Engineering, 2013, 5, 237-243 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
Heart Rate Variability Applied to Short-Term
Cardiovascular Event Risk Assessment
Simao Paredes1, Teresa Rocha1, Paulo de Carvalho2, Jorge Henriques2,
Ramona Cabiddu3, João Morais4
1Computer Science and Systems Engineering Department, Polytechnic Institute of Coimbra (IPC/ISEC), Coimbra, Portugal
2Centre for Informatics and Systems of the University of Coimbra,
Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
3Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
4CardiologyDepartment, Leiria-Pombal Hospital Centre, Leiria, Portugal
Received June 2013
Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily
practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due
to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease
event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations. This
work addresses this problem with two different methodologies: 1) combination of risk assessment tools based on fusion
of Bayesian classifiers complemented with genetic algorithm optimization; 2) personalization of risk assessment
through the creation of groups of patients that maximize the performance of each risk assessment tool. This last ap-
proach is implemented based on subtractive clustering applied to a reduced-dimension space. Both methodologies w ere
developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-
tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies: 1) San-
ta Cruz Hosp ital, Portugal, N = 460 patients; 2) Leiria-Pombal Hospita l Centre, Portugal, N = 99 patients. This work im-
proved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity
and geometric mean of, respectively, 80.0%, 82 .9%, 81.5%. Besides this enhancement, the proposed methodologies
allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be
applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification)
should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters de-
rived from heart rate variability (HRV ), is introduced in this wor k. HRV is a strong and independent predictor of mo r-
tality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coro-
nary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hos-
pital Centre (LPHC).
Keywords: CVD Risk Assessment; Knowledge Management; Management of Cardiovascular Diseases;
Decision-Support Systems
1. Introduction
Coronary heart disease (CHD)1, approximately half of all
cardiovascular disease (CVD) deaths, is the single most
common cause of death in Europe [1].
European Heart Network supports that around 80% of
CHD are preventable [2], which shows that the improve-
ment of preventive health care can originate important
benefits reducing t he i nci dence of c ardi ovas cular disea ses .
Therefore, preventive health care assumes a critical
importance in the present health care context. It is the
key aspect in reducing the social and economic costs
directly originated by cardiovascular diseases. In fact, it
is commonly accepted that current health care paradigm
has to move from reactive care towards preventive care,
reducing the amount of in hospital care. Health telemo-
nitoring systems are essential to achieve this target, as
they allow the remote monitoring of patients who are in
1Coronary heart disease (heart attacks), cereb rovascular
disease (stroke)
raised blood pressure (hypertension), peripheral artery disease, rheu-
matic heart disease, congenital heart disease and heart failure are dis-
orders of the heart and blood vessels globally designated by cardiovas-
cular diseases (CVD).
Copyright © 2013 SciRes. ENG
different locations away from the health care provider [3].
This remote monitoring is more challenging to the care
provider, as the reliability/quality of the clinical decision
must be guaranteed in order to optimize therapy.
The CVD risk assessment, i.e., the evaluation of the
probability of occurrence of an event (death, myocardial
infarction, hospitalization, disease development, etc.)
give the patient’s past and current exposure to risk fac-
tors, assumes great importance in this remote health
monitoring. It contributes in providing the patient’s
health development as well as generating alarms [3]. In
this way, a correct CVD risk assessment helps clinical
professionals to identify the best treatment to each pa-
tient as well as to motivate the patient increasing the
treatment compliance with the corresponding health ben-
efits (patient seen as a co-producer of health) [4].
Additionally, clinical guidelines re commend the use of
risk scores in the daily clinical practice to predict the risk
of a cardiovascular disease event [5]. This assessment
contributes in helping medical professionals in managing
the patient population. Actually, physicians gather more
information to identify the patients that need urgent hos-
pitalization, those that need urgent review of respective
care plans (lack of treatment, over treatment situations…)
and those that correspond wit h the expect ed condit ion.
As a result, it is clinically recogn ized that the research
and development of practical and accurate CV risk as-
sessment tools2 are of vital importance [6]. In this con-
text, several risk assessment tools were developed to as-
sess the probability of o ccurrence of a CVD event with in
a certain period of time. Two types of risk may be calcu-
lated: absolute risk , i.e . , probability o f dev eloping a CVD
event over a given period of time (e.g. 10 years), and a
relative risk, i.e., risk of someone developing a CVD
event that has risk factors compared to an individual of
the same age and sex who does not. Moreover, available
risk assessment tools differ on the assessed period of
time (short-term [months]/long-term [years]), predicted
events (death/non-fatal), disease (coronary artery disease,
heart failure, etc.), risk factors, patient condition (ambu-
latory patients, hospitalized patients, cardiac transplant
candidates, etc.) [7].
In spite of their relevance, these risk assessment tools
exhibit important drawbacks: 1) may present some lack
of performance; 2) ignore the information provided by
other risk assessment tools that were previously devel-
oped; 3) consider (each individual tool) a limited number
of risk factors; 4) have difficulty in coping with missing
risk factors; 5) do not allow the incorporation of addi-
tional clinical knowledge; 6) do not assure the clinical
inter- pretability of the respective parameters; 7) impose
a selection of a standard tool to be applied in the clinical
This work addresses the identified weaknesses with
two different methodologies: 1) combination of risk as-
sessment tools (fusion of naïve Bayes classifiers com-
plemented with genetic algorithm optimization); 2) per-
sonalization of risk assessment (creation of groups of
patients based on subtractive clustering applied to a re-
duced-dimension space).
Two main hypothesis support the first approach: 1) it
is possible to create a common representation of individ-
ual risk assessment tools; and 2) it is possible to combine
individual models. The main goal is to integrate several
sources of information (risk assessment tools) to defeat
the identified limitations. Though, current risk assess-
ment tools are diversely represented [8-10], which does
not facilitate their integration/combination. Therefore a
common representation must be created verifying some
requirements: 1) simplicity; 2) ability to incorporate new
risk factors (empirical clinical knowledge); 3) clinical
interpretability; and 4) ability to deal with missing risk
factors. The creation of a flexible framework based on
the combination of available knowledge, is the basis of
the second hypothesis. According to various authors an
ensemble of classifiers is often more accurate than any of
the respective single classifiers. Thus, there are several
methods to implement model’s combination which can
be organized in two main categories: 1) model output
combination; and 2) model parameter/data fusion. The
former includes the voting (e.g., voting, weighted voting,
dynamic voting, bagging algorithms, boosting algorithms,
etc.) and selection methods (e.g. information criteria,
cross-validation varian ts, dynamic selection, etc.) [11,12].
Model parameter/data fusion implements a direct com-
bination of the parameters of individual models [13,14].
The approach proposed in this work is included in this
last category and explores the particular features of
Bayesian inference mechanism. This framework also
permits the implementation of optimization methodolo-
gies to increase the CVD risk prediction performance.
The second methodology, personalization of risk as-
sessment, addresses the problem of the low performance
exhibited by the current risk assessment tools when ap-
plied to the general population. The methodology is
based on the evidence that risk assessment tools perform
differently among different populations. Thus, the main
hypothesis that supports this methodology can be stated
as: if the patients are properly grouped (clustered) it
would be possible to find the best classifier for each
The two methodologies were applied to three (GRACE
[10], TIMI [8], PURSUIT [9]) well-accepted risk as-
sessment tools [15]. The validation phase was supported
by two real ACS-NSTEMI patient testing datasets: i)
Santa Cruz Hospital, Lisbon/Portugal, N = 460 patients;
2In orde
r to clarify, risk assessment models that have been statistically
validated and are available in literature are going to be designated
through this work as risk assessment tools.
Copyright © 2013 SciRes. ENG
ii) LeiriaPombal Hospital Centre, Portugal, N = 99.
In spite of these achievements, the CVD risk assess-
ment (patient stratification) should be improved. The
possible incorporation of new risk factors recognized as
clinically significant, namely parameters derived from
heart rate variability (HRV), is introduced in this work.
In fact, HRV is a strong and independent predictor of
mortality in patients following acute myocardial infarc-
The paper is organized as follows: section II presents
the developed methodologies. In section III some results
of the validation procedure are discussed. Section IV
depicts the incorporation of the heart rate variability pa-
rameters in the CVD risk assessment. The main conclu-
sions are derived.
2. Methodology
Figure 1 presents the developed strategies. These me-
thodologies were further detailed in previous publica-
tions of this research team [16,17].
2.1. Combination of Individual Tools
The implementation of this approach is composed of two
main phases: 1) common representation of individual risk
assessment tools based on naïve Bayes classifier; 2) a
combination scheme that exploits the probabilistic nature
of naïve-Bayes inference mechanism complemented with
an optimizati on ba s ed on gene tic algori t hms (GA).
2.1.1. Common Representation of Individual Tools
Current individual risk scores (risk assessment tools) are
diversely represented (equations/scores/charts) which
hinder their combination. To allow the fusion (combina-
tion) of these risk scores a common representation is
created. The classifier selected to implement this com-
mon representation is the naïve Bayes classifier as it
presents some important features: 1) simplicity; 2) ability
to deal with missing risk factors; and 3) interpretability
[18]. Its inference mechanism assumes that observations
(attributes) are conditionally independent, given the val-
ue of hypothesis C:
(|)(|,...,) () (|)
The term
( |)
PC x is the probability that the hypo-
thesis is correct (e.g., the risk is high) given a set of
[ ,...,]
(e.g., demographic data, clin-
ical examination, laboratory measurements, etc.).
gives the prevalence of each risk level (a priori probabil-
ity) and
(|)PX C
expresses the probability of the ob-
servation X given the value of class of risk
is a normalization constant.
The process of representing a specific individual risk
Figure 1. Proposed methodologies.
assessment tool as a naïve Bayes classifier can be syste-
matized as follows: 1) a training datasetis generated,
[ ,...,]
composed of
attributes; 2)
each instanceis applied to the risk assessment tool in or-
der to obtain a complete labeled dataset
{(, ),....,(,)}
Jc c=xx
; and 3) based on
through the maximum likelihood estimation method, the
naïve Bayes classifier that resembles the behavior of that
specific risk assessment tool is derived [16]. The proba-
results directly from distribution of the class
values (low risk/high risk patients) .
2.1.2. Individual Models Parameters’ Weighted
The Equation (2) implements the proposed combination
scheme, where it is possible to assign different weights
for the individual Bayesian models.
ij j
PC PCwherew
PX CPXCwherew
= =
= =
= ×Γ=
= ×=
is the number of individual models,
the number of individual models that contain the attribute
denotes each individual model,
is the
weight of model
An optimization based on GA can be performed. The
GAfocuses on the
(|),( )
that are the parame-
ters of the global model originated through the combina-
tion method. The optimization is performed in the neigh-
bourhood of the initia l values and through a multi-objec-
tive approach where sensitivity and specificity should be
maximized. A detailed approach to this optimization
procedu re can be fou nd o n [16].
2.2. Personalization Based on Grouping of
This second methodology was developed to enhance the
performance of the risk prediction when compared to the
one obtained with current risk assessment tools. It is
based on the hypothesis that it is possible to select the
most appropriate current risk assessment tool for a spe-
cific group of patients.
This methodology is composed of two main phases: 1)
grouping of patients; and 2) identification of risk tools.
Grouping of patients is supported on a dimension re-
Copyright © 2013 SciRes. ENG
duction step as it facilitates clustering ; it avoids the hete-
rogeneity (continuous, Boolean, etc.) of risk factors and
it assures the uniformization of each patient’s data (same
scale). A non-linear mapping is implemented directly
supported on the outputs of the selected set of risk as-
sessment tools [17]. Thus, all instances
[ ...]
i iT
i PN
xx ×
= ∈xX
, that correspond to the
are mapped into
N, 1,...,
notes the output of tool
to classify the patient
. Then,
clustering is applied through subtractive clustering [19].
Patients are grouped, based on the outputs of the risk
, in order to create K disjoint groups (clusters)
of patients with similar characteristics.
The second phase is the identification of risk assess-
ment tools, where the performance of the several indi-
vidual tools is assessed within each cluster. This allows
that each cluster be assigned to the tool that presents the
best performance. The final classification of a particular
patient that belongs to a given cluster corresponds to the
classification obtained with the individual tool that has
the best performance with patients from that cluster [17].
3. Results
The two developed methodologies were applied to coro-
nary artery disease patients (secondary prevention/short
term) (Table 1).
The three risk assessment tools (TIMI [8], PURSUIT
[9], GRACE [10]) were selected as they are the most
well accepted/known CVD risk assessment tools specific
for CAD patients [15 ].
Two testing ACS-NSTEMI real patient datasets were
applied in the validation procedure: 1) Santa Cruz hospital
with N = 46 0 patients. The event rate of combined end-
point (death/myocardial infarction) is 7.2%. 2) Leiria-
Pombal Hospital Centre with N = 99 patients with an
endpoint rate of 5.1%.
The training dataset was created
[ ...]
i ii
for all
; 1i iN≤≤
, with
, based on the approach
proposed in [14].
Table 1. Short-term risk assessment models.
Model Event Time Prev. Risk Factors
GRACE [10] D
MI 6 m Sec. Age, SBP, CAA HR, Cr,
MI 30 d Sec. Age, Sex, SBP, CCS, HR,
TIMI [8] D
14 d Sec. Age, STD, ECM, KCAD,
D: Death; MI: Myocardial Infarction; UR: Urgent revasc.; m: months; d:
days; S: Secondary Prevention; CrCreatinine, HRHeart Rate, CAA
Cardiac Arrest at Admission, CHFCongestive Heart Failure, STD—ST
Segment. Depression, EC EElevated Cardiac Markers/Enzymes, KCAD
Known CAD, ERLEnrolment (MI/UA), HFHeart Failure, CCS
Angina clas sificat ion, ASUs e of aspir in in the p revious 7 days, AG—2 or
more angina events in pa s t 24 hrs, RF—3 or more cardiac risk factors.
3.1. Combination of Individual Tools
Table 2 contains the comparison of the Bayesian global
model with the individual risk assess ment tools as well as
with the voting model (based on the outputs of the three
individual risk assessment tools).
Table 3 presents the results obtained after the optimi-
zation pr oc edure bas e d o n G A operatio n.
Considering the obtained results in table, the optimi-
zation improved the capability of the global model to
predict the risk. However, there were some test cases
where the combination methodology did not achieve an
improvement of the performance, namely of the specific-
ity value.
3.2. Personalization Based on Grouping of
This methodology was applied to the Santa Cruz hospital
dataset (combined endpoint, D/MI), based on the same
risk tools (TIMI [8], PURSUIT [9], GRACE [10]). The
first step was the dimensionality reduction from the
risk factors to Q = 3 outputs of the risk
tools. The clusters were created and the performance of
each tool in each cluster was assessed.
This strategy achieved a higher sensitivity than all the
individual tools (the best individual sensitivity is 60.8%
while the sensitivity for the proposed strategy is 72.9%)
(Table 4). It did not reduce the specificity, which shows
the potential of this approach to improve the risk predic-
More detailed results obtained with the validation of
these two methodologies, can be found on [16,17].
4. Final Considerations
4.1. Ongoing Research
In spite of the performance enhancements, there are some
research directions that must be pursued to improve the
CVD risk assessment. The fusion of the two developed
methodologies must be further explored (personalization).
Furthermore, the flexibility of the combination metho-
dology (Bayesian global model) allow s the incorporation
of parameters recognized as clinically significant to im-
prove risk assessment, namely the heart rate variability
Heart Rate Variability
Heart rate variability is an ECG derived signal consistin g
in the oscillation in the interval between consecutive
heart beats [20].
Cardiac rhythmical activity is controlled by the auto-
nomic nervous system (ANS) where the sympathetic
system (arousal/activation) and parasympathetic system
(inhibition) are the key elements. A significant correla-
Copyright © 2013 SciRes. ENG
Table 2. Performances comparison—Santa cruz, (D/MI).
SE 60.6 42.4 33.3 60.6 48.5
SP 74.9 74.2 73.5 67.0 75.6
Gmean 67.3 56.0 49.4 63.4 60.6
samp les
n = 1000
SE 60.8 (60.2; 61.3) 42.4 (41.9; 43.1) 33.5 (33.0; 34.0) 60.6 (60.1; 61.3) 48.6 (48.0; 49.2)
SP 74.9 (74.8; 75.1) 74.2 (74.1; 74.3) 73.6 (73.5; 73.7) 67.0 (66.9; 67.2) 75.6 (75.5; 75.8)
Gmean 67.3 (67.0; 67.6) 55.8 (55.5; 56.2) 49.3 (48.9; 49.7) 63.6 (63.3; 63.9) 60.3 (60.0; 60.7)
SE: Sensitivity; SP: Specificity; D: Death; MI: Myocardial Infarction; (-;-) = 95% CI; ByGBaye sia n Global Model, VotVoting.
Table 3. Performances comparison.
Santa Cruz 30 days/D/MI Santa Cruz 30 days/D Santo André 30 days/D
SE 60.6 72.7 61.5 76.9 80.0 80.0
SP 67.0 69.1 65.7 70.7 67.0 82.9
Gmean 63.4 70.9 63.5 73.7 73.2 81.5
n = 1000
SE 60.6 (60.1; 61.3) 72.9 (72.4; 73.4) 61.6 (60.7; 62.5) 77.3 (76.5; 78.0) 80.3 (78.9; 81.5) 79.8 (78.6; 81.0)
SP 67.0 (66.9; 67.2) 69.1 (69.0; 69.2) 65.8 (65.6; 65.9) 70.6 (70.5,70.8) 66.8 (66.4; 67.2 ) 83.8 (83.3; 84.2)
Gmean 63.6 (63.3; 63.9) 70.9 (70.6; 71.1) 63.1 (62.7; 63.6) 73.6 (73.3; 74.0) 72.3 (71.5; 73.1) 80.9 (80.0; 81.6)
ByGBayesian Globa l Model; ByG AOBayesian Global M odel after Optimization.
Table 4. Performances comparison—Santa cruz (Death/MI).
samp les
n = 1000
SE 60.8 (60.2; 61.3) 42.4 (41.9;43.1) 33.5 (33.0; 34.0) 72.9 (72.6; 73.5)
SP 74.9 (74.8; 75.1) 74.2 (74.1;74.3) 73.6 (73.5; 73.7) 74.9 (74.8; 75.1)
tion between autonomic functionality and CV mortality
is documented [20]. Increased HRV reflects a healthy
ANS that is able to respond to changes in the environ-
mental circumstances. By contrast, decreased HRV is a
marker of ANS inflexibility, which may precede more
sys- temic problems [21].
Depressed HRV has been reported in several CVD, in-
cluding coronary artery disease (CAD) and heart failure.
Actually, HRV is a strong and independent predictor of
mortality in CAD patients (after MI). HRV is depressed
in these patients, with a reduction in the total power of
the signal, presenting some parameters that indicate a
prevalence of sympathetic activation, which may lead to
cardiac electrical instability. Thus, HRV parameters
(time domain, frequency domain) should be explored as
quantitative markers of ANS activity, as they are s ignif i-
cantly correlated with all-cause mortality, cardiac death,
and arrhythmic death [20,22].
HRV is usually assessed with two types of recordings:
1) short-term (e.g. 5 minutes); 2) long-term (~24 hours).
Although the latter is a stronger risk predictor, HRV as-
sessed from short recordings also provides useful prog-
nostic information [20]. Ideally, HRV parameters should
be assessed within one week after MI. However these
parameters are significant mortality predictors even when
measured after that period [23].
Current risk assessment tools do not include HRV pa-
rameters. However, there are several HRV derived para-
meters that can potentially be applied to improve the
CVD risk assessment. The flexibility of the developed
Bayesian global model solves this problem, as it allows a
straightforward integration of additional knowledge/new
risk factors .
This is the main focus of the ongoing research: the se-
lection and incorporation of HRV parameters in order to
improve risk assessment and consequently the patients’
stratification. The incorporation mechanism is assured by
the developed combination methodology however the
Copyright © 2013 SciRes. ENG
selection of the specific HRV parameters must be care-
fully considered.
Time domain parameters may include: 1) HRV mean,
the average value o f RR interbeat interv als; 2) HRV SDNN
the normal-to-normal (NN) intervals standard deviation;
3) HRV RMSSD the square root of the mean squared dif-
ferences of successive NN intervals; 4) HRV NN50 the
number of interval differences of successive NN intervals
greater than 50 ms; and 5) HRV pNN50 the proportion of
NN50 considering the total of NN intervals.
In the frequency domain, three main spectral compo-
nents can be identified: the very low frequency (VLF:
0.01 - 0.04 Hz), the low frequency (LF: 0.04 - 0.15 Hz)
and the high frequency components (HF: 0.15 - 0.4 Hz).
Changes in the LF and the HF components reflect sym-
pathetic and parasympathetic activities, and their ratio
(LF/HF) is considered as a marker of the sympatho-vagal
balance controlling the heart rate [22]. A different energy
distribution was observed in MI patients, VLF compo-
nents are responsible for the main amount while a minor
part is assigned to HF components [23]. The correlation
between these components and specific conditions must
be further investigated to obtain the required data to per-
form the incorporation in the global framework. Spectral
analysis must be conducted on the HRV signals obtained
from the ECG recordings performed on CAD patients.
Non-linear phenomena are also involved in HRV as
cardiac activity is also regulated by intrinsically non-
linear mechanisms. Some non-linear parameters can be
identified such as 1/f slope of Fourier spectra, Sample
Entropy, Lempel-Ziv Complexity.
The impact of HRV parameters in the characterization
of CAD patients w ill be con ducted during hospitalization
of these patients in LPHC. An integrated clinical plat-
form, integrating the developed algorithms, will be im-
plemented. In addition to the information obtained from
the hospital information system, ECG (Holter) signals
will be collected to derive the HRV parameters.
5. Conclusion
The two developed methodologies improved the perfor-
mance of risk assessment when compared to the one
achieved by the current risk assessment tools. Moreover,
the combination methodology allows important features
such as the ability to deal with missing risk factors as
well as the incorporation of new risk factors. However,
we believe that the incorporation of the Heart Rate Va-
riability parameters can significantly improve the risk
assessment/patient stratif ica tio n.
6. Acknowledgements
This work was partially fina nced by iCIS (CENTRO-07-
ST24-FEDER-002003) and by Cardiorisk (PTDC/EEI-SII/
[1] WHO, World Health OrganizationCardiovascular Dis-
eases,” Fact Sheet No. 317, 2009.
[2] EHN, European Heart Net work, Heal thy Hea rts for All,”
Annual Report 2009, 2010.
[3] H. Reiter, et al., “HeartCycle: Compliance and Effective-
ness in HF and CAD Closed-Loop Management,” Pro-
ceedings of the 31st Conference of the IEEE EMBS, 2009.
[4] N. Boye, et al., “PREVE White PaperICT Research
Directions in Disease Prevention,” 2010.
[5] I. Graham, et al., “Guidelines on Preventing Cardiovas-
cular Disease in Clinical Practice: Executive Summary,”
European Heart Journal, Vol. 28, 2007, pp. 2375-2414.
[6] J. Perk, et al., “European Guidelines on Cardiovascular
Disease Prevention in Clinical Practice,” European Heart
Journal, Vol. 33, 2012, pp. 1635-1701.
[7] NVDPA, Guidelines for the Assessment of Absolute
Cardiovascular Disease Risk,” National Heart Foundation
of Australia, 2009.
[8] E. Antman, et al., “The TIMI Risk Score for Unstable
Angina/Non-St Elevation MI—A Method for Prognosti-
cation and Herapeutic Decision Making,” Journal of
American Medical AssociationJAMA, Vol. 284, No. 7,
2000, pp. 835-842.
[9] E. Boersma, et al., “Predictors of Outcome in Patients
with Acute Coronary Syndromes without Persistent
ST-Segment Elevation; Results from an International Tri-
al of 9461 Patients,” Circulation, American Heart Associ-
ationAHA, Vol. 101, 2000, pp. 2557-2657.
[10] E. Tang, C. Wong and P. Herbinson, “Global Registry of
Acute Coronary Events (GRACE) Hospital Discharge
Risk Scores Accurately Predicts Long Term Mortality
Post-Acute Coronary Syndrome,” American Heart Jour-
nal, Vol. 153, No. 1, 2007, pp. 30-35.
[11] E. Auer and R. Kohavi, “An Empirical Comparison of
Voting Classification Algorithms: Bagging, Boosting and
Variants,” Machine Learning, Vol. 36, 1998, pp. 1-38.
[12] Tsymbal, et al., “Ensemble Feature Selection with the
Simple Bayesian Classification,” Information Fusion, Vol.
4, No. 2, 2003, pp. 87-100.
[13] G. Samsa, G. Hu and M. Root, “Combining Information
from Multiple Data Sources to Create Multivariable Risk
Models: Illustration and Preliminary Assessment of a
New Method,” Journal of Biomedical Biotechnology, Vol.
2, 2005, pp. 113-123.
[14] Twardy, “Knowledge Engineering Cardiovascular Baye-
sian Networks from the Literatur e,” Technical Report
2005/170, Monash University, 2005.
[15] P. Gonçalves, et al., “TIMI, Pursuit and Grace Risk
Scores: Sustained Prognostic Value and Interaction with
Revascularization in NSTE-ACS,” European Heart
Journal, Vol. 26, 2005, pp. 865-872.
Copyright © 2013 SciRes. ENG
[16] S. Paredes, T. Rocha, P. de Carvalho, J. Henriques, J.
Morais, J. Ferreira and M. Mendes, “Cardiovascular
Event Risk Assessment—Fusion of Individual Risk As-
sessment Tools Applied to the Portuguese Population,”
Proceedings of the 15th International Conference on In-
formation Fusion, 2012, pp. 925-932.
[17] S. Paredes, T. Rocha, P. de Carvalho, J. Henriques, J.
Morais, J. Ferreira and M. Mendes, Improvement of
CVD Risk Assessment Tools’ Performance through In-
novative Patients’ Grouping Strategies,” Proceedings of
the 34th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, 2012.
[18] N. Friedman, et al., “Bayesian Network Classifiers,”
Machine Learning, Vol. 29, 1997, pp. 131-163.
[19] J. Han, M. Kamber and J. Pei, Data Mining: Concepts
and Techniques,” 3rd Edition, Morgan Kaufmann, 2011.
[20] Heart Rate Variability. Standards of Measurement, Phy-
siological Interpretation and Clinical Use. Task Force of
the European Society of Cardiology and the North Amer-
ican Society of Pacing and Electrophysiology,” European
Heart Journal, Vol. 17, 1996, pp. 354-381.
[21] A. Kemp, et al., “Depression, Comorbid Anxiety Disord-
ers, and Heart Rate Variability in Physically Healthy,
Unmedicated Patients: Implications for Cardiovascular
Risk,” PLoS One, Vol. 7, No. 2, 2012.
[22] M. Pagani, et al., “Power Spectral Analysis of Heart Rate
and Arterial Pressure Variabilities as a Marker of Sym-
patho-Vagal Interaction in Man and Conscious Dog,”
Circulation, Vol. 59, 1986, pp. 178-193.
[23] J. Bigger, et al., “Frequency Domain Measures of Heart
Period Variability to Assess Risk Late after Myocardial
Infarction,” Journal of the American College of Cardiol-
ogy, Vol. 21, 1993, pp. 729-736.