Telecardiology and Teletreatment System Design for Heart Failures Using Type-2 Fuzzy Clustering Neural Networks

Rahime Ceylan , Yüksel Özbay & Bekir Karlik
International Journal of Artificial Intelligence And Expert Systems (IJAE), Volume (1): Issue (4) 100
Telecardiology and Teletreatment System Design for Heart Failures
Using Type-2 Fuzzy Clustering Neural Networks
Rahime Ceylan rpektatli@selcuk.edu.tr
Department of Electrical & Electronics
Engineering, Selcuk University,
Konya, Turkey
Yüksel Özbay yozbay@mevlana.edu.tr
Department of Electrical & Electronics
Engineering, Mevlana University,
Konya, Turkey
Bekir Karlik bkarlik@mevlana.edu.tr
Department of Computer Engineering,
Mevlana University, Konya, Turkey
Abstract
Proper diagnosis of heart failures is critical, since the appropriate treatments are
strongly dependent upon the underlying cause. Furthermore, rapid diagnosis is also
critical, since the effectiveness of some treatments depends upon rapid initiation. In this
paper, a new web-based telecardiology system has been proposed for diagnosis,
consultation, and treatment. The aim of this implemented telecardiology system is to
help to practitioner doctor, if clinic findings of patient misgive heart failures. This model
consists of three subsystems. The first subsystem divides into recording and
preprocessing phase. Here, electrocardiography signal is recorded from emergency
patient and this recorded signal is preprocessed for detection of RR interval. The
second subsystem realizes classification of RR interval. In other words, this second
subsystem is used to diagnosis heart failures. In this study, a combined classification
system has been designed using type-2 fuzzy c-means clustering (T2FCM) algorithm
and neural networks. T2FCM was used to improve performance of neural networks
which was obtained very high performance accuracy to classify RR intervals of ECG
signals. This proposed automated telecardiology and diagnostic system assists to
practitioner doctor to diagnosis heart failures easily. Training and testing data for this
diagnostic system include five ECG signal classes. The third subsystem is consultation
and teletreatment between practitioner (or family) doctor and cardiologist worked in
research hospital with prepared web page (www.telekardiyoloji.com). However,
opportunity of signal’s evaluation is presented to practitioner and expert doctor with
prepared interfaces. T2FCM is applied to the training data for the selection of best
segments in the second subsystem. A new training set formed by these best segments
was classified using the neural networks classifier which has well-known
backpropagation algorithm and generalized delta rule learning. Correct classification
rate was found as 100% using proposed Type-2 Fuzzy Clustering Neural Networks
(T2FCNN) method.
Keywords: Telecardiology, Type-2 Fuzzy C-Means Clustering, ECG, Neural Network, Diagnosis

1. INTRODUCTION
Some problems have been faced in carrying health service to human lived in far regions from city center
and rural fields. Telemedicine is envisioned as a major improvement for the quality and effectiveness of
healthcare. When look from this angle, telemedicine provide a potential which transport health service to
every kind of region whatever geographical settlement. Cardiology is most necessary application in
telemedicine systems. In many occurrences, both between practitioner and expert doctor carry weight for
patient’s state doing consultation quickly. If clinic findings of patient came to village clinic misgive heart
attack, practitioner need to take assistance from cardiology doctor worked in research hospital.
Telecardiology provide to practitioner this assistant.
The last four decades, computer-aided diagnostic (CAD) systems have been applied to the classification
of the ECG resulting in several techniques [1-3]. Included in these techniques are multivariate statistics,
decision trees, fuzzy logic, expert systems and hybrid approaches [3-7]. In our previous works, we had
showed clearly that neural network and fuzzy system with feature extraction methods had better
performances than the traditional clustering and statistical methods [8, 9]. Type-1 fuzzy c-means
clustering in [10-18] was used to choose the best patterns belong to same class in implemented systems
in our previous works. But, there are (at least) four sources of indefinites in type-1 fuzzy logic systems
(FLSs): (1) the explanations of the words that are utilized in the antecedents and consequents of rules
can be uncertain (words mean different things to different people). (2) Consequents may have a
histogram of values connected to them, especially when knowledge is extracted from a group of experts
who do not all agree. (3) Measurements that activate a type-1 FLS may be noisy and therefore uncertain.
(4) The data that are used to tune the parameters of a type-1 FLS may also be noisy [19, 20-23].
In this study, a telecardiology and teletreatment system which ensures interpretation easiness to
practitioner doctor and permit to consultant with expert doctor was proposed. Implemented telecardiology
system was formed three phases. First phase was to record signal and to realize preprocessing. The
ECG of a patient, which came to village clinic and his clinic findings misgive heart attack, was measured
with electrocardiography device, and then preprocessing was did on ECG signal. Second phase was
contained a diagnosis to ECG signal. The new classification system was constituted using type-2 fuzzy c-
means clustering algorithm and artificial neural networks for classification of ECG signal. Data taken from
MIT-BIH ECG arrhythmia database was used for training and testing of classification system. The training
and test data were included 5 ECG signal classes. It is examined that whether this system can distinguish
various types of abnormal ECG signals such as Right Bundle Branch Block (RBB), Left Bundle Branch
Block (LBB), Atrial Fibrillation (AFib), Atrial Flutter (AFlut) from Normal Sinus Rhythm (NS). These
arrhythmias are very dangerous for human life. Branch blocks may not be seen, but infarction in a patient
undergoing branch block can be seen. Patients with atrial fibrillation and flutter also are seen in the heart
of the possibility of throwing clots expected. This is the case in the brain can cause paralysis. In this case,
the patient is under observation by a doctor using proposed teletreatment system.
Classification accuracy on test data was obtained as 99% with this improved classification system. Third
stage was to transmit signal and diagnosis to research hospital. This stage was executed with web page.
Prepared www.telekardiyoloji.com web page provided consultation between research hospital and village
clinic. Additionally, opportunity of signal’s appraisal was presented to practitioner and expert doctor with
prepared interfaces.
2. MATERIALS AND METHODS
This paper presents using type-2 fuzzy c-means clustering algorithm to develop the performance of
neural network. In this paper, the separation of RR intervals in recorded ECG signal is done by QRS
detection algorithm implemented by Ahlstrom and Tompkins [24].
2.1. The Type-2 Fuzzy C-Means Clustering
Generally, computation of fuzzy memberships in FCM is achieved by computing the relative distance
among the patterns and cluster prototypes according to (1) [10, 14].
12 ( 1)
1
1
( )
( )
j i
C ji m
k
ki
u x
d
d
−
=
=
∑
(1)
Distance
( )ji kid d
denotes the distance between cluster prototype
( )j kv v
and pattern ix
. Now, to define
the interval of primary membership for a pattern, we define the lower and upper interval memberships

using two different values of m . The primary memberships that extend pattern ix
by interval type-2 fuzzy
sets become [23].
1 1 2
2
2 ( 1) 2 ( 1) 2 ( 1)
1 1 1
2 ( 1)
1
1 1 1
if
( ) ( ) ( )
( )
1
otherwise
( )
and
1
(
( )
C C Cm m m
ji ki ji ki ji kik k k
j i
C m
ji kik
ji
j i
d d d d d d
u x
d d
d d
u x
− − −
= = =
−
=

>

= 



=
∑ ∑ ∑
∑
1 1 2
2
2 ( 1) 2 ( 1) 2 ( 1)
1 1 1
2 ( 1)
1
1 1
if
) ( ) ( )
1
otherwise
( )
C C Cm m m
ki ji ki ji kik k k
C m
ji kik
d d d d
d d
− − −
= = =
−
=

≤





∑ ∑ ∑
∑ (2)
In (2), 1m and 2m are fuzzifiers that represent different fuzzy degrees. When we define the interval of
primary membership for a pattern, we use the highest and lowest primary membership of the interval for a
pattern. These values are denoted by upper and lower membership for a pattern, respectively. The uses
of fuzzifiers, which represent different fuzzy degrees, give different objective functions to be minimized in
FCM in Eq. (3) [10, 14, and 23].
2
1 1 1
( , ) ( ( )) subject to 1 for all
mC N C
j i ji ji
j i j
J U V u x d u i
= = =
= =∑∑ ∑
(3)
In type -1 FCM, final clustering stage can be summarized as:
If ( ) ( ) 1,..., and then is assigned to clusterj i k i iu x u x k C j k x j> = ≠
(4)
Unfortunately, Eq. (4) cannot be applied directly to our proposed method since the pattern set is extended
to an interval type-2 fuzzy set. We need to reduce the type of an interval type-2 fuzzy set before hard-
partitioning. Type-reduction before hard-partition should be handled carefully since upper and lower
membership
( )iu x and
( )iu x
cannot be used directly in achieving type-reduction before hard-
partitioning. Type-reduction was performed in order to estimate cluster centers. For this, left memberships
( )L
j iu x
and right memberships
( )R
j iu x
for all patterns have already been estimated to organize left ( Lv )
and right ( Lv ) cluster center (Eq. (5)), respectively [23].
1
1
( )
( )
N
i ii
x N
ii
x u x
v
u x
=
=
=
∑
∑ (5)
Therefore, type-reduction can be achieved using
( )L
j iu x
and
( )R
j iu x
to partition a pattern set into
clusters. In this approach, hard-partitioning can be obtained as follows [23].
( ) ( )
Type-reduction: ( ) , 1
2
and
Hard partitioning: Same as in (4)
R L
j i j i
j i
u x u x
u x j ,....,C
+
= =
(6)
In Eq (6), memberships
( )L
j iu x
and
( )R
j iu x
are also different according to each features for a pattern.
Therefore, a representative value for left and right membership needs to be computed for each feature.
This can be obtained as [23];

1
1
( ) if uses ( ) for
( ) where ( )
otherwise
and
( ) if uses ( ) for
( ) where ( )
otherwi
M R
j jjl i il i jR l
j i jl i
j
M L
j jjl i il i jL l
j i jl i
j
u x u x u x v
u x u x
M u
u x u x u x v
u x u x
M u
=
=

= = 

= =
∑
∑
se


 (7)
Where, M denotes the number of features for each pattern of ix .
In this study, classifier was trained training set obtained using different fuzzifier coefficients to examine
impact of fuzzifier coefficients on training period. Optimum fuzzifier pair was found empirically
as
2and3 21 == mm .
2.2. The Multilayer Perceptron (MLP) Architecture for Backpropagation Algorithm
In this study, a three-layered feed-forward (MLP) neural network architecture was utilized and trained
with the error backpropagation algorithm. The input signals of NN were obtained by cluster centers that
generalized by type-2 fuzzy c-means clustering. The Backpropagation algorithm is described step by
step as [24]:
1. Initialization: Set all the weights and biases to small real random values.
2. Presentation of input and desired outputs: Present the input vector x(1), x(2),…,x(N) and
corresponding desired response d(1),d(2),…,d(N), one pair at a time, where N is the number of training
patterns.
3. Calculation of actual outputs: Use Eq. (8) to calculate the output signals MNyyy ,...,, 21
)(
1
1
)1()1()1(
∑
−
=
−−−
+=
MN
j
M
i
M
j
M
iji bxwy ϕ
, 1,...,1 −= MNi
(8)
4. Adaptation of weights (wij) and biases (bi):
)().(.)( )1()1(
nnxnw l
ij
l
ij
−−
=∆ δµ
(9)
)(.)( )1()1(
nnb l
i
l
i
−−
=∆ δµ
(10)
and
[ ]( 1)
( 1)
( 1) ( )
( ) ( ) ,
( ) ( ) . ( ), 1
l
i i i
l
l li
i ki k
k
net d y n l M
n net w n l M
ϕ
δ ϕ δ
−
−
−
′ − =
=  ′ ≤ ≤

∑
(11)
where xj(n)= output of node j at iteration n, l is layer, k is the number of output nodes of neural network,
M is output layer, φ is activation function. The learning rate is demonstrated by µ. It may be noted here
that a large value of the learning rate may lead to faster convergence but may also result in oscillation.
With the purpose of achieving faster convergence with minimum oscillation, a momentum term may be
added to the basic weight updating equation.
After completing the training procedure of the neural network, the weights of MLP are frozen and
ready for use in the testing mode.
3. THE RESULTS OF NUMERICAL EXPERIMENTS
In this paper, a new telecardiology system for consultation and treatment was proposed. This
telecardiology model consists of three phases: (1) Data recording and preprocessing phase, (2)
Classification of RR intervals of ECG signal, and (3) Consultation between practitioner doctor and
cardiologist. Figure 1 shows block diagram of proposed web-based telecardiology system.

FIGURE 1: Block diagram of proposed telecardiology system
3.1. Recording and Preprocessing Phase
In the recording and preprocessing phase, electrocardiography signal is recorded from emergency
patient came to village clinic. After this recorded signal is preprocessed to detect RR interval of ECG
signals which were sampled at 360 Hz during record. Then, ECG signal is filtered with low pass and
high pass filters. Then, QRS detection is found from filtered ECG signal. The detected RR intervals are
arranged as 200 samples, which are called as a segment [8, 9].
3.2. Classification of RR Intervals of ECG Signal
As is can be seen in Figure 2, a new clustering-based approach is applied to classify the heart failures
from ECG signal using type-2 fuzzy c-means (T2FCM) algorithm. It is combination both a fuzzy self-
organizing layer and a supervised neural networks. It is connected in cascade, where the number of
data points is reduced using type-2 fuzzy c-means clustering before inputs are presented to a
supervised neural networks architecture. Therefore, the training period of the neural network is
decreased. The self-organizing layer is responsible for the clustering of the input data. The outputs of all
self organizing neurons (the cluster centers) form the input vector to the second Multi Layer Perceptron
(MLP) subnetwork. The number of data points is reduced using fuzzy c-means clustering before inputs
are presented to a neural network system. The cluster centers are classified by neural network trained
by backpropagation algorithm. ECG signal in this study belongs to five different heart failures (or
arrhythmias) which used as input to neural networks.
FIGURE 2: Structure of Type-2 Fuzzy Clustering Neural Network

3.2.1. Training and Test Data
Used training and test data of ECG arrhythmias were taken from MIT-BIH ECG Arrhythmias Database
[25]. Chosen types of arrhythmias were normal sinus rhythm (N), right bundle branch block (R), left
bundle branch block (L), atrial fibrillation (AFib) and atrial flutter (AFl.). Training patterns were sampled
at 360 Hz, so we arranged them as 200 samples in RR intervals for all arrhythmias, which are called as
a segment. Detection of RR intervals was executed using one of the known QRS detection algorithms
developed by Ahlstrom&Tompkins [26]. Training and test patterns were formed by blending from the
arrhythmias preprocessed with respect to the order above. The sizes of the training and test patterns
were 80 segments*200 samples (called as original training set) and 80 segments*200 samples,
respectively. The features of formed training and test patterns were given in Table 1.
TABLE 1: The number of segments for each arrhythmia
Training Test
Arrhythmia Record Time (minute) 80 Sets 80 Sets
Normal Sinus Rhythm 103 1.09-17.21 20 20
Right Bundle Branch Block 118 13.47-22.32 15 15
Left Bundle Branch Block 109 17.08-17.50 15 15
Atrial Fibrillation 202 29.35-30.06 15 15
Atrial Flutter 202 25.58-27.55 15 15
3.2.2. Test Results
In this paper, a type-2 fuzzy clustering neural network was proposed to diagnose an abnormal heart
beat on electrocardiogram. The improved T2FCNN [27] was obtained by combination of fuzzy clustering
layer and final classifier (Figure 2). In fuzzy clustering layer, type-2 fuzzy c-means clustering layer was
utilized to choose segments demonstrated features of arrhythmia class as well good for each
arrhythmia in original training set. On the other word, we proposed an approach that the number of
segment in original training set was reduced by type-2 fuzzy c-means clustering algorithm and process
of reducing was performed on each arrhythmia type individually. The experimental studies were realized
to find the minimum number of segment for each arrhythmia in the new training set. The number of
segments in original training set (80 segments*200 samples) and a new training set (38 segments*200
samples) obtained by T2FCM can be seen in Table 2.
TABLE 2: The number of segments for each arrhythmia
Arrhythmi
a type
In original training set
In new set obtained with
T2FCM
NN T2FCWNN
N 20 10
RBB 15 7
LBB 15 7
AFib. 15 7
AFl. 15 7
TOTAL 80 38
The optimum numbers of hidden nodes were determined as 40 for T2FCNN. Learning rate and
momentum constant were chosen as 1.0 and 0.7 in training via experimentation. Training error was
obtained as 7.18.10-9 %. The obtained results can be seen in Table 3. As it can be seen in this table,
we found training and test errors calculated from tables according to the equations given in [8, 9].
For comparison, the same processes were realized using neural network. On the other words, original
training patterns (80 segments*200 samples) were presented to neural network. As result, training and
test errors were obtained as 7.29.10-9 % and 0.055, respectively.

TABLE 3: Classification results for each arrhythmia in test
Architecture
Optimum
Configurati
on
Optimum
Fuzzy
Parameter
s
Test Error
(%)
NS RBB LBB
AFi
b
AFlt
MC
N
T2FCNN 200:40:5
m1= 3.0
m2= 2.0
0.022 20 15 15 15 15 0
NN 200:25:5 - 0.055 20 15 15 15 15 0
MCN: Misclassification number
3.3. Consultation Between Practitioner Doctor and Cardiologist
Consultation between practitioner doctor and cardiologist is the last phase of presented telecardiology
system in this paper [28]. First phase was to record signal and to realize preprocessing. Second phase
was a diagnosis phase that realized classification of ECG signal. The new classification system was
implemented by type-2 fuzzy c-means clustering algorithm, and artificial neural networks to detect ECG
arrhythmias. In third phase, the transport of signal and diagnosis to research hospital is made. This
stage was performed by web page. Prepared www.telekardiyoloji.com web page provided consultation
between research hospital and village clinic. In addition, chance of signal’s evaluation was presented to
practitioner and expert doctor with prepared interfaces. Interfaces were prepared with MATLAB
software and toolboxes (2008a) [28].
As seen in Fig.3, if “Record” button is activated in the prepared interface for village clinic, ECG data is
recorded with ECG device and send to computer using data acquisition card. Then, when “Classify”
button is pressed, ECG signal recorded from patient is filtered using low pass and high pass filters. RR
intervals of filtered ECG signal are detected. Each RR interval was classified by trained optimum
T2FCNN structure (Table 3) and classification results are demonstrated for each arrhythmia class in
interface, individually. On the other side, a number of beat determined for each arrhythmia class in
patient’s ECG signal can be seen by practitioner doctor. However, patient information (age, sexuality
and complaints etc.) can be registered in this interface. So, practitioner doctor can interpret patient’s
health state and consultation can be done with cardiologist using prepared web page
(www.telekardiyoloji.com). When “Transmit” button is activated, recorded ECG signal, classification
results of this signal and patient information are transmitted to research hospital. Transmitted
information is displayed on research hospital interface [28].

107
FIGURE 3: The prepared interface for village clinic
The prepared interface for research hospital can be seen in Fig.4. In this interface, when “ECG” button
is activated, ECG signal sent from village clinic is loaded to this interface. The any part of signal can be
investigated by entering “minimum” and “maximum” time value as second. If cardiologist worked in
research hospital press “Results” button, classification results and information about of patient is loaded
to this interface. So, cardiologist can interpret signal and results using this interface and consultation
can do with practitioner doctor worked in village clinic using the prepared web page
(www.telekardiyoloji.com) [28]. Moreover, the patients can use this system at his/her home easily.

108
FIGURE 4: The prepared interface for research hospital
4. 4. CONCLUSION
In this paper, the new telemedicine system as automated diagnostic system assisted to practitioner
doctor was presented. Furthermore, the T2FCNN was proposed and developed to classify
electrocardiography signals. In proposed structure, the conventional type-1 memberships for each
pattern were extended to type-2. These type-2 memberships were incorporated in the cluster updating
process. The cluster centers obtained by T2FCM are classified by neural network. The realized
T2FCNN structure was compared with the studies in literature. It can be seen in Table 4 that the most
high recognition rate was obtained using T2FCNN for five ECG class types.
Telecardiology systems in literature were developed for daily care of patients exposed to heart attack.
The proposed telecardiology system is fairly different others presented in literature because of helping
to person who didn’t suffer a heart attack beforehand. This telecardiology system consists of emergency
treatment and consultation for person suffered from ache of chest or arm, etc.

109
TABLE 4: Comparison with literature
Study Method
Number of
ECG
signal
class
Sensitiv
ity
(%)
Specificit
y
(%)
Average
Recogniti
on Rate
(%)
Osowski
(2001)
FNN 7 99.7 98 98.8
Özbay ve
ark.
(2006)
FCNN 10 96.7 100 98.3
Yu ve Chou
(2006)
ICA-MLP 8 98.37 99.6 98.9
Hosseini
(2007)
Forward NN 6 85.3 82.02 83.6
Übeyli
(2008)
Eigenvector
method
4 98.89 98.05 98.47
This study
(2011)
T2FCNN 5 100 100 100
5. ACKNOWLEDGEMENTS
This work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects under
Project No: 07101021
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