Download A neuro-fuzzy recognition of premature ventricular contraction

A neuro-fuzzy recognition of premature ventricular contraction
1
M. A. CHIKH, 2 M. AMMAR, 3 R. MAROUF
« Biomedical Engineering Laboratory - Tlemcen University- Algeria »
E-mail : [email protected],
2
[email protected], [email protected]
ABSTRACT
This paper presents a fuzzy rule based classifier and its
application to discriminate premature ventricular
contraction (PVC) beats from normals. An Adaptive
Neuro-Fuzzy Inference System (ANFIS) is applied to
discover the fuzzy rules in order to determine the correct
class of a given input beat. The main goal of our
approach is to create an interpretable classifier that also
provides an acceptable accuracy. The performance of the
classifier is tested on MIT-BIH (Massachussets Institute
of Technology-Beth Israel Hospital) arrhythmia database.
On the test set, we achieved an overall sensitivity and
specificity of 97.92 % and of 94.52% respectively.
Experimental results show that the proposed approach is
simple and effective in improving the interpretability of
the fuzzy classifier while preserving the model
performances at a satisfactory level.
Keywords: Adaptive Neuro-Fuzzy Inference System,
interpretable classification, MIT-BIH arrhythmia
database
Fuzzy models have been widely and successfully used
in many areas such as data mining [7], data analysis [8] ,
image processing [9] and industrial processes where
Takagi and Sugeno proposed a mathematical tool to build
a fuzzy model, two industrial processes are discussed : a
water cleaning process and a converter in steel-making
process [10]. Traditionally, fuzzy rules are generated
from human expert knowledge or heuristics, which brings
about good high-level semantic generalization capability.
On the other hand, some researchers have made efforts to
build fuzzy models from observational data, leading to
many successful applications [11],[12],[13],[14],[15].
Also, more and more efforts have been made to approach
the problem of interpretability of data-driven fuzzy
models [16],[17],[18],[19],[20], [21],[22],[23]. Recently
fuzzy logic and neural networks, have provided attractive
alternatives to the traditional equation-based techniques
to accommodate the non-linearity and imprecise
information involved in modeling complex systems.
Adaptive network-based fuzzy inference system (ANFIS)
is a specific approach in neuro-fuzzy modeling which
utilizes the neural networks to tune the rule-based fuzzy
systems [12],[24]. Successful implementations of ANFIS
in biomedical engineering have been reported recently in
classification [25],[26],[27],[28],[29]. Dalief Nauck and
Rudolf Kruse proposed a neuro-fuzzy model for the
classification of data (NEFCLASS), this model derives a
fuzzy rules from data to classify them into a number of
classes [30]. S. Bellal et al developed a technique for
classifying plethysnogram pulses via an implementation
of fuzzy inference system (FIS) which were tuned using
an ANFIS and ROC curves analysis [31]. The purpose of
this study is to enable neuro fuzzy model classifiers aid to
the Cardiologist in diagnosis. Furthermore, we aim to
increase the interpretability and understandability of the
diagnosis with the rules of neuro fuzzy model classifiers.
The paper is structured as follows: the explanation of
The Takagi-Sugeno Fuzzy Model and the ANFIS method
are presented in Section 2 followed by a presentation of
experimental data in Section 3. In Section 4, a structural
learning and then parameter learning are developed. In
Section 5, the results are presented and discussed.
Finally, Section 6 concludes the findings.
1. INTRODUCTION
A premature ventricular contraction (PVC) is an extra
heartbeat resulting from abnormal electrical activation
originating in the ventricles before a normal heartbeat
would occur. PVCs are common, particularly among
older people. This arrhythmia may be caused by physical
or emotional stress, intake of caffeine (in beverages and
foods) or alcohol. Other causes include coronary artery
disease (especially during or shortly after a heart attack)
and disorders that cause ventricles to enlarge, such as
heart failure and heart valve disorders [42].
They are more common in patients with sleep disordered
breathing than in those without [1]. Although the risk
associated with presence of PVCs is generally considered
to be low [2], recent studies in subjects with no history of
coronary artery disease have found that the risk of death
and coronary events is [2],[3] fold greater in subjects with
PVCs compared to those without [3],[4]. With regard to
the specific risk for arrhythmic death, a study involving
over 15,000 healthy men found that the presence of any
PVC was associated with a 3-fold risk of sudden cardiac
death [5]. Presence of complex PVCs increases
arrhythmic death risk further [2],[5],[6].
Automatic detection and classification of cardiac
arrhythmias such as PVC’s have become an important
thrust area of research in biomedical engineering and bioinformatics over the last few decades.
2. THEORY
2.1. The Takagi-Sugeno Fuzzy Model
Rule-based models of the Takagi-Sugeno (TS) type [32]
are suitable for the approximation of a broad class of
1
functions. The TS model consists of a set of rules where
the rule consequents are often taken to be linear functions
of the inputs:
with
Fig.1 shows equivalent ANFIS architecture,
(3)
Ri : if x1 is Ai1 and … xn is Ain then
oi = pi1x1 + …, pinxn + pi(n+1),
i= 1,…,M .
T
Here, x =[x1, x2, … , xn] is the input vector and oi the
output (consequent).
Ri denotes the ith rule, and Ai1,…, Ain are fuzzy sets
defined in the antecedent space by membership functions
[0,1] , pi1,…, pi(n+1) are the consequent
Aij (xj) :
parameters and M is the number of rules.
Each rule in the TS model defines a hyperplane in the
antecedent-consequent product space, which locally
approximates the real system’s hypersurface. The output
y of the model is computed as a weighted sum of the
individual rule contributions:
Fig.1. Adaptive Neuro-Fuzzy Inference System
architecture
(1)
Aij(xj) is the membership of input xj in the fuzzy set Aij,
i.e., it is the degree of match between the given fact and
the proposition Aij in the antecedent of the ith rule.
3. EXPERIMENTAL DATA
In this work, we classify the cardiac arrhythmias by a
neuro-fuzzy approach using ANFIS. The ECG signals used
in this work are recordings collected between 1975 and
1979 by the laboratory of BIH arrhythmia (Beth Israel
Hospital) in Boston in the United States, which is known
as the MIT-BIH data base [44]. The ECG signals are
sampled at a frequency of 360 Hz. Two or more
cardiologists have made the diagnosis for these various
records and they have annotated each cardiac cycle. These
annotations will be useful for learning the neuro-fuzzy
model classifier.
2.2. Adaptive Neuro-Fuzzy Inference System (ANFIS)
The choice of target diseases is dictated by the nature of
work itself:
where
is the degree of fulfillment of the ith rule:
(2)
ANFIS is an adaptive network which permits the usage of
neural network topology together with fuzzy logic. It not
only includes the characteristics of both methods, but also
eliminates some disadvantages of their lonely-used case.
Actually, ANFIS is like a fuzzy inference system with
this difference that here by using feed-forward back
propagation tries to minimize error. Consequent
parameters are calculated forward while premise
parameters are calculated backward. Several fuzzy
inference systems have been described by different
researchers [11], [33], [34]. The most common used
systems are the Mamdani type and Takagi-Sugeno type.
In our work, we use zero-order Takagi-Sugeno fuzzy
inference system, where the premise part of fuzzy rule is
fuzzy proposition and the conclusion part is a constant.
The advantage of this type is clear, because it gives a
powerful tool for data classification.
PVC: The premature ventricular contraction (see Fig.2)
Signal amplitude,mv
2000
1500
N
N
N
PVC
1000
500
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Output variables are obtained by applying fuzzy rules to
fuzzy sets of input variables. For example,
Rule 1: If x1 is A1 and x2 is B1 then y1=f1(x1, x2) = a1 x1 +
b1 x2 + c1
Rule 2: If x1 is A2 and x2 is B2 then y2=f2(x1, x2) = a2 x1 +
b2 x2 + c2.
2
147.5
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148.5
149
Time, sec
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150.5