Download the acute-phase heart rate variability in acute coronary syndrome

Taiwan Crit. Care Med.2010;11:239-250
Different analyses of acute-phase HRV in ACS
THE ACUTE-PHASE HEART RATE VARIABILITY IN ACUTE
CORONARY SYNDROME- THE IMPLICATIONS OF DIFFERENT
ANALYSES
Ho-Tsung Hsin1,2, Chi-Yu Yang3, Jiann-Shing Shieh4, Pi-Chi Lin1, Liang-Yu Chen1
Abstract
Purpose: Heart-rate variability (HRV) is non-stationary. HRV is traditionally
analyzed by power spectrum (low / high frequency (LF/HF) ratio). Detrended
fluctuation analysis (DFA) deduces HRV to a simple fractal scaling exponent alpha.
Previously, HRV studies focused on post-MI and chronic heart failure. The decreased
DFA alpha is associated with mortality. The acute-phase HRV has not been elucidated.
We intended to evaluate acute-phase HRV of acute coronary syndromes (ACS),
in comparison with non-ACS subjects.
Methods: Our study had two parts: simulation of acute stress on non-ACS
volunteers and the prospective observation in patients of ACS. We assessed 1hour ECG of volunteers taking three designated intensity of exercise and patients
in the acute stage of ACS. All ECG data were analyzed by DFA and power spectrum.
The results of HRV were evaluated by univariate analysis.
Results: The 30 volunteers got elevated DFA alpha while the intensity of
exercise increased (0.95 0.050 to 1.07 0.084 to 1.20 0.083, p<0.05). The
fractal properties of 33 ACS patients correlated with the complexity of post-MI
course (1.004 0.0080 in non-complicated vs. 1.216 0.058 in complicated p
<0.05). There was no significance when the fractal scaling exponent was categorized
by age, type of ACS and other variables. The LF/HF ratio did not differ in any
clinical perspective but between RCA/LCx and LAD groups (4.23 2.34 / 4.987
vs. 1.87 1.06, p<0.05).
Conclusions: DFA can be used to study the acute-phase HRV in ACS,
while the traditional power spectral analysis failed. The increased value derived
from DFA may imply unresolved cardiac stress which demands further attention.
Key words: Acute coronary syndrome, Detrended fluctuation, Fractal, Heart-rate
variability, Power spectrum
Correspondence: Dr. Liang-Yu Chen
Cardiovascular Intensive Care Unit, Far-Eastern Memorial Hospital, 14F, No. 29, Sec. 2, Nan-Ya South Rd., Banciao
City, Taipei County, Taiwan, 2201
Phone: +886-2-8966-7000 ext.1602; Fax: + 886-2-8966-5138; E-mail: [email protected]
National Tainan Institute of Nursing, Tainan City, Taiwan2
Division of Cardiology, Cardiovascular Center, Far-Eastern Memorial Hospital, Taipei, Taiwan3
Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Taiwan4
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Ho-Tsung Hsin et al.
Introduction
physiologic implications.
Heart rate, a biologic signal, bears the inherited nature of being non-stationary. In addition,
the dynamics of heart rate, referring to heart rate
variability (HRV), vary in different disease status.
Traditionally, HRV can be analyzed by several
means, such as power spectral analysis (lowfrequency/high frequency ratio), or time-domain
analysis (standard deviation of consecutive RR
intervals (SDNN)). However, the traditional methods
are easily hampered by the variation of signal/
noise components and the results may be misleading.
The fractal organization of human HRV is believed
to reflect the balance between sympathetic and
vagal outflow.1 It can also be used as a reliable
indicator of state of the heart.2 Detrended fluctuation analysis (DFA), a mathematic approach
to study non-stationary signals, has made HRV
much easier understood by qualitatively deducing
HRV to a simple fractal scaling exponent ().
The DFA of HRV has been used to evaluate
the risk of mortality in various patient groups.
In selected patients with depressed left ventricular
function or heart failure, the decreased shortterm fractal scaling exponent () has been shown
to be a strong predictor of cardiac and total
mortality.3-6 In addition, the DFA has also been
prospectively applied in a consecutive series of
survivors of acute myocardial infarction (AMI).
The reduced short-term fractal scaling exponent
was significantly correlated with mortality in
follow-up.7
So far, the studies of HRV by traditional
power spectral analysis mostly focused on
convalescent or chronic stable phase of specific
disease entities, such as survivors of AMI, or
patients of chronic compensated heart failure.
Moreover, the available literature discusses the
application of DFA in HRV are limited in the
chronic phase, too.3-7 The physiologic significance
of HRV analyzed by above methods in the acute
phase of acute coronary syndromes (ACS) is not
clear. Therefore, we conduct this study to evaluate
the acute-phase HRV in ACS by both power
spectral analysis and DFA, and try to clarify their
Materials and Methods
Acquisition of electrocardiogram
The device used to acquire electrocardiogram (ECG) was tailor-made. The acquisition
system in this article referred to an industrial
personal computer (IPC). The IPC could be
connected to any ECG monitor with analogue
electric output, such as the polygraphic monitor
in the intensive care unit (Fig. 1) or the directcurrent defibrillator. The capturing of ECG worked
with a frequency of 400Hz. The captured signals
were digitalized and further processed by the
software MatLab 6.0, which could translate
digitalized ECG into beat-to-beat R-R interval.
In addition, a portable single-chip device
was designed for ECG acquisition for ambulatory
volunteers, in order to capture exercise ECG.
The single-chip device took ECG by conventional
leads, as that in performing routine treadmill
exercise ECG or Holter ECG scanning.
Simulation of acute stress
The heart variability shall change with
different stress, so shall the consequent shortterm fractal scaling exponent (), which was
our primary hypothesis. We arranged programmed
exercise for 30 subjects without current of past
history of ACS. These volunteers were in their
sixties and with or without hypertension, diabetes
or hyperlipidemia. The percentage of the comorbidities were tried to rival the studied ACS
patients. The 30 volunteers bore the portable
single-chip ECG device while they were taking
activities of specific-intensities after presenting
their informed consent. The stress protocol included
light, moderate and vigorous activities. All of
the subjects took the three designated intensity
of activities for 60 minutes, respectively. Sedentary works such as casual reading, internet
browsing were regarded as light activities, which
rendered less than 3 metabolic equivalents (METs).
The activity of moderate stress in this study
referred to programmed treadmill exercise, 4 miles
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Different analyses of acute-phase HRV in ACS
Fig. 1. The instrument used in the intensive care unit for ECG acquisition. The industrial personal
computer is connected to the polygraphic monitor in the unit and the process is controlled
by a laptop.
and 1 or 2 of the following parameters: (1) chest
pain or dyspnea lasting for more than 30 minutes,
and (2) ischemic ECG changes on admission or
any later change of ECG caused by AMI. The
exclusion criteria were unstable hemodynamics
requiring very high dose of intravenous inotropes
(defined as an intravenous infusion of dopamine
more than 10 microgram/kg/min with or without
using additional vasopressive agents, such as
norepinephrine, epinephrine and vasopressin),
advanced age (>80 year-old), dementia, old or
ongoing intracranial process (i.e. old or active
cerebrovascular accidents, brain tumor, metabolic
encephalopathy, parkinsonism), alcoholism, drug
abuse, or any other condition that could impair
the subject’s or the family’s capability to give
informed consent. In addition, patients of nonamenable cardiac dysrhythmia were also excluded,
such as chronic atrial fibrillation, refractory
frequent premature atrial or ventricular complex,
and malignant arrhythmia requiring immediate
resuscitation. Other non-cardiovascular conditions that made the patient excluded were immediate
per hour in speed and a 5% incline, which offered
3 to 7 METs. The vigorous one was defined as
treadmill exercise at 4 miles per hour in speed
and a 10% incline, and the intensity of stress
was believed to be 7-10 METs. 8 During each
course of designated activity, the single-chip ECG
acquisition device recoded the ECG signal.
Study of ACS patients
This is a prospective observational study
conducted in the coronary care unit of a medical
center. A consecutive series of patients of acute
coronary syndrome admitted to the unit were
screened after obtaining their informed consent.
During the period of 2 months, we enrolled 33
eligible patients and their ECG data. The acute
coronary syndromes referred to unstable angina
(UA), non-ST elevation myocardial infarction
(NSTEMI) and ST-elevation myocardial infarction (STEMI). The diagnosis of the acute coronary
syndrome was based on an elevation of myocardial
enzymes up to more than 2 times the upper limit
that could not be attributable to any other condition,
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Ho-Tsung Hsin et al.
series. The root-mean-square fluctuations of
integrated and detrended time series is measured
at different observational windows and plotted
against the size of the observational window on
a log-log scale. The slope of the root-mean-square
line relating log F(n) to log n determines the
scaling exponent (self-similarity parameter)-.
Two equations could summarize the above words:
surgical indication, pregnancy and terminal stage
of malignancy. In addition, the study protocol
conformed to the ethical guidelines of the 1975
Declaration of Helsinki as reflected in a priori
approval by the institution’s human research
committee.
Every eligible patient was put on routine
polygraphic monitoring after being admitted to
the unit. The IPC was connected to the bed-side
polygraphic monitor and a one-hour ECG acquisition was carried out within 48 hours of
admission. The short-time acquisition of ECG,
referring to “one-hour” in our study, is justified
by the mathematic basis described below and
another HRV study adopting “Chaos theory”9.
As we adopted early-invasive strategy to treat
acute coronary syndromes in this institute, all
of the enrolled ACS patients had undergone
percutaneous coronary intervention before the
ECG acquisition. The therapy for acute coronary
syndromes complied with the latest ACC/AHA/
ESC treatment guidelines for either unstable
angina/non-ST elevation or ST-elevation myocardial infarction. In other words, dedicated drug
therapy such as aspirin, clopidogrel, anti-coagulants,
beta-blocker, angiotensin-converting enzyme
inhibitor (ACEI)/ angiotensin receptor blocker
(ARB), and statins were administered accordingly,
unless contraindicated.
1) the least square line
2) the detrended fluctuation analysis
The advantages of DFA over conventional
methods are that it permits the detection of longrange correlations embedded in a seemingly nonstationary time series and also avoids the spurious
detection of apparent long-range correlations that
are an artifact of non-stationarity. The DFA
method has been tested on control time series
that consist of long-range correlations with
superposition of a non-stationary external trend.12
The advantage of this mathematic model renders
the convenience that it needs only 1000 RR interval
(in other words less than 18 minutes for a subject
with HR of 60/min) to suffice the analysis of
the instant HRV.11 The transformation of ECG
signal to RR-intervals and final DFA () is
exemplified in Fig. 2, one of our ACS patient’s
data. The measured frequency was 400 Hz.
The detrended fluctuation analysis (DFA)the fractal scaling exponent ()
The ECG data of sinus beats, which meant
“edited data” were used for the DFA.10,11 The
computational details of DFA are well demonstrated by Dr. Peng and his colleagues. 12,13 In
short, the DFA is a novel mathematic analysis
developed to elucidate long-range continuous
non-stationary signals, especially biological ones.
It has been successfully applied in analyzing
electroencephalogram (EEG), waves of respiratory motion, ECG and even the complex sequences
of humane DNA.13-15 The conceptual simplification of DFA can refer to that moving window
of size n is used to study how the fluctuation
F(n) grows with n for the inter-beat interval time
The power spectral analysis ECG by frequency domain
The power spectrum is a quantity widely
used to measure correlations in a time series,
which measures the relative frequency content
of a signal. The HRV data was measured by
fast-Fourier transform analysis in 2 frequency
bands: 0.04 to 0.15 Hz (low frequency, LF), and
0.15 to 0.40 Hz (high frequency, HF). LF and
HF components were computed over the entire
1-hour recording interval. The LF/HF ratio was
calculated accordingly.
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Different analyses of acute-phase HRV in ACS
Fig. 2. The plots of transforming ECG data to DFA , exemplified by one of the ACS patients.
The slope is the DFA .
Statistical analysis
The analysis was done with Statistical Package for Social Sciences software (SPSS 12.0 for
Windows). The DFA () has been proved to be
normally distributed by Dr. Tapanainen’s work.7
The baseline characteristics were compared by
univariate analysis. The chi-square test was used
for categorical variables, and the comparison of
continuous variables was done by 2-tail t test.
A p value <0.05 was considered statistically
significant.
rival in age, gender and co-morbidities. The
percentages of taking specific prescription were
also similar, except for anti-platelet agents and
statins. Other demographics and clinical profiles
were listed in Table 1.
Acute stress simulation
The baseline resting HRV of the 30 volunteers resulted in a fractal exponent () =
0.95 0.050. The value during exercise of
moderate-intensity was 1.07 0.084, and it
increased to 1.20 0.083 as the volunteers took
vigorous exercises. (p< 0.05). On the other hand,
the LF/HF ratio did not show any trend at all
(2.83 1.74 to 5.49 2.30 to 4.39 1.29). (Fig.
3)
Results
Basic demographics
The 30 non-ACS volunteers were 66.5 5.2 year-old on average. The ACS patients were
62.2 12.8 year-old. The group of simulation
and the studied ACS patients were tried to be
Study on ACS patients
During a period of 2 months, we enrolled
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Ho-Tsung Hsin et al.
Table 1. Clinical variables of the ACS patients and non-ACS subjects
Age (year-old)
Gender: male/female
Co-morbidity
Hypertension
Diabetes mellitus
Hyperlipidemia
Current smoker
Current drug
Aspirin
Clopidogrel
Statin
Beta-blocker
ACS patients
n=33 (%)
Non-ACS subjects
n=30 (%)
P value
66.5 5.2
26/7
62.2 12.8
24/6
NS*
NS
(75.6%)
(33.3%)
(51.5%)
(54.5%)
24 (80%)
9 (30%)
15 (50%)
15 (50%)
NS
NS
NS
NS
33 (100%)
33 (100%)
24 (72%)
25 (75.6%)
9 (30%)
0 (0%)
15 (50%)
21 (70%)
P<0.05
P<0.05
P<0.05
NS
25
11
17
18
*NS: not significant
Fig. 3. The power spectral LF/HF ratio and fractal scaling exponent of the 30 healthy volunteers
during different intensities of exercise. (The 2nd data of LF/HF was truncated as the standard
deviation surpassed the range.)
DFA: deterended fluctuation analysis
LF: low frequency
HF: high frequency
MET: metabolic equivalent
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Different analyses of acute-phase HRV in ACS
was 83 10.3/min. The mean arterial pressure
was 73 9.8 mmHg. There was no in-hospital
mortality in this cohort. In Table 2, we illustrated
the summary of HRV both by DFA and power
33 eligible patients and their ECG data of acute
coronary syndromes. The acquisition of ECG was
done at around 26.8 2.1 hours after admission
to the unit. The heart rate during ECG acquisition
Table 2. The results of HRV analyzed by DFA and power spectrum in ACS patients
by different categories
DFA ()
LF/HF ratio
Age (No. of patients)
65 y/o (24) > 65 y/o (9)
P Value
1.031 0.113
2.82 1.93
0.660
0.662
1.009 0.144
2.46 1.96
Type of Acute Coronary Syndromes (No. of patients)
DFA ()
LF/HF ratio
STEMI (18)
1.008 0.122
3.03 1.83
NSTEMI (8)
1.045 0.163
2.03 2.01
UA (7)
1.070 0.046
0.91 0.58
0.722
0.230
Number of diseased coronary arteries (No. of patients)
DFA ()
LF/HF ratio
1 VD (10)
1.023 0.053
2.28 1.03
2 VD (9)
1.070 0.160
3.05 2.90
3 VD (9)
1.003 0.135
2.51 2.18
LM (5)
1.006 0.177
1.67 1.01
0.838
0.774
Killip IV (1)
0.937
2.24
0.479
0.908
Killip classification of AMI (No. of patients)
DFA ()
LF/HF ratio
Killip I (14)
0.999 0.317
3.20 2.00
Killip II (7)
1.005 0.367
3.36 1.43
Killip III (4)
1.081 0.120
3.90 2.96
Left ventricular ejection fraction (No. of patients)
DFA ()
LF/HF ratio
< 50% (8)
0.973 0.099
2.67 2.01
50% (25)
1.061 0.112
2.63 1.81
0.070
0.962
Infarct-related artery (No. of patients)
DFA ()
LF/HF ratio
LAD (18)
1.005 0.106
1.87 1.06
RCA (14)
1.058 0.176
4.23 2.34
LCx (1)
0.998
4.987
0.440
0.029*
Complicated (6)
1.216 0.058
3.00 1.58
<0.001
0.662
Post-MI course (No. of patients)
DFA ()
LF/HF ratio
Non-complicated (27)
1.004 0.080
2.76 1.85
*The significance exists between LAD and no-LAD categories.
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Ho-Tsung Hsin et al.
subject is believed to be 1.0 0.1.17,18 In our
study, the value of the volunteers before exercise
was 0.95 0.050, which was compatible with
the published data. In other words, the equality
of our value to that in the literature preliminarily validated the authenticity of our study
protocol. By applying stepwise programmed
exercise, we found that the more intensive the
physical stress, the larger the value (0.95 0.050 increased to 1.07 0.084 and then up to
1.20 0.084). This phenomenon explicitly indicates that the short-term fractal scaling exponent
() increased with the increment of the acute
stress. On the other hand, the LF/HF ratio did
not show any consistent trend of variation in
stress simulation (2.83 1.74 to 5.49 2.30 to
4.39 1.29), which failed to demonstrate its
usefulness in evaluating acute stress.
spectral analysis in different categories of acute
coronary syndromes. As far as power spectrum
is concerned, the only significance existed in the
category of infarct-related artery (IRA). The LF/
HF ratio is significantly higher in RCA (right
coronary artery)/ LCx (left circumflex artery)
group than LAD (left anterior descending artery)
group (4.23 2.34/ 4.987 vs. 1.87 1.06, p=
0.029). Regarding the DFA of HRV, the univariate
analyses based on age, type of ACS, number
of diseased coronary vessels, IRA, and Killip
classification, did not show statistical significance
between groups. On the other hand, if we took
the perspective of post-MI complications, referring to post-MI angina and recurrent cardiogenic
lung edema (defined by aggravated lung congestion on chest roentgenogram, or requiring
increased or re-instituting intravenous diuretics),
there were 6 cases bearing the fractal scaling
exponent = 1.216 0.058. Those who experienced smooth post-MI course presented the
result of their HRV by DFA () = 1.004 0.080 (p<0.001).
Implications of HRV by different analyses
Regarding the application of HRV as prognostic factors in patients of acute coronary syndromes,
all of the analyses, such as SDNN, power spectrum,
predicted mortality when the HRV was measured
in the convalescent phase after an AMI. 19-25
However, there are some arguments that these
traditional HRV parameters are able to predict
mortality in the univariate analysis, but the power
is weakened after adjustment for the clinical
variables and left ventricular function.7 The original
studies showing the association between the
reduced heart rate variability and mortality rates
were from the pre-thrombolytic era.19,20 With the
advent of modern pharmacologic therapy and
coronary revascularization, the mortality is much
lowered. In addition, the power spectral analysis
using LF/HF ratio has inherited flaws: the calculation
is based on the assumption that the studied signal
is stationary. If deliberately applied, the power
spectrum would lead to misleading results in
analyzing non-stationary signals. In controlled
external contexts with fixed respiratory rate, the
short-term fractal scaling exponent () of DFA
is closely related to LF/HF ratio.26 However, the
correlation becomes weaker in “free-running”
ambulatory conditions. This change is ascribed
Discussion
First of all our study focused on clarifying
the physiologic implications of the short-term
fractal scaling exponent () of acute phase HRV.
Our intention was to imply unresolved cardiac
stress in ACS patients in acute stage by analyzing
the 1-hour ECG at a specific time point. The
study protocol and preliminary result were ever
published elsewhere.16 Secondly, we compared
different analyses, DFA and power spectral analysis
in studying acute-phase HRV, and found that
power spectral analysis could not reach a consistent trend in detecting probable stress while
DFA could.
Acute stress simulation
The simulation of acute stress on the heart
is maneuvered by imposing different intensity
of physical exertion on age- and co-morbiditymatched volunteers without ACS. The fractal
scaling exponent () of a healthy and tranquil
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Different analyses of acute-phase HRV in ACS
to that the DFA provides precise information on
the scaling properties of heart rate fluctuations
over highly segmented time windows, whereas
power spectral analysis only vaguely represent
HRV in summated time windows.27 The “acute
stress” may offer too “narrow” a time window
(only 1 hour in our study) and too “non-stationary”
a signal (the ACS is still evolving) for the power
spectrum to take effective. In our study, the only
difference of LF/HF ratio resided in different
IRA (RCA/LCx vs. LAD: 4.23 2.34/ 4.987 vs.
1.87 1.06, p= 0.029). Inferior wall MI related
to RCA/LCx occlusion is well renowned to have
Bezold-Jarisch reflex, which comes from enhanced vagal modulation.28 Physiologically, HF
components of HRV represent the activity of vagal
outflow, but it is dramatically influenced by
respiration. On the other hand, the LF components
indicate the mixed modulation of both sympathetic
and parasympathetic nerves.29 As a consequence,
the enhanced LF/HF ratio in the IRA group of
RCA/LCx could not conform to the vagal-dominant nature of inferior wall MI, implying that
a LF/HF ratio is of poor clinical significance.
with angina pectoris without prior MI were
significantly higher, in comparison with agematched healthy controls (1.34 0.15 vs. 1.11
..
0.12, p< 0.001). Dr. Ma kikallio concluded
that the short-term fractal scaling exponent ()
performed better than other HRV parameters on
differentiating patients with active coronary artery
diseases (CAD) from healthy subjects, but it was
not related to the clinical or angiographic severity
of CAD.30 The preliminary conclusion reached
by our simulation of acute stress on heart is
..
compatible with that of Dr. Ma kikalli’s study.
In our patients of acute coronary syndromes,
increased value (1.216 0.058) was observed
in 6 complicated cases of recurrent cardiogenic
lung edema during hospitalization. All the other
patients experienced non-complicated postrevascularization course and had the value
close to subjects free from ACS (1.004 0.080).
The difference of the short-term fractal scaling
exponent () between the complicated and noncomplicated patients was statistically significant.
The fractal properties of HRV in our patients
were not influenced by the type of acute coronary
syndromes, the number of diseased coronary
vessels, the infract-related artery, and mostinterestingly, the predetermined Killip class of
acute myocardial infarction. We may preclude
the only one case of Killip IV, and the shortterm scaling exponent did not differ between
patients of Killip I, II and III (0.999 0.119
vs. 0.984 0.317 vs. 1.080 0.243, p>0.05).
This observation may conform to the experience
of our daily practice, in which a majority of the
AMI patients would experience an event-free stay
after successful revascularization of the IRA,
regardless of the Killip classification. Based on
the hypothesis built by the acute stress simulation,
the DFA probably discloses sort of ongoing
cardiac stress, despite the patient is kept rest and
the heart has been successfully revascularized.
The on-going cardiac stress resulted in the increment
of value and the clinical manifestation of acute
cardiogenic lung edema.
The implication of increased DFA ()
Previously, the reduced scaling exponent
() is found to provide prognostic information
among patients with depressed left ventricular
systolic function.3-6 The role of the scaling exponent
has been broadened to play as a risk stratifier
of mortality beyond the patients with impaired
left ventricular function to more general postMI populations7. Those studies demonstrated that
a reduced short-term scaling exponent (<1.0)
predicted pots-MI mortality in follow-up. Differently,
our study concerned the unresolved acute-phase
stress of ACS on heart, not the mortality in chronic
follow-up. According to the result of acute stress
simulation in our study, we preliminarily elucidate
the physiologic background of the short-term
scaling exponent: the acute stress would make
the scaling exponent () higher. Dr. Tulppo in
his delicate study also showed the higher the
sympathetic tone, the higher the alpha value.29
The scaling exponents in symptomatic patients
Limitation of this study
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Taiwan Crit. Care Med.2010;11:239-250
Ho-Tsung Hsin et al.
questions on your competing interest form are
all No and there have nothing to declare.
This is a single-institute observational study,
which has limited significance as inherited. In
addition, the scale is too small, which made the
receiver’s operating curve (ROC) out-of-thequestion. We also acknowledged that this study
was designed to elucidate the relationship between
“unresolved stress” and HRV. Therefore, higher
event rate than that in general population may
be inevitable. However, it is not our initiative
to evaluate the DFA among general populations,
but test it in patients with probable stress. In
addition, the number of Killip IV myocardial
infarction is too small to be taken in to account.
We should take caution in the preliminary conclusion that the short-term fractal scaling exponent
did not correlate with the pre-determined Killip
classification. Due to the limited number of
subjects, we were unable to perform multivariate
analysis. It is also impossible to clarify the
influence of beta-blocker, inotropes and DM on
HRV. Further expansion of the case number is
desired in the future.
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Conclusion
In Short, abnormal HRV in chronic stage
is proved to be associated with increased mortality.
However, we did not know how acute stress
influences the HRV until the ushering of DFA.
Our study implied the probable failure of power
spectral analysis (LF/HF ratio) in dealing with
acute-phase HRV. On the other hand, our study
indicated that elevated acute stress resulted in
an increased short-term fractal exponent ().
Clinically speaking, the increased DFA () value
more than 1.0 probably implies that there should
be sort of unresolved cardiac stress, which demands
further attention.
Acknowledgement
This study was supported by grants of FarEastern Memorial Hospital (FEMH-95-C-029).
Statement of conflict of interest
All authors declare that the answer to the
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Ho-Tsung Hsin et al.
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