J. Biomedical Science and Engineering, 2011, 4, 289-296 JBiSE
doi:10.4236/jbise.2011.44039 Published Online April 2011 (http://www.SciRP.org/journal/jbise/).
Published Online April 2011 in SciRes. http://www.scirp.org/journal/JBiSE
Cardiac arrhythmias detection in an ECG beat signal using
fast fourier transform and artificial neural network
Himanshu Gothwal1, Silky Kedawat1, Rajesh Kumar2
1Department of Compute r Science and Engineering, Malaviya National Institute of Technology, Jaipur, India;
2Department of Electri cal Engineering, Malaviya National Institute of Technology, Jaipur, India.
Received 15 April 2010; revised 13 May 2010; accepted 17 May 2010.
ABSTRACT
Cardiac Arrhythmias shows a condition of abnor-
mal electrical activity in the heart which is a threat
to humans. This paper presents a method to analyze
electrocardiogram (ECG) signal, extract the fea-
tures, for the classification of heart beats according
to different arrhythmias. Data were obtained from
40 records of the MIT-BIH arrhythmia database ( o n ly
one lead). Cardiac arrhythmias which are found are
Tachycardia, Bradycardia, Supraventricular Tachycar-
dia, Incomplete Bundle Branch Block, Bundle Branch
Block, Ventricular Tachycardia. A learning dataset
for the neural network was obtained from a twenty
records set which were manually classified using
MIT-BIH Arrhythmia Database Directory and do cu-
mentation, taking advantage of the professional ex-
perience of a cardiologist. Fast Fourier transforms
are used to identify the peaks in the ECG signal and
then Neural Networks are applied to identify the
diseases. Levenberg Marquardt Back-Propagation
algorithm is used to train the network. The results
obtained have better efficiency then the previously
proposed methods.
Keywords: Card iac Arrhythmias; Neural Network;
Electrocardiogram (ECG); Fast Fourier Transform (FFT)
1. INTRODUCTION
Electrocardiogram contains a wealth of diagnostic in-
formation routinely used to guide clinical decision mak-
ing. ECG remains the reference standard for diagnosis
despite the advance of many other diagnostic techniques
[1]. With the features present in ECG Signal various
Cardiac Arrythmias can be predicted. Within the last
decade many new approaches to feature extraction have
been proposed, for example, algorithms from the field of
artificial neural networks [2-5], genetic algorithms [6],
wavelet transforms [7], filter as well as heuristic meth-
ods mostly based on nonlinear transforms. In our Project
twenty records (chosen arbitrarily) are used each in the
training and testing of th e EKG classifier in this project.
The overview is focused on the descrip tion of the princi-
ples. Algorithmic details can be found in the original
papers that are referenced at the end of this article. Be-
yond feature extraction and deflection identification,
many papers have been published in related fields. Con-
ventional approach to predict these diseases is to analyze
the ECG signal by the doctor manually. Various con-
figurations of neural network are tried and the best con-
figuration is proposed here. The results obtained are bet-
ter then earlier approaches. The method used for peak
identification is also very flexible for detecting peaks in
ECGs that do not follow the normal pattern. Section 2
discusses the problem and various aspects related to it.
The methodology for Identification of QRS complex and
disease prediction is discussed in Section 3. Section 4
deals with the results and discussions.
2. PROBLEM FORMULATION
Cardiac arrhythmias can be detected using an ECG Sig-
nal. By determining the features present in an ECG sig-
nal various arrhythmias like Tachycardia, Bradycardia,
Supraventricular Tachycardia, Incomplete Bundle Bran-
ch Block, Bundle Branch Block, Ventricular Tachycardia
can be detected. Using Fast Fourier Transform noise can
be removed form ECG signal and using Neural Net-
works Arrhythmias can be detected efficiently.
The ECG is a noninvasive technique that is inexpen-
sive, simple, and reproducible. It is one of the most
commonly used diagnostic test that can be recorded rap-
idly with the extremely portable equipment and gener-
ally is always obtainable [1]. Electrocardiography has a
basic role in cardiology since it consists of effective,
simple, noninvasive, low-cost procedures for the diag-
nosis of cardiovascular disorders that have a high epi-

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 290
demiologic incidence and are very relevant for their im-
pact on patient life and on social costs. Biomedical en-
gineering is the application of engineering principles and
techniques to the medical field. It combines the design
and problem solving skills of engineering with medical
and biological sciences to help improve patient health
care and the quality of life of individuals. Cardiac dis-
ease is an umbrella term for a number of different dis-
eases affecting the heart [8]. Cardiac arrhythmias are a
common accompaniment of a variety of cardiac diseases
[9]. Cardiac arrhythmias become important when they
cause symptoms, threaten life, or are indicative of an
adverse prognosis. In the last few years, most attention
has been paid to ventricular ectopic beats. They have
prolonged implications for a variety of cardiac patholo-
gists. The research in cardiac arrhythmia has increased a
lot but a system which can detect various arrhythmias
present with a high accuracy and in real time is still not
available. Due to this several people die as they have no
idea of any arrhythmia present till it reaches to a level
which can be risky for the life. The large majority of the
deaths are sudden deaths after a heart attack. Sudden
death is defined as death rising less than one hour after
the first symptoms felt by the victim. It concerns about
300 000 people who die suddenly each year in the U.S.
and 60 000 people per year in France. 90% of sudden
deaths are due essentially to cardiac arrhythmias: 20% of
cardiac arrhythmias are caused by heart block or pause
(bradycardia) and 80% of them are caused by ventricular
fibrillation (VF), frequently initiated by ventricular ta-
chycardia (VT) [10].
Fundamentally an ECG is a graphic representation of
the electrical activity of the heart muscle. A brief over-
view of this electrical activity follows.
When cardiac muscle cells are excited, they produce
an electrical impulse lasting approximately 300 ms. [11]
[12].This is followed shortly by mechanical contraction
of the muscle cells. The electrocardiographic deflections
are termed P, QRS complex, T and U as in Figure 1. The
P wave represents Atrial activation; QRS complex
represents ventricular activation or depolarization. The T
wave represents ventricular recovery or re-polarization
and the S-T segment, the T wave and the U wave to-
gether represent the total duration of ventricular recovery.
The QRS complex is the most striking waveform within
the electrocardiogram (ECG). Since it reflects the elec-
trical activity within the heart during the ventricular
contraction, the time of its occurrence as well as its
shape provide much information about the current state
of the heart. Du e to its characteristic shape (see Figure 1)
it serves as the basis for the automated determination of
the heart rate, as an entry point for classifica tion sch emes
Figure 1. Diagrammatic representation of the basic electrocar-
diographic deflections.
of the cardiac cycle. The standard clinical apparatus is
the 12-lead system [13], whereas in this project a 3-lead
system is used. Both feature 3 electrodes placed on the
limbs. The MIT-BIH database is collaboration between
MIT (Massachusetts Institute of Technology) and the
Beth Israel Hospital (BIH) to produce a public database
of EKG recordings for the analysis of arrhythmia and
other cardiovascular conditions [14] .It consists of two-
channel, half-hour ambulatory EKG recordings, totaling
forty-eight records collected from forty-seven anony-
mous patients, collected from 1976 to 1979.
Sinus node lies in the superior part of the right atrium
at its junction with the superior vena cava. It is heart’s
dominant pacemaker. Its intrinsic rate is modulated by a
variety of neurohormonal influences to cause rate accel-
eration in situations of increased demand for cardiac
output. Normal Sinus rhythm is defined as rate between
60 and 100 beats per minute. Lower rates are termed
sinus bradycardia and faster rates sinus tachycardia. Si-
nus tachycardia reflects extracardiac pathology, for ex-
ample, hyperthyroidism, anemia, or a catecholamine-
secreting tumor. The term Supraventricular tachycardia
(SVT) encompasses a range of common arrhythmias in
which the atrial o r atrioventr icular (AV) node is essential
for the perpetuation of the tachyarrhythmia [15,16]. Su-
praventricular Tachycardia (SVT) is a fast heart rate that
begins in the upper part of the heart (atria), above the
ventricles. Normally, the heart’s electrical system pre-
cisely controls the heart’s rhythm; in this condition, ab-
normal electrical connections cause the heart to beat too
fast. [17] Most Supraventricular Tachycardias result from
abnormal electrical connections (bypass tracts) in the
heart that short-circuit the normal electrical system and
cause an increase in electrical activity. Supraventricular
tachycardia may cause an uncomfortable feeling that the
heart is racing, pounding , and/or beating irregularly (pal-
pitations). If Supraventricular tachycardia recurs, medica-
tions or a procedure called catheter ablation may be
needed to correct the abnormal heartbeat. Bundle branch
block (BBB) is a disruption in the normal flow of elec-

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 291
trical pulses that drive the heart beat. Bundle branch
block belongs to a group of heart problems called Intra-
ventricular conduction defects (IVCD) [18]. There are
two bundle branches, right and left. The right bundle
carries nerve impulses that cause contraction of the right
ventricle (the lower chamber of the heart) and the left
bundle carries nerve impulses that cause contraction of
the left ventricle. Bundle branch block is a slowing or
interruption of nerve impulses. A problem may exist in
any of the three bundles. Patients with BBB are gener-
ally without symptoms unless the disease is severe
enough to cause a complete. Left bundle branch block
usually happens as a consequence of other diseases such
as arteriosclerosis, rheumatic fever , congenital heart disease,
myocarditis, myocardial infarction, metastatic heart tu-
mors, or other invasions of the heart tissue. Right bundle
branch block happens less often from underlying heart
disease. Detection of BBB usually takes place during a
normal physical examination. The block shows up as a
widening of the second heart sound. Confirmation of
BBB is obtained by electrocardiogram (ECG). The pat-
tern seen in the electrocardiogram indicates pulses in a
heart beat and their duration. QRS duration of greater
than 110 milliseconds is a diagnostic indication of BBB
[14].
Conventionally these diseases are treated by the
symptoms shown or for verification by analyzing ECG
manually. A better approach to analyze the ECG effi-
ciently with high accuracy is proposed in this paper.
3. PROPOSED METHODOLOGY
The proposed methodology is depicted in the block dia-
gram in Figure 2.
Initially ECG Signals are preprocessed for removal of
power line noise and high frequency interference. Then
deflections in the ECG Signal Q, R, S are identified and
through these deflections QRS complex is identified
which is a very important feature in identifying arrhyth-
mias. A neural network is trained with 20 dataset con-
taining features of QRS complex which are maximum
QRS width, minimum QRS complex width, Average
QRS width and the Heart Rate. Once trained, the net-
work is tested on 20 more datasets which have gone
through the same procedure as by training dataset.
3.1. QRS Complex Identification & Feature
Extraction
The system for QRS complex identification works in
three phases. The ECG signals from MIT-BIH Arrhyth-
mia database were collected from physionet in text for-
mat using rdsamp-O-Matic tool. The rdsamp-O-Matic
allows converting binary signal files from PhysioBank
into text form. The ECG signals from database were
Figure 2. Methodology.
preprocessed for removal of power line noise and high
frequency interference. Deflection Identification is then
applied to the data thus obtained. Deflection indices
found works as inp ut to feature extraction and then neu-
ral network is applied to train the system.
3.1.1. R P eak Detection
The first stage is the extraction of suitable metrics form
the signal of interest. Before these can be extracted from
the ECG signal, the Q, R, S deflections in each beat were
identified. This is performed with an algorithmic script
with the following methodology:
The first goal is the detection of the R Peak because
once the R-Peak is detected; it can be used to detect the
Q and S points easily. Due to the idiosyncratic nature of
the QRS complex & the distinctive characteristics o f the
R peak, this is readily identified even in the most dis-
torted ECG readings. Thus it is used as the basis for
ECG feature identification. Here a Digital signal proc-
essing based method was implemented to identify the
deflections.
Figure 3 represents the original ECG Signal taken for
the analysis. Initially FFT is applied on the ECG signal
using Eq.1
2
1
0e,0,,1 (1)
i
Nnk
N
kn
Xxnk N



 

Figure 4 represents the FFT filtered ECG then low fre-

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 292
0200040006000800010000 12000 14000 1600018000
-
0.2
0
0.2
0.4
0.6
0.8
1
Figure 3. Original ECG.
02000 40006000800010000 12000 14000 16000 18000
-
0.2
0
0.2
0.4
0.6
0.8
1
Figure 4. FFT filtered ECG.
02000 4000600080001000012000140001600018000
0
0.2
0.4
0.6
0.8
1
1.2
Figure 5. Peaks after first pass.
quency components were removed. On the resultant
signal inverse FFT is applied given by Eq.2. Peaks de-
tected after first pass to the filter is shown in Figure 5.
Now the signal is filtered for detecting the R peaks.
2
1
0
1e,0,,1 (2)
i
Nnk
N
kn
XxnkN
N



 

The Signal obtained after first pass is again pass to the
filter and after second pass R peaks detected are shown
0200040006000800010000 1200014000 16000 18000
0
0.2
0.4
0.6
0.8
1
1.2
Figure 6. Peaks after second pass.
02000 40006000 8000 10000 12000 14000 1600018000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Comparative
ECG
R
-
Peak
De t ection
Plot
Figure 7. R-Peaks detected in the ECG signal.
in Figure 6.
Figure 7 represents the detected peaks with the origi-
nal ECG signal which clearly shows that right peaks are
detected.
3.1.2. Peak Detection
The R-point, as calculated above, is found to have rea-
sonable accuracy. Q wave is defined as “A negative
wave at the onset of the QRS complex and the valley
[minimum] is defined as Q point” [12]. Thus to positio n
the Q point, it is positioned as the local minimum in a
short (approx. 0.05 sec) window around the left of
R-point estimated in Eq.3
 
dd
0and 0(3)
dd
yy
xxt xxt
xx

3.1.3. S Peak Detection
The S-point is first approximated as the point where the
slope has its first negative to positive zero crossing after
the R-point found in (2). It was found that it could be
placed more accurately as the local minimum in a 0.05
sec window before the above approximation.
 
dd
0and 0(4)
dd
yy
xxs xxs
x
x
 

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 293
This is to be true for the s window length of 0.05 sec.
3.2. Feature Extraction
Feature as RR interval (used medically as an indicator of
Ventricular Heart Rate) metrics are generated from the
deflection positional information. To determine RR in-
terval two R peaks in consecutive beats is calculated and
their difference is computed. Heart Rate is 60/RR inter-
val beats per minute.
60
Rate=RRinterval beats per minute (5)
With these features various Cardiovascular Arrhyth-
mias are detected as Right bundle branch block. Right
bundle branch block is a delay or block of conduction
within the right bundle branch. A delay of conduction
manifests as incomplete right bundle branch block. A
QRS duration greater than 0.14 sec results in right bun-
dle branch block. Bradycardia occurs on resting heart
rate of under 60 beats per minute, though it is seldom
symptomatic until the rate drops below 50 beat/min.
Tachycardia refers to rapid beating of the heart as a heart
rate greater than 100 beats per minute in adult. Diseases
were predicted from these features derived as according
to medical science for Tachycardia heart rate > 100 bpm
(beats per minute) [19]. Normal Sinus Rhythm is 60 to
100 bpm. Ventricular tachycardia for heart rate is from
101 to 250 bpm and QRS width > 0.12 sec. Normal QRS
width is 0.04 - 0.10 sec. Incomplete Bundle Branch
Block for QRS width between 0.10 sec and 0.12 sec and
Bundle Branch Block for QRS width > 0.12 sec [11,12].
3.3. Disease Prediction Using ANN
The artificial neural networks play a significant role in
the field of artificial intelligence. In spite of the advent
of computers, a system which can imitate the human
brain is of a great demand. Artificial Neural Network is
the technique where we try to copy the working of hu-
man brain. It has a very significant role in the field of
artificial intelligence. An ANN comprises of intercon-
nection of artificial neurons which follows the function
of biological neurons and are basic building blocks of
the network. They learn from the data fed to them and
keep on decreasing the error during training time and
once trained properly, their results are very much same
to the results required from them [20] thus referred to as
universal approximators [21]. The error is decreased by
modifying weights of individual neurons.
The most popular neural netwo rks us ed by research ers
are the multilayer feed forward neural network trained
by the back propagation algorithm [20,22]. There are
different kinds of neural networks classified according to
operations they perform or the way of interconnection of
neurons. The ANNs are capable of learning the desired
mapping between the inputs and outputs signals of the
system without knowing the exact mathematical model
of the system. Since the ANNs do not use the mathe-
matical model of the system, they are excellent estima-
tors in non linear systems.
3.4. Network Architecture and Training
To solve the problem of identifying diseases, a Feed-
Forward Network with one input layer, one hidden layer
and one output layer is proposed as shown in th e Figure
8. The input layer consist of five neurons with the trans-
fer function of tan-sigmoid, the hidden layer consist of
four neurons with the transfer function of log-sigmoid
whereas the output layer consist of six neurons with the
linear transfer function. The inputs given to the input
lay er a r e ma xi mu m Q R S w i dt h , mi n i mu m QR S co mp le x
width, Average QRS width and the Heart Rate whereas
the outputs obtained are the presence of diseases namely
Tachycardia, Bradycardia, Super Ventricular Tachycardia,
Incomplete Bundle Branch Block, Bundle Branch Block,
Ventricular Tachycardia.
Where, D1 is Tachycardia, D2 is Bradycardia, D3 is
Super Ventricular Tachycardia, D4 is Incomplete Bundle
Branch Block, D5 is Bundle Branch Block, D6 is Ven-
tricular tachycardia.
Hence the output from the network will be governed
by the following equations:

4
111
1
tansig (6)
aajja
j
TwPb











5
2212
1
logsig (7)
aajja
j
TwTb










Figure 8. Network architecture.

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 294

4
3323
1
p
urelin (8)
aaajj a
j
TDwTb



 






Where Tmn is output from nth neuron of mth layer,
wpqr is weight for jth input for qth neuron of pth layer and
bmn is bias for nth neuron of mth layer.
The network is trained by Levenberg Marquardt
Back-Propagation algorithm [20,22] which updates the
weights and biases according to Levenberg Marquardt
up to the mean squared error of 0.001 which was at-
tained in one hundred and seventy eight epochs with 20
training dataset. The training graph is shown in Figure
9. As soon as the training procedure is over, the neural
network gives almost the same output pattern for the
same or nearby values of input as visible by the mean
squared error in Figure 9. This tendency of the neural
networks which approximates the output for new input
data is the reason for which they are used as intelligent
systems.
4. EVALUATION AND EXPERIMENTAL
RESULTS
Since the process is divided on two levels, first identify-
ing the features and second predicting the disease from
the features identified. Hence, the accuracy of the results
depends on accuracy of both levels. So the results on
both levels have been depicted below.
4.1. Feature Extraction for Worst Case
Feature extraction for the worst case ECG signal which
involves lots of noise with it and high variations is taken
to prove the efficiency of the system. The various steps
involved in Section 3.1.1 are shown in Figures 10-13 for
the worst case which clearly shows that the algorithm is
very good in handling even the worst cases especially
because of its two pass filter.
Figure 9. Training of the network.
Figure 10. Original ECG.
Figure 11. FFT filtered ECG.
Figure 12. Peaks after first pass.
Figure 13. Peaks after second pass.

H. Gothwal et al. / J. Biomedical Science and Engineering 4 (2011) 289-296 295
Figure 14. R-Peaks detected in ECG signal.
As clear by the Figure 14, the algorithm is able to de-
tect the R-Peaks even in a very irregular ECG. Hence
once the features are identified correctly, we are sure that
correct input will be given to the neural network.
4.2. Effect of Hidden Layer Neurons and Final
Network
Several combinations were tried with different number
of neurons in the hidden layer. The results of these
changes are depicted in the Table 1.
The decr ement in perfor mance on increasing the num-
ber of neurons could be due to overtraining. On the basis
of above readings, the network with five neurons in the
input layer, four neurons in the hidden layer and six
neurons in the output layer was selected. The network so
formed was then tested on 20 dataset with an accuracy of
98.48%.
Figure 15 shows the predictions and the actual dis-
ease identification for each of the twenty dataset. X-axis
represents the dataset id whereas the Y-axis represents
the disease id. A circle represents the actual value (target
value) an d th e star (*) r epr esent the value predicted b the
network. The results shown above clearly indicate that
the system can assist a doctor to a very good level about
disease indications from the ECG and hence the neces-
sary steps could be taken before the disease strikes the
patient.
The solution proposed has an accuracy of 98.48%.
Table 2 depict a comparative study of various tech-
niques used previously and clearly indicates that pro-
Table 1. Effect of hidden layer neurons on accuracy.
Number of neurons in
Hidden Layer Accuracy
2 94.17%
3 96.25%
4 98.48%
5 97.50%
6 96.67%
7 96.67%
Figure 15. Predicted output against desired output.
Table 2. Comparative results of different methods.
Method Accuracy
BSS-Fourier [7 ] 85.04%
MOE [23] 94.0%
Fhyb-HOSA [25] 96.06%
DWT-NN [26] 96.79%
FTNN [24] 98.0%
ICANN [3] 98.37%
Proposed 98.48%
posed method is better than previous ones.
5. CONCLUSION AND FUTURE WORK
In this paper, Fast Fourier Transform has been used to
identify features from an ECG signal. Those features
generated the training as well as testing dataset for the
Artificial Neural Network to predict diseases. It is found
that the system is very robust and can identify and pre-
dict features even from highly abnormal ECG. This is a
big benefit since the ECG pattern varies in many factors
from person to person. These factors may be height of
peaks, width of QRS complex, presence or absence of
peaks, heart rate etc. QRS complex is the most important
section of any ECG and once it has been detected, one
can use it in other study or system also. High accuracy of
the system makes it highly reliable and efficient.
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