a consequence of variations in cardiac vagal
efferent activity (Yasuma and Hayano, 2004).
bpmðiÞ ¼ 60=t R ðiÞ−t R ði−1Þ
where bpm(i) is an instantaneous estimation of the number
of beats that would be contained in one minute (figure 1C).
Although the instantaneous heart rate signal can be used, the
R-R signal is usually utilized for the processing of different HRV
measurements. Moreover, for the HRV estimation, it is recommended to select only normal beat-to-beat intervals, or the
so-called normal-to-normal or NN intervals, and not those
representing ectopic phenomena such as abnormal arrhythmias or their compensatory pauses (Task Force, 1996).
Higher cortical centers, especially those located in the limbic cortex, can also induce changes in HR. Some of changes
can be associated with the occurrence of external stimuli. In
the case of emotions (e.g., fear, anger, sadness) for instance,
the implication of regions such as the amygdala has been
correlated to chronotropic effects, with relative increases in
HR associated with negative stimuli and relative decreases
associated with positive ones (Critchley et al., 2005), or, in a
general manner, with an increase in HR associated with a
greater amygdala activation (Yang et al., 2007).
Time domain methods
The most representative methods for the analysis of HRV
in time domain can be divided in statistical and geometrical
methods. Some of the most common statistical methods
include: SDNN, the standard deviation of NN intervals;
SDANN, the standard deviation of the average NN interval,
usually calculated over short periods of 5 minutes; RMSSD,
the root mean square estimated from the differences of successive NN intervals; NN50, the number of differences of successive NN intervals greater than 50 ms; pNNSO, the proportion
calculated from the ration between NN50 and the total
number of NN intervals. Geometrical methods are based on
measurements that are estimated from the geometrical form
of, for instance, the distribution of NN intervals or the distribution of differences between consecutive NN intervals. Sample
density distributions can be approximated by the histogram
of respective data series and from them, measurements like
the HRV triangular index, which corresponds to the total
number of data points (i.e., total number of NN or differences
between consecutive NN intervals) divided by number of
data points in the modal bin, can be calculated. Geometrical
Heart rate variability measurements
The analysis of HRV measurements has been applied to the
study of different physiological or pathological contexts where
the assessment of the autonomic activation at particular times
has been required (Kleiger et al., 2005; Lewis, 2005). Those
analyses are derived from diverse methodological techniques
that have been developed for the estimation of the autonomic
function (parasympathetic and sympathetic) from the noninvasive electrocardiogram (ECG) signal (for a comprehensive
review on HRV analysis see Task Force, 1996).
ECG preprocessing: R-R intervals signal
Standard ECG recordings, for example those recorded from
bipolar lead I, II or III derivations (Guyton and Hall, 2006), are
shaped by particular waves that represent the sequence of
Épilepsies, vol. 22, n° 3, juillet-août-septembre 2010
196
Heart rate variability as measurement of heart-brain interactions
A
R
R−R
R
T
P
Q
1m V
0.5 sec
C
Instantaneous heart rate (bpm)
1
120
0.95
110
0.9
0.85
0.8
0.75
0.7
0.65
0.6
R − R interval (sec)
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B
100
bpm =
90
60
R − R (sec)
80
70
60
0.55
0
100
200
300
400
500
600
700
800
0.5
900 1 000
0
100
200
300
400
500
600
700
800
50
900 1000
Time (sec)
Time (sec)
Figure 1. R-R intervals and instantaneous heart rate signals from the non-invasive ECG.
(A) ECG recording with the main component waves. (B) R-R signal obtained from the time duration between successive cardiac cycles.
(C) Instantaneous heart rate in beats per minute (bpm) determined from the R-R intervals.
distributed with frequency (Marple, 1987). Commonly, two
different approaches have been implemented for the PSD
estimation: non-parametric and parametric methods. While
the first is usually based on the computation of the fast Fourier
transform (FFT), which is computationally faster, the second
requires the estimation of a parametric model (i.e., the estimation of the coefficients of the model), usually an autoregressive
model, that depends on additional parameters such as the type
and the order of the chosen model, but provides a smoother
function from where is usually easier to identify the contribution of each one of the spectral bands of interest. Depending
on the length of the R-R signal (preferentially the NN intervals), several spectral components have been defined which
are supposed to be associated with individual or interactive
activities of parasympathetic and/or sympathetic branches. For
short-time or long-time recordings (~ 5 minutes), the main
frequency bands have been defined as follows: high frequency
component (HF), which refers to the spectral power between
frequencies from ~ 0.15 to 0.4 Hz, is generally considered as a
marker of a respiration-mediated vagal modulation, determined in consequence by the frequency of breathing (Sztajzel,
2004); low frequency component (LF), associated with the
power between frequencies from ~ 0.04 to 0.15 Hz, reflects
generally influences from both autonomic branches (Lewis,
2005); very low frequency component (VLF), referred to the
methods depend on the chosen size of the bin but are less
sensible to the presence of not removed ectopic intervals than
statistical ones. Different variations of HRV time-domain
indexes have been correlated to diverse physiological or
pathological contexts. In general terms, a reduced HRV, when
compared to control sets, has been associated with pathology
(Stein and Kleiger, 1999) or negative prognostic implications
(Goldberger et al., 2001). This can be understood as the more
healthy the heart, the more variable its rhythmic activity or
the wider the range of possible reaction states. In postmyocardial infarction for instance, several studies have
revealed that a low statistical and/or geometrical index values
(e.g., SDNN or HRV triangular index) are associated with higher
risks of mortality (Wolf et al., 1978; Kleiger et al., 1987; Farrell
et al., 1992). This reduction on the variability has been suggested to reflect a modification of the normal sympatho-vagal
balance consisting in a decreased parasympathetic tone and an
increased sympathetic activity (Lombardi, 2004).
Frequency domain methods
Frequency domain measurements constitute probably one
of the most employed tools for the analysis of the HRV. They
are based on the estimation of the power spectral density (PSD)
function, which describes how the variance (i.e., power) is
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Épilepsies, vol. 22, n° 3, juillet-août-septembre 2010
M. Valderrama, et al.
(e.g. sleep spindles) that alternate with a rather low-amplitude
desynchronized background. During deep stages, the highamplitude slow activity becomes predominant and synchronized across large cortical extents. REM sleep is on the contrary
characterized by a low-voltage desynchronized activity that is
accompanied by an absence of muscular tone. When the autonomic regulation during these physiological states has been
investigated, clear differences between different HRV quantifications have been documented. In the case of spectral indexes
for instance, one study has shown an increase of the power in
the HF component in light sleep accompanied by a simultaneous reduction of VLF and LF components, suggesting a concurrent increment of parasympathetic tone with a decrement
of sympathetic activity during this stage of sleep (Vaughn
et al., 1995). This same study reported a decrease in the power
of all spectral components during deep sleep, which was associated with a reduction of both autonomic influences, and an
increase of power in the VLF and LF components during REM
sleep, explained by a possible increase in sympathetic influence. Although these results are consistent with those found
by other studies, some of them have reported nevertheless an
explicit increase of the power in the HR component during
deep sleep, reflecting a preferential parasympathetic influence,
as well as an explicit decrease of the same spectral component
during REM, suggesting a supplementary parasympathetic
withdraw during this stage (Baharav et al., 1995; Elsenbruch
et al., 1996; Toscani et al., 1996). In two studies, the spectral
power activity of the EEG has been correlated to the spectral
components of the HRV (Ako et al., 2003; Miyashita et al.,
2003). In both, the EEG spectral components related to the
presence of absence of slow waves have been statistically significant correlated to the activity of LF and LF / HF components but not to the HF one, suggesting that the autonomic
regulation during sleep is especially influenced by sympathetic
or sympatho-vagal balance fluctuations rather than by a predominant vagal influence. In order to appreciate the cardiac
autonomic fluctuations during sleep, figure 2 presents the
simultaneous evolution of some HRV indexes in time and frequency domains during successive NREM and REM stages of a
seizure-free night corresponding to an epileptic patient following an EEG-Video examination. The alternating sleep stages
can be clearly appreciated in the scalogram (figure 2 Top),
which represents the power content of different frequency
bands as a function of time. In particular, during deep sleep
periods of NREM sleep, characterized by a strong presence of
slow waves (red boxes in figure 2) as can be confirmed by the
high power in low frequency bands (~ 0.3 – 3 Hz), the instantaneous heart rate and the standard deviation of consecutive
beat-to-beat intervals (SDNN) decrease, reflecting a slow and
regular heart rhythm respectively, which, in turn, indicates a
presumably preferential respiration-mediated vagal influence
on the heart rate, manifested by a prominent increase of the
HR spectral component at the same time of a notorious decrement of VLF and LF indexes (green ellipse in figure 2). On the
contrary, during REM sleep (blue boxes in figure 2) the heart
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power of frequencies below ~ 0.04 Hz, is a major determinant
of physical activity and has been proposed to reflect a marked
sympathetic activity (Sztajzel, 2004). When long-time recordings are available, one additional spectral component can be
determined associated with frequencies lower than 0.003 Hz:
ultra-low frequency component (ULF). Additionally, the ratio
LF / HF is considered as an index of the global sympathovagal balance (Malliani et al., 1991).
As time domain measurements, frequency domain ones
have also been applied in different clinical and physiological
contexts. In experiments where HRV has been investigated
under diverse physiological conditions such as different corporal positions at rest, altitude, exercise, water immersion, light
exposure, sleep and others, frequency domain indexes have
revealed to be valuable markers of the autonomic interaction,
with sensible variations of corresponding spectral components
related to changes in levels of parasympathetic or sympathetic
activations (Toscani et al., 1996; Tsunoda et al., 2001; Perini
and Veicsteinas, 2003). Similarly, pathological conditions
such as epilepsy (Harnod et al., 2009), depression (Carney
et al., 2001), or different cardiovascular diseases (Sztajzel,
2004), have also appeared to present alterations of spectral
components that have been related to an abnormal autonomic
regulation (Malliani and Montano, 2004).
Complementary HRV analysis methods
Besides time and frequency domain indexes, complementary approaches based on non-linear dynamics or timefrequency representations have also been applied to the
analysis of HRV. Non-linear approaches attempt in general to
extract information from the complex structure of the R-R
signal, which is not readily available with other types of
measurements. Among the most common non-linear methods,
power-law slope, detrended fluctuation analysis (DFA) and
approximate entropy have proved to be informative in clinical
contexts (for a review see Mäkikallio et al., 2004).
Heart rate variability: an application to sleep
During sleep, the evolution of different HRV indexes is
marked by the presence of sleep stages. These changes reflect
the fluctuation of the autonomic regulation during different
physiological brain states. In general terms, numerous studies
have shown that while a marked vagal modulation, mediated
by the respiratory activity, is associated with non-rapid eye
movement (NREM) sleep, an increased sympathetic activity is
observed during rapid eye movement (REM) periods of sleep
(Baharav et al., 1995; Vanoli et al., 1995; Vaughn et al., 1995;
Toscani et al., 1996; Scholz et al., 1997; Elsenbruch and Harnish, 1999). NREM sleep can be subdivided in light and deep
stages. During light stages, the electroencephalogram (EEG)
presents a succession of short periods of synchronized highamplitude slow activity accompanied by faster oscillations
Épilepsies, vol. 22, n° 3, juillet-août-septembre 2010
198
Heart rate variability as measurement of heart-brain interactions
Slow waves
60
15
40
10
Power
Frequency (Hz)
Sleep spindles
20
5
0
Mean ins. heart
rate (bpm)
VLF
LF
HF
00
01
02
04
03
Frequency domain
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SDNN
Time domain
Mean beat-tobeat (sec)
05
Time (hours)
Figure 2. Evolution of selected HRV indexes in time and frequency domain during sleep. HRV measurements were calculated for shortwindows of 5 minutes with a sliding step of 5 seconds.
See explanations in the text.
the cardiac activity related to different neurological pathologies such as epilepsy where it is suspected to be one of the
cause of sudden and unexpected deaths. However, despite
these useful or potential applications, further studies are
still required in order to better explore i) physiological significance of most of the HRV developed indexes, especially the
complex ones, ii) new measurements of the simultaneous
evolution of brain and heart activities which will permit to
improve the knowledge of interactions between heart and
brain.
rhythm becomes more irregular, as appreciated in the SDNN
measurement, and the decrement of the HF index accompanied by a simultaneous increment of VLF and LF ones suggests
a change in the sympatho-vagal balance, consisting in a
presumably vagal withdraw with a concurrent increment of
the sympathetic influence (purple ellipse in figure 2).
Conclusion
□
The analysis of HRV through the different methodological
approaches presented provides valuable information about the
cardiac autonomic modulation in diverse physiological and
clinical situations. Additional to be quantitative indexes
indicating fluctuations of normal or abnormal autonomic
activities, HRV measurements are valuable markers of good
prognostic of the outcome of particular cardiovascular-related
diseases. Similarly, as being derived from non-invasive,
routinely acquired ECG recordings, HRV analysis allows to
study, over long-term, the altered autonomic regulation of
Conflict of interest: non declared.
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