Journal of Software Engineering and Applications, 2011, 4, 491-496
doi:10.4236/jsea.2011.48057 Published Online August 2011 (http://www.SciRP.org/journal/jsea)
491
The Use of Fuzzy Clustering and Correlation to
Implement an Heart Disease Diagnosing System in
FPGA
Evaldo Renó Faria Cintra, Tales Cleber Pimenta, Robson Luiz Moreno
Federal University of Itajuba, Itajuba, Brazil.
Received June 11th, 2011, revised July 13th, 2011, accepted July 20th, 2011.
ABSTRACT
In this paper we present a signal processing method capable of detecting cardiopathies in electrocardiograms that was
implemented in FPGA. The adopted procedure is based on fuzzy clustering to reduce the amount of data sampling, and
a comparison with samples from a previously established database. By using the correlation method on the samples, it
is possible to establish an initial indication of a cardiopathy. The reduced number of samples of the clustering process
turns the processing simpler and allows its hardware implementation. According to the tests conducted, the method
achieves 91% correct diagnoses.
Keywords: Cardiopathy, Heart, Correlation, Fuzzy Clustering, Electrocardiogram
1. Introduction
Due to the large number of death caused by heart dis-
eases, researchers have been working on the search for
solutions that can provide early detection of heart prob-
lems, and thus increase the chance of survival [1-3].
In order to diagnose a cardiopathy some factors are
taken into account such as patient’s age and physical
activity. Nevertheless, the main analysis is based on the
electrocardiogram. Some studies [2-6] describe computer
systems running signal processing techniques that evalu-
ate the characteristics of the electrocardiograms to obtain
a preliminary diagnosis of any disease. Those techniques
turn possible an automatic patient diagnostic system. It
allows remote monitoring that can trigger an alarm to
notify the patient or the medical team, which in turn can
take early actions to treat the problem as soon as it ap-
pears.
This article intends to show a signal processing tech-
nique that uses fuzzy clustering to reduce the amount of
data to be processed, and a correlation method used to
identify heart problems on the electrocardiogram. The
smaller amount of data means also hardware simplifica-
tion. It does allow FPGA implementation and provides
diagnosis similar to software implementations [4-6], as it
will be shown in Section 6.
Due to the presence of noise and DC components in
the electrocardiograms, signal processing tools such as
the third order Butterworth filter were used. The elimina-
tion of DC level allows more accuracy to the results.
The signal is them compared to a signal database and a
correlation value between then is generated. The diag-
nosed cardiopathy corresponds to the signal from the
database that shows the highest direct correlation with
the signal under analysis.
Next section presents the electrocardiogram and its
main features that are used to diagnose a cardiopathy.
Section three describes the fuzzy clustering process that
is used to reduce the processing. The fourth section pre-
sents the diagnosis based on the correlation. Section five
describes validating system. The sixth section describes
the tests conducted and finally the last section presents
the conclusions and compares our results with others
listed in the literature.
2. Electrocardiogram
The electrocardiogram is a test widely used to assess
cardiac rhythm disturbances. By using electrocardiogram,
it is possible obtain information of structural cardiac
problems such as myocardial ischemia, myocardial elec-
trophysiological disturbances, pericardial diseases, heart
position, cardiac pacing, systemic electrolytic and meta-

The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA 492
bolic alterations, documentation of autonomous and
pharmacological influences (therapeutic or toxic) [1].
The exam also shows the entire heart conduction path.
Figure 1 highlights the main signal segments of a
typical ECG signal. Segment P represents the depolariza-
tion that runs on both atriums, starting on the right atrium
and later on the left one. The segment represented by PR
is the time interval the electric pulse flows in the His, and
the left and right branches. It indicates the beginning of
the atrium activation until the ventricular activation.
The ventricular depolarization is represented by seg-
ment QRS. The Q wave is the negative segment just be-
fore the positive QRS. The R wane is the positive part of
the QRS, and the S wave is the negative segment just
after the positive QRS.
The ST segment corresponds to the beginning of the
ventricular repolarization. The T wave describes the final
ventricular repolarization.
The diagnosis of a cardiopathy is made by assessing
and detecting any amplitude variation or time variation
during each interval
3. Fuzzy Clustering
The fuzzy clustering process for data pruning [7] allows
the system to process a smaller number of samples to
describe the main features of the original signal. There-
fore, the required processing to generate the signal diag-
nosis is faster and consequently the required hardware to
process it can be simplified.
The clustering process consists on the application of
orthogonal transformations and fuzzy clustering to ex-
tract the fuzzy rules from the input data. Each cluster is
characterized by a function that indicates the output ten-
dency due to an input near to certain values.
That process can be used to create a control system.
The control actions are implemented by a system training
in which the clusters are generated, and their respective
P PR
QRS
ST
T
Figure 1. Typical electrocardiogram waveform with seg-
ment indication.
memberships will perform the actions at the system out-
put. In this system we have used known values at input
and the system is conditioned to generate the desired
output. Therefore, the generated clusters are functions
that will provide the correct output, according to their
cluster training.
In this work, the clustering process was used to iden-
tify the position and value of each cluster, since they are
generated to operate in the most relevant points of the
input signal in order to generate the output control. The
input signal used to generate the clusters is the electro-
cardiogram with a cardiopathy. Therefore, the generated
clusters describe the main features of a signal with a cer-
tain cardiopathy [7].
In this work, the clustering process is used to indicate
the value and location of each cluster, without requiring
the generation of functions or rules corresponding to
each cluster.
Consider the set of N input-output data pairs where X
is the n dimensional input vector


12
,,,
n
X
xx x
corresponding to the acquisition time of each electrocar-
diogram signal and vector


12
,,,
n
y yYy is the
electrocardiogram samples generated each time for vec-
tor X. Here, n corresponds to the nth sample.
The parameters ai and bi of the corresponding func-
tions in each rule are obtained through expression (1) [7].
 
1
..
T. T
X
XX 


 y
(1)
where,


12
.,.,,.
eeMe
X
XX X
  (2)
According to expression (2), Xe is a matrix


,1
e
X
X
i
,
and the activation of each rule is provided by

, which
is a diagonal matrix whose normalized degree is the
diagonal element.
ki
u

1
()
ik
ki M
i
i
A
x
u
A
k



(3)
where,
ki —is the normalized degree of participation of each
input for rule Ri [7]:
u
Ai is a group of fuzzy antecessors of a given i–rule,
given by expression (4) [7].
1
()( )
n
iij
j
i
A
x 

x (4)
where the membership degree of each rule, regarding to
the input xi, is given by μij.
Once Ai is achieved, the normalized degree of ante-
cessor for rule Ri can be obtained.
By running the algorithm for the reduction of number

The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA493
of clusters, it is obtained a vector v that provides the pro-
totype of the most important prototypes of clusters. Vec-
tor v is given by expression (5) [7]:




1
() 1
1
1
.
,1
Nm
l
ki k
lk
Nm
l
ki
k
uz
vi
u






M
(5)
where
Zk—is the matrix in which each column represents the
input output pair as Zk = [Xk,Yk];
m—is a fuzziness parameter (m > 1);
M—is the number of rules;
N—is input-output data pairs;
l—number of interactions.
The waveform presented in Figure 2 presents a typical
electrocardiogram signal. This signal is obtained by de-
tecting and amplifying tiny electrical changes on the skin
that are caused when the heart muscle “depolarises” dur-
ing each heartbeat. A typical electrocardiogram wave-
form is obtained in millivolts per second according to the
PhysioNet database.
According to the fuzzy clustering process, 20 clusters
were generated, that describe the cluster prototypes to be
processed.
Table 1 presents the points generated by the clustering
process for the ECG signal of Figure 2, as given by ex-
pression (5). Column Position Vector indicates the posi-
tion of each cluster during the electrocardiogram signal
sampling time, and the column Cluster Vector indicates
the voltage value of each cluster.
Figure 3 shows the 20 clusters, from Table 1, ob-
tained by fuzzy clustering process from the signal of
Figure 2. Those points describe the main changes in the
electrocardiogram signal. The samples were selected
0 0.15 0.3 0.45 0.6 s
mV
0.4
0.3
0.2
0.1
0
–0.1
–
0.2
Figure 2. Typical electrocardiogram waveform.
Table 1. Location of generated clusters.
Point Position Vector ClusterVector
1 0.030 0.076438
2 0.079 0.38141
3 0.106 0.012971
4 0.140 –0.065785
5 0.169 –0.019915
6 0.198 –0.02885
7 0.228 –0.055106
  
19 0.591 0.02855
20 0.622 -0.032333
1
13
2
45
6
7
8
9
11
12
13
10
14
15
16
17
18
19
20
0 0.15 0.3 0.45 0.6 s
mV
0.4
0.3
0.2
0.1
0
–0.1
–0.2
Figure 3. Clusters obtaited by the fuzzy clustering process.
according to the fuzzy clustering process. Thus, the sys-
tem will use the 20 samples for the diagnosis.
4. Correlation
Many computer programs are used to obtain diagnoses,
such as Hidden Markov Models [1], Fuzzy classifiers [6],
Artificial Neural Network and Rough Set Theory [8],
Discrete Wavelet Transform [9], and Adaptive Net-
work-based Fuzzy Interferences System [10]. Correlation
was used in this work to reduce the processing required
and to simplify the hardware used to implement it.
An electrocardiogram signal does not have an exact
equation, therefore the diagnostic system will work with
the variation of the features over several signal samples.
By using the fuzzy clustering, these samples will be
reduced to a set of 20 samples, which are represented by
the generated clusters.

The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA 494
The clusters generated by the fuzzy clustering process
are compared with the clusters of signals from a database,
whose diagnosis is known. This comparison is done by
calculating the correlation among them. In calculating
the correlation, three outcomes are possible:
 If the correlation value is equal or close to –1, it indi-
cates a strong inverse correlation between two sig-
nals;
 If the result of correlation is equal or close to zero, it
indicates no correlation among the signals compared;
 If the result of correlation is equal or close to 1, it in-
dicates a strong direct correlation among the signals
compared.
The system will identify the diagnosis as the signal from
the database that receives the highest correlation with the
assessed signal.
The calculation of correlation between two signals can
be obtained by expression (6) [11]:
x
y
x
y
n





 (6)
where,
ρ—is the correlation value;
x—is calculated according to expression (7).
x
XMX (7)
X—is the set of points of the sampled signal;
MX—is the arithmetic mean of these sampled points,
given as (8):
X
MX n  (8)
y—is given by equation (9):
y
YMY (9)
Y—is the set of points of the signal pattern to be com-
pared;
MY—is the arithmetic mean of these sampled points,
given as (10):
Y
MY n  (10)
n—is the number of points for X and Y;
σx—is the standard deviation of x;
σy—is the standard deviation of y.
According to the correlation calculation presented by
equation (6), it can be observed that by using the fuzzy
clustering process of the number of points to be proc-
essed in the correlation are smaller, thus the processing
time becomes shorter.
5. Validation System
In order to demonstrate the effectiveness of the tech-
niques previously described, a system was created to
validate the proposed method. The validation system
receives the electrocardiogram signal samples to be di-
agnosed. The signal is filtered and the most important
features of the signal are obtained by the clustering proc-
ess. The diagnostics can be obtained from the smaller set
by correlation.
The proposed system was simulated on MATLAB®.
The electrocardiogram signals used to create the database
and to perform the tests were obtained from PhysioNet
database [1]. After the simulation, the system was vali-
dated in an FPGA implementation on a XILINX Spar-
tan®-3A Starter FPGA. Figure 4 shows a summary of
the validation system used [1].
The Physionet Data presented in Figure 4 represents
the electrocardiogram signal acquisition. Each signal is
represented by 2,500 samples at a sampling frequency of
333 Hz.
In the first block, the sampled signal is digitally fil-
tered by a third–order Butterworth low-pass filter. The
main feature of the filter is a flat frequency response in
its bandwidth and a zero response outside its bandwidth.
The magnitude of the N–order function with a bandwidth
cutoff frequency
p
w is given by expression (11).

2
2
1
1
N
p
Tjw
w
ew





(11)
Figure 5 shows the Butterworth filter response.
It was calculated the arithmetic average of the signal in
order and them subtracted from the signal on each sam-
ple in order to eliminate the DC offset.
In the second block, 20 samples are separated to be
processed by the fuzzy clustering process. The input sig-
nal samples are processed according to expression 1. The
block outputs the 20 samples, obtained by the clustering
system.
In the third block, the 20 samples are correlated with
the database signal samples, as described in Section IV.
It outputs the generated correlation values.
Implementation in FPGA Physionet Data
Block 1 Block 2
Block 4 Block 3
Leads of ECG signals
samples
Digital Filter and
eliminate DC level
Clustering
process
Corre lat ion
Probable
Diagnosis
Figure 4. Block diagram of the validation system.

The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA495
2
1
1e
1
0
p
w
w
T
Figure 5. Butterworth transfer function.
In the last block, the correlation values from the pre-
vious block are compared, and the signal from the data-
base that presents largest direct correlation is indicated as
the probable diagnosis.
6. Results and Conclusions
According to the literature, performance results are pre-
sented in terms of sensitivity (Se), positive predictivity
(Pp) and accuracy (Acc).
The Se parameter indicates the percentage of correct
diagnoses compared to diagnoses not detected. It can be
obtained according to the equation (12) [12].
Tp
Se Tp Fn  (12)
where Tp and Fn are the correct and undetected diag-
noses, respectively.
The Pp parameter indicates the percentage of correct
diagnoses in compared to wrong diagnoses. It is given by
expression (13) [13].
Tp
Pp Tp Fp  (13)
where Fp indicates the wrong diagnoses.
The Acc–parameter indicates the accuracy of the sys-
tem and can be obtained according to Equation (14) [12].
1err
total
N
Acc N  (14)
where Nerr indicates the number of wrong diagnoses and
TN indicates the total number of diagnoses.
Tests were applied to the proposed system to verify its
effectiveness and the results are summarized in Table 2.
Table 3 shows a comparison of our work with others
in the literature.
Table 2. Performance comparison.
PatientSystem DiagnosisMedical Diagnosis Result
1 Angina Angina TP
2 Angina Angina TP
3 Angina Angina TP
4 Infarction Angina and Infarction TP + FN
5 Infarction Angina and Infarction TP + FN
6 Infarction Angina FN + FP
73 Angina Angina TP
Table 3. Comparison with other work.
Paper Accuracy Pp Se
[1] - 85% 83%
[2] - 75% 83%
[3] - 88% 87%
[4] - 78% 89%
[5] - 81% 84%
[6] 93.13% - -
[9] - 99.59% 99.68%
[15] 96.75 % - -
This Work 91% 92% 85%
It can be observed from Table 3 that our work is simi-
lar or superior to others. The proposed system allows the
development of a fast and simple implementation since
the use of fuzzy clustering reduces the number of sam-
ples to be processed.
Additionally, our system allows the diagnosis of more
than one cardiopathy at the same time. The generated
figures of merit were subject to three types of possible
diagnoses. Therefore our system shows an additional ca-
pacity compared to others.
By using fuzzy clustering, the signal processing is
greatly reduced since the correlation is not conducted on
all the signal samples.
Since the calculations conducted requires less samples,
the system can be easily implemented in hardware, once
it requires less memory. It can also be implemented in a
dedicated hardware by using VHDL, and thus imple-
menting a fast and efficient system. The system was im-
plemented in a Xilinx Spartan®-3A Starter Kit with the
Spartan-3A FPGA. It is a low cost board that runs at 50
MHz. It has 32 MB DDR2 SDRAM memory, I/O
RS-232, serial port serial, 4 bottoms, 4 switches, LEDs,

The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA 496
clock counter and JTAG USB download port [14]. Mi-
croblaze® is a XILINX 32-bit RISC soft processor Intel-
lectual Property-IP [15]. The obtained results were the
same as using MATLAB®.
The system was implemented in a Spartan-3A FPGA
according to Section 5. The tests were conducted for
many sets of samples, and it was observed that the num-
ber of clock cycles for 20 samples is approximately 9
times shorter than the number of clock cycles for 213
samples of the whole signal. Therefore, the fuzzy clus-
tering process, used to reduce the number of samples to
be processed, caused a reduction in the number of clock
cycles in the hardware implementation.
Thus, there is an almost linear relationship between the
number of clock cycles and samples to be processed.
7. Acknowledgements
This work was supported by CAPES, CNPq, FAPEMIG
and UNIFEI.
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