The Open Neurology Journal, 2010, 4, 39-49 39

1874-205X/10 2010 Bentham Open
Open Access
Autonomic Dysfunction and Risk Stratification Assessed from Heart Rate
Pattern
A. Günther*, O.W. Witte and D. Hoyer
Department of Neurology, Friedrich-Schiller-University of Jena, Erlanger Allee 101, D-07747 Jena, Germany
Abstract: The modulation of the autonomic nervous system (ANS) under physiological and pathophysiological condi-
tions is in focus of recent research. Many patients with cardio- and cerebrovascular diseases display features of sympatho-
vagal dysregulation. Measuring specific ANS parameters could improve risk stratification. Thus, the early diagnosis of
ANS dysfunction in these patients poses a great challenge with high prognostic relevance.
The most relevant methods and measures of Heart Rate Variability (HRV) analysis and HRV monitoring will be described
in detail in this chapter. The grown importance of these easily obtainable heart rate patterns in stratifying the risk of pa-
tients with myocardial infarction and heart failure as well as ischemic stroke will be demonstrated based on recent clinical
studies. In order to perspectively improve clinical management of these patients further large scale clinical investigations
on the role of ANS dysfunction will be useful.
Keywords: Autonomic nervous system, heart rate variability, heart failure, stroke.
1. INTRODUCTION
Recent research has made important progress in charac-
terizing the role of the autonomic nervous system (ANS)
under physiological and pathophysiological conditions as an
integrative component in the interorgan interplay.
Early diagnosis of ANS dysfunction within intensive care
patients poses a challenge as the majority will go on to dis-
play features of sympatho-vagal dysregulation during the
course of the disease (e.g. [1]).
Through the measurement of specific ANS parameters
clinicians and researchers are enabled to improve cardiovas-
cular and cerebrovascular risk stratification.
The following chapter briefly outlines ANS functions and
interactions and summarizes the most relevant methods of its
measurement through Heart Rate Variability (HRV) analy-
sis. Prognostic evaluation of critical care patients using such
a cost and time-efficient tool as a Holter-ECG in conjunction
with clinical scores have been assessed in a number of clini-
cal studies. Here, we will review the present evidence from
these studies for such highly relevant pathophysiological
conditions of heart failure and myocardial infarction with
focus on prognosis of sudden cardiac death, and stroke with
a special emphasis neurological outcome related to ANS
dysfunction.
2. THE AUTONOMIC NERVOUS SYSTEM (ANS)
Autonomic nervous system (ANS) responses to (patho-)
physiological stimuli have been characterized by a complex
interaction between the different components of the ANS on
the one hand, and visceral organs and (involuntary)


*Address correspondence to this author at the Department of Neurology,
Friedrich-Schiller-University of Jena, Erlanger Allee 101, D-07747 Jena,
Germany; Tel: ++4936419323417; Fax: ++4936419323402;
E-mail: albrecht.guenther@med.uni-jena.de
innervated glands on the other hand. In conjunction with the
sensory and motor nervous system, the autonomic nervous
system (ANS) is responsible for fast, continuous and nor-
mally subconscious control of basic physiological functions,
such as heart rate, blood pressure, respiratory rate, body
temperature and gastrointestinal motility. Forming a func-
tional network of peripheral and central mechanisms
throughout the body the autonomic nervous system, how-
ever, has two basic divisions: the parasympathetic and the
sympathetic component. The classical thinking that para-
sympathetic and sympathetic nervous system are usually
acting in opposition holds true for many ANS functions as in
cardiovascular, respiratory as well as gastrointestinal and
urinary tract innervations and reactions. But in many other
situations the two divisions work synergistically in response
to a simultaneous sympathetic and vagus nerve stimulus.
The ANS consists of efferent neurons running in series to
innervate smooth musculature of all viscerals and vessels,
the heart and glands. Afferent neurons relay visceral input to
different parts of the brain. The sympathetic postganglionic
transmitter is norepinephrine, whereas the parasympathetic
postganglionic transmitter is acetylcholine.
ANS, however, also interacts with different brain struc-
tures. The central and peripheral autonomic information is
processed by the central nervous system, involving forebrain
structures (frontal premotor cortex), hypothalamus, pituitary,
amygdala, and the insular cortex. Included in this central
network are also several brainstem structures, such as nu-
cleus tractus solitarius (NTS), dorsal motor nucleus of the
vagus (DMN), and nucleus ambiguous (NA).
To generate a homeostatic signal level three pathways are
relevant: the hypothalamo-pituitary-adrenal (HPA) axis, the
parasympathetic and the sympathetic nervous system [2].
Hypothalamic signal input results in the release of cortico-
trophin-releasing factor from the paraventricular nucleus, by
which the HPA axis and the sympathetic nervous system as

40 The Open Neurology Journal, 2010, Volume 4 Günther et al.
well as the parasympathetic system are modulated. The HPA
axis activation finally leads to production of glucocorticoids,
resulting in immunomodulation among other hormone re-
lated effects.
Peripherally, afferent vagus nerve fibers forward visceral
(inflammatory) signals to the brain. Inhibition of inflamma-
tion by suppressing pro-inflammatory cytokine release
through efferent vagus nerve signals (mediated by acetylcho-
line, the principle vagal neurotransmitter) has been proposed
as the cholinergic anti-inflammatory pathway [3]. Synergis-
tic with the parasympathetic nervous system, the sympathetic
activation and secretion of epinephrine and norepinephrine
from the adrenal medulla and release from sympathetic nerve
terminals also results in beta adrenergic mediated immu-
nological and autonomic modulation [4, 5].
These mechanisms aim to keep sympatho-vagal homeo-
stasis. The role of the ANS, however, remains to be further
elucidated as a possible key player in the bodies’ response to
pathophysiological conditions in order to consolidate cardio-
and cerebrovascular and immune function as well as to de-
velop implications for early diagnosis and treatment of re-
spective dysfunctions.
3. METHODS OF HRV ANALYSIS
3.a. Introduction of Linear and Nonlinear Methods
The fluctuations of heart rate essentially reflect modula-
tions mediated by the autonomic nervous system. They pro-
vide insights into different parts of autonomic control as well
as risk indices in various pathological entities. In several
studies traditional time and frequency domain measures of
heart rate variability (HRV) have been investigated in con-
junction with cardiovascular outcomes as an important com-
partment of risk stratification. Furthermore, non-linear HRV
measures, such as fractal scaling exponents, complexity in-
dices and indices based on Poincarè plots have been applied
in this context.
All of these measures are based on electrocardiographic
(ECG) recordings. By means of mobile so called “Holter
ECG” systems, the patient’s HRV can be assessed under
“normal” daily activity. In clinical settings the ECG can be
recorded both by means of Holter systems and by stationary
bedside monitoring systems. In order to obtain a reasonable
precision of the heart rate fluctuations the ECG should be
sampled with rate of at least 1000 Hz. The heart beats have
to be exactly identified for example by template matching or
a steepest ascent criterion followed by an annotation of nor-
mal and arrhythmic beats. In order to focus on the autonomic
rhythms and discriminate from those beats reflecting cardio-
genic arrhythmias the convention of analyzing only intervals
between normal heart beats (so called NN intervals) was set
more than a decade ago [6]. Clean NN-interval series can be
constituted after treating arrhythmias by appropriate interpo-
lation or deletion. The relative number of such removed ar-
rhythmic beats is an important measure of the data quality
and can itself provide additional prognostic value beyond the
“Task Force HRV” analysis [6].
Indices of HRV Under Spontaneous Conditions
HRV indices can be calculated from the overall 24 hour
recording or from shorter periods which reflect particular
situations. Also the circadian rhythm of autonomic activity
should be taken into consideration.
Linear HRV assessment of those type of data was first
organized by a Task Force of the European Society of Cardi-
ology and the North American Society of Pacing and Elec-
trophysiology in 1996 [6], and has recently been successfully
validated in several clinical studies as demonstrated in the
studies referred below. These guidelines are still the estab-
lished HRV standard, even though an update has been rec-
ommended [1, 7]. This mainly “linear” HRV analysis is out-
lined in more detail below (see 3b).
Nonlinear HRV assessment was proposed by using dif-
ferent mathematical approaches:
Fractal approaches such as the beta-slope in the log-log
power spectrum and indices of detrended fluctuation analysis
(DFA, [8, 9] assess the self-similar qualities of the HR time
series between different time scales. Their success as predic-
tors of outcome supports the potential relevance of measur-
ing complex cardiovascular coordination over a wide range
of scales and frequencies, respectively. Normal behaviour is
associated with fractal-like correlated properties. Patho-
physiological conditions can change the fractal scaling ex-
ponent or even distort the self-similarity in favour of single
scale rhythms at one extreme or random noise at the other.
Complexity approaches assess the randomness (unpre-
dictability) of the NN interval series. This can be done based
on the original data leading to approximate entropy [10],
sample entropy ([11], conditional entropy [12], or informa-
tion loss [13-15]
The accentuation of key patterns by transformation of the
NN interval sequences into sequences of a few symbols
leads to complexity measures of symbolic dynamics [16,17].
Those approaches are furthermore a fundamental way of
analyzing the information flow that characterizes communi-
cation. Cardiovascular control operates by communication
between different parts of the autonomic nervous system at
different time scales [13,15] Complexity measures as men-
tioned above are calculated using one selected time scale
[10,18] Costa et al. [19] introduced “Multiscale entropy”, a
complexity function over multiple scales, and demonstrated
the relevance of heart rate complexity pattern on different
time scales.
Autonomic Information Flow (AIF) functions reflect
the information flow over different prediction time horizons
and time scales, respectively, of NN interval series. AIF
functions allow the assessment of complexity of both short
and long term heart rate patterns at scales analogous to those
of traditional HRV measures [20]. In that way the complex
rhythms and interactions, essentially mediated by the auto-
nomic nervous system were introduced into cardiovascular
risk assessment. The capability of AIF analysis opens a new
window into the development of physiologically relevant
complexity measures by considering underlying autonomic
mechanisms and the extensive knowledge base already
gained using the standard HRV approaches of task force
monitoring [21].
These methods focus on autonomic rhythms, reflected by
normal-to-normal (NN) beat intervals after removing and
interpolating cardiogenic arrhythmic parts from the meas-