Vol.2, No.6, 439-444 (2009)
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
Wavelet based detection of ventricular arrhythmias with
neural network classifier
Sankara Subramanian Arumugam1, Gurusamy Gurusamy2, Selvakumar Gopalasamy3
1Department of Electrical and Electronics Engineering, NPA Centenary Polytechnic College, Kothagiri, Tamilnadu, India;
2Department of Electrical and Electronics Engineering, Bannariamman Institute of Technology, Sathyamangalam, Tamilnadu, India;
3Department of Electrical Sciences, Vinayaka Mission’s Kirupananda Variyar Engineering College, Salem, Tamilnadu, India.
Email: sankar_aru@yahoo.com; ngselva_kumar@yahoo.com
Received 29 August 2009; revised 10 September 2009; accepted 15 September 2009.
This paper presents an algorithm based on the
wavelet decomposition, for feature extraction
from the Electrocardiogram (ECG) signal and
recognition of three types of Ventricular Ar-
rhythmias using neural networks. A set of Dis-
crete Wavelet Transform (DWT) coefficients,
which contain the maximum information about
the arrhythmias, is selected from the wavelet
decompositio n. Th ese co eff icients are fed to t h e
feed forward neural network which classifies
the arrhythmias. The algorithm is applied on the
ECG registrations from the MIT-BIH arrhythmia
and malignant ventricular arrhythmia databases.
We applied Daubechies 4 wavelet in our algo-
rithm. The wavelet decomposition enabled us to
perform the task efficiently and produced reli-
able result s.
Keywords: Daubechies 4 Wavelet; ECG; Feed
Forward Neural Network; Ventricular Arrhythmias;
Wavelet Decomposition
The cardiac disorders which are life threatening are the
ventricular arrhythmias such as Ventricular Tachycardia
(VT), Ventricular Fibrillation (VFIB) and Ventricular
Flutter (VFL). The classification of ECG into these dif-
ferent pathological disease categories is a complex task.
Successful classification is achieved by finding the
characteristic shapes of the ECG that discriminate effec-
tively between the required diagnostic categories. Con-
ventionally, a typical heart beat is identified from the
ECG and the component waves of the QRS, T and P
waves are characterized using measurements such as
magnitude, duration and area (Figure 1).
In an arrhythmia monitoring system or a defibrillator,
it is important that the algorithm for detecting ECG ab-
normalities should be reliable. The patient will be loos-
ing a chance of treatment if the system is not able to de-
tect the arrhythmia. Also a false positive detection will
initiate a defibrillator to give improper therapeutic inter-
vention. Both situations are linked with the patient’s life.
An algorithm based on Wavelet Transform is quite ef-
ficient for the detection of ventricular arrhythmias com-
pared to the Discrete Fourier Transform (DFT) since it
permits analyzing a discrete time signal using frequency
components. The DWT has been used for analyzing,
decomposing and compressing the ECG signals [1].
The wavelet transforms make possible, the decompo-
sition of a signal into a set of different signals of restrict-
ed frequency bands. Wavelet processing can be consid-
ered as a set of band pass filters [2]. Moreover, the dis-
crete wavelet transform corresponds to a multiresolution
analysis [3] which can reduce the redundancy of each
filtered signal so that the processing algorithm can be
applied effectively to a small data subset of the original
signal [4,5,6,7,8,9].
A classification scheme is developed in which a feed
forward neural network is used as a classification tool
depending on the distinctive frequency bands of each
arrhythmia. The Back Propagation (BP) algorithm al-
lows experiential acquisition of input/output mapping
Figure 1. Normal ECG waveform.
A. S. Subramanian et al. / J. Biomedical Science and En gineering 2 (2009) 439-444
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
knowledge within multi-layer networks [10]. BP per-
forms the gradient descent search to reduce the mean
square error between the actual output of the network
and the desired output through the adjustment of the
weights. It is highly accurate for most classification
problems because of the property of the generalized data
rule. In the traditional BP training, the weights are
adapted using a recursive algorithm starting at the output
nodes and working back to the first hidden layer.
In our study, an algorithm is implemented to detect
and classify Ventricular Tachycardia (VT), Ventricular
Fibrillation (VFIB) and Ventricular Flutter (VFL) using
wavelet decomposition and neural classification.
The ECG registrations from MIT-BIH arrhythmia and
malignant ventricular arrhythmia databases are used here.
The analysis is done using Daubechies 4 wavelet.
Arrhythmias are the abnormal rhythms of the heart.
They cause the heart to pump the blood less effectively.
Most cardiac arrhythmias are temporary and benign. The
ventricular arrhythmias are life threatening and need
treatment. Such ventricular arrhythmias are Ventricular
Tachycardia, Supraventricular Tachycardia, Ventricular
Fibrillation and Ventricular Flutter.
VT is a difficult clinical problem for the physicians
(Figure 2). Its evaluation and treatment are compli-
cated because it often occurs in life-threatening situa-
tions that dictate rapid diagnosis and treatment. Ven-
tricular tachycardia is defined as three or more beats of
ventricular origin in succession at a rate greater than 100
beats/minute. There are no normal-looking QRS com-
plexes. Because ventricular tachycardia originates in the
ventricle, the QRS complexes on the electrocardiogram
are widened (>0.12 seconds). Ventricular tachycardia has
the potential of degrading to the more serious ventricular
VFIB is a condition in which the heart's electrical ac-
tivity becomes disordered (Figure 3). During Ventricular
Figure 2. ECG waveform with VT.
Figure 3. Ventricular fibrillation.
Figure 4. Ventricular flutter.
fibrillation, the heart's ventricles contract in a rapid and
unsynchronized way. It is a medical emergency. If this
condition continues for more than a few seconds, blood
circulation will cease, as evidenced by lack of pulse,
blood pressure and respiration, and death will occur [11].
VFL is a tachyarrhythmia characterized by a high
ventricular rate with a regular rhythm (Figure 4). The
ECG shows large sine wave-like complexes that oscil-
late in a regular pattern. There is no visible P wave. QRS
complex and T wave are merged in regularly occurring
undulatory waves with a frequency between 180 and 250
beats per minute. In severe cardiac or systemic disease
states, ventricular tachycardia can progress to ventricular
flutter, then to ventricular fibrillation.
Computer based detection and classification algorithms
can achieve good reliability, high degree of accuracy and
offer the potential of affordable mass screening for car-
diac abnormalities.
Till now, many linear techniques for the detection of
ventricular arrhythmias have been developed, such as the
probability density function method [12], rate and ir-
regularity analysis, analysis of peaks in the short-term
A. S. Subramanian et al. / J. Biomedical Science and En gineering 2 (2009) 439-444
SciRes Copyright © 2009 http://www.scirp.org/journal/JBISE/
autocorrelation function [13], sequential hypothesis tes-
ting algorithm [14], correlation waveform analysis, four
fast template matching algorithms, VF-filter method [15,
16], spectral analysis [17], and time-frequency analysis
[18]. However, these methods exhibit disadvantages,
some being too difficult to implement and compute for
automated external defibrillators (AED’s) and implant-
able cardioverter defibrillators (ICD’s), and some only
successful in limited cases. For example, the linear tech-
niques [13,18] using the features of amplitude or fre-
quency have shown their limits, since the amplitude of
ECG signal decreases as the VFIB duration increases,
and the frequency distribution changes with prolonged
VFIB duration.
Openly accessible at
The VF filter method relies on approximating the
VFIB signal as a sinusoidal waveform. The method is
equivalent to using a bandpass filter, the central fre-
quency of which is the mean signal frequency. The spec-
tral analysis technique relies on the fact that the VFIB
frequency contents are concentrated in the bandwidth
4-7Hz [13]. The increased power in this band of fre-
quencies is the major indication of the presence of VFIB.
The above spectral analysis technique is applied to sta-
tionary signals. However, abrupt changes in the non-
stationary ECG signal are spread over the whole fre-
quency range. Important time varying statistical charac-
teristics are lost once the signal has been Fourier trans-
In recent years time-frequency analysis techniques
have proved to be useful in experimental and clinical
cardiology. These techniques are needed to fully de-
scribe and characterize the various ventricular arrhyth-
mias and facilitate the development of new detection
schemes with high correct detection rate, or equivalently,
with low false-positive and false-negative performance
statistics. The wavelet transforms make possible, the
decomposition of a signal into a set of different signals
of restricted frequency bands. Wavelet processing can be
considered as a set of band pass filters [15]. Moreover,
the discrete wavelet transform corresponds to a multi-
resolution analysis [12] which can reduce the redun-
dancy of each filtered signal so that the processing algo-
rithm can be applied effectively to a small data subset of
the original signal.
A classification scheme is developed in which a feed
forward neural network is used as a classification tool
depending on the distinctive frequency bands of each
arrhythmia. The algorithm is applied to ECG signals
with ventricular arrhythmia disorders.
The wavelet transform is a mathematical tool for de-
composing a signal into a set of orthogonal waveforms
localized both in time and frequency domains. The de-
composition produces coefficients, which are functions
of the scale (of the wavelet function) and position (shift
across the signal).
A wavelet which is limited in time and frequency is
called “mother wavelet”. Scaling and translation of the
mother wavelet gives a family of basis functions called
“daughter wavelets”.
The wavelet transform of a time signal at any scale is
the convolution of the signal and a time-scaled daughter
wavelet. Scaling and translation of the mother wavelet is
a mechanism by which the transform adapts to the spec-
tral and temporal changes in the signal being analyzed.
The continuous wavelet transform for the signal x(t) is
defined as:
where a and b are the dilation (scaling) and translation
parameters, respectively. A wide variety of functions can
be chosen as mother wavelet ψ, provided ψ(t) Є L2 and
The DWT makes possible the decomposition of ECG
at various scales into its time-frequency components. In
DWT two filters, a Low Pass Filter (LPF) and a High
Pass Filter (HPF) are used for the decomposition of ECG
at different scales. Each filtered signal is down sampled
to reduce the length of the component signals by a factor
of two. The output coefficients of LPF are called the
Approximation while the output coefficients of HPF are
called the Detail. The approximation signal can be sent
again to the LPF and HPF of the next level for second
level of decomposition; thus we can decompose the sig-
nal into its different components at different scale levels.
Figure 5 shows the decomposition process of a signal
into many levels. The details of all levels and the ap-
proximation of the last level are saved so that the origi-
nal signal could be reconstructed by the complementary
filters. The reconstructed signals at each level are repre-
sented by the notations D1, D2, D3 and so on.
Figure 5. Wavelet decomposition and reconstruction.
A. S. Subramanian et al. / J. Biomedical Science and En gineering 2 (2009) 439-444
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
Figure 6. Ideal frequency bands for the various details.
Figure 6 shows the ideal frequency bands for a sam-
pling frequency of 360 samples/second. Depending on
the scaling function and the mother wavelet, the actual
frequency bands and consequent frequency selectivity of
the details are slightly different.
In this section we present the algorithm which efficiently
detects and classifies the various ventricular arrhythmias
using wavelet decomposition and neural classification.
5.1. Description of the Algorithm
The algorithm first decomposes the ECG signals using
wavelet transform. The decomposed signals are fed to
the feed forward neural network. The wavelet theory is
used as a time-frequency representation technique to
provide a method for enhancing the detection of life
threatening arrhythmias. It reveals some interesting
characteristic features such as low frequency band (0-5
Hz) for VFL, two distinct frequency bands (2-5, 6-8 Hz)
for VT, and a broad band (2-10 Hz) for VFIB.
A classification scheme is developed in which a neural
network is used as a classification tool depending on the
above distinctive frequency bands of each arrhythmia.
The algorithm is applied to ECG signals obtained from
patients suffering from the arrhythmias, mentioned above.
5.2. ECG Analysis Using Wavelet
To quantify the differences between the various ar-
rhythmias with the help of the wavelet transform, the
densities for different frequency bands are compared.
The wavelet transform is performed using Daubechies 4
wavelet since it provides better sensitivity [19].
The algorithm computes the volume underneath the
3D plots of the square modulus of the wavelet transform
for several regions of the time-frequency plane. The
time-frequency plane is divided into seven bands rang-
ing from 0 to 15 Hz. For sinus rhythm the energy is cal-
culated within the time intervals T1 and T2 integrated
over the whole frequency axis. The time interval T1 is
determined by the region of QRS complex, and the time
interval T2 is determined by the region of the T-wave.
As the wavelet transform is very sensitive to abrupt
changes in the time direction, the energy parameter over
the given time intervals attains relatively large values for
normal subjects. This parameter is referred to as Tv and
it defines the sum of the energy parameters computed
within the intervals T1 and T2. Although the signals of
VFL and VT exhibit a QRS-complex, the parameter
value T2 for these signals remains relatively small, ow-
ing to the absence of abrupt changes in the region of the
T-wave. Therefore, the value of Tv will still be smaller
than that of the normal subjects.
The 2D and 3D wavelet transform contours of Normal
Sinus Rhythm (NSR), VT and VFL are shown in Figure 7.
Figure 7. (a) 2D wavelet of NSR, (b) 2D wavelet of VT, (c) 2D wavelet of VFL, (d) 3D wavelet of NSR, (e) 3D wavelet of VT
and (f) 3D wavelet of VFL.
A. S. Subramanian et al. / J. Biomedical Science and En gineering 2 (2009) 439-444
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The weights and biases of the A
work (ANN)
in the direction of the gradient, but in the
3. Classification of Ventricular
Arrhythmias Using Neural Network
There are quite lot of network topologies with power
ng strategies which exist to solve nonlinear prob-
lems [20,21,22,23]. For the classification of Ventricular
Arrhythmias, back propagation with momentum is used
to train the feed forward neural network [24].
A multilayer feed forward neural network with one
layer of hidden (Z) units is used for this problem. The
tput (Y) units have weights wjk and the hidden units
have weights vjk. During the training phase, each output
neuron compares its computed activation yk with its tar-
get value dk to determine the associated Error (E) by
using (3) for the pattern with that neuron.
kk ydE
rtificial Neural Net-
are adjusted to minimize the least-square
ror. The minimization problem is solved by gradient
technique, the partial derivatives of E with respect to
weights and biases are calculated using the generalized
delta rule. This is achieved by the back propagation of
the error.
When using momentum, the neural network is pro-
ceeding not
direction of the combination of the current gradient and
the previous direction of weight correction. Convergence
is sometimes faster if a momentum term is added to the
weight update formula. In the back propagation with
momentum, the weights for the training step t+1 are
based on the weights at training steps t and t-1. The
weight update formulae for back propagation with mo-
mentum are given by (4)
)]1()([)()1(  twtwztwtw kjkjk
)]1()([)()1(  tvtvxtvtv ijijijijij
where the learning factor α and momentum param
eural net-
d for the analysis [25]. The
rom the records 200, 203, 205, 207, 208,
14, 215, 217, 221, 223, 233, 106 etc were
re used for training. The learning
of VT,
) and (6).
eter µ
are constrained to be in the range 0 to 1, exclusive of the
end points. The weights and biases are initialized to
some random values and updated in each iteration until
the network has settled down to a minimum.
The classification of the VT, VFIB and VFL arrhyth-
mia is carried out using a Back-propagation n
work whose input is the energy level calculated by the
wavelet transform as described above, the output is a
three bit pattern, in which 100 corresponds to VFL, and
010 corresponds to VT, and 001 corresponds to VFIB.
The network has three layers: The input, the output
and one hidden layer. The input layer has seven nodes
representing the seven different frequency bands from
0-15 Hz. The output layer has three nodes that represent
the three different types of arrhythmia signals VFL, VT
effective size of the network and acceptable efficiency.
The learning rate and the momentum term are chosen to
be 0.7 and 0.3 respectively.
5.4. Data
The MIT-BIH arrhythmia and malignant ventricular ar-
rhythmia databases were use
and VFIB. The hidden layer consists of six nodes fo
VT episodes f
210, 213, 2
taken from the MIT-BIH arrhythmia database. VFIB and
VFL episodes were from MIT-BIH malignant ventricular
arrhythmia database from the records 418 – 430, 602,
605, 607, 609, 610, 611, 612, 614, 615 and cu01 – cu35.
The data files from arrhythmia database and malignant
ventricular arrhythmia database were sampled at 360
samples / second.
Twenty-five signals of VT, ten signals of VFL and five
signals of VFIB a
process took approximately 1000 iterations to c
with classification error of 0.001. Thirty signals
fifteen signals of VFL and four signals of VFIB are se-
lected as test set. For decomposition of ECG signal
Daubechies 4 wavelet is used.
In medical statistics, few parameters are important to
evaluate the performance of the algorithm. These pa-
rameters are sensitivity (Se) and positive predictivity (+P)
which can be computed using (5
where TP is True Positive, FP is False Positive and
False Negative.
Classification results of testi
Forward Neural Network with BP are shown in Table 1.
ias are shown in Table 2. The results
Detected Ventricular Arrhythmia Total
FN is
ng data sets using Feed
Sensitivity and Positive predictivity for the various ven-
tricular arrhythm
ow that the BP algorithm is reliable in the classifica-
tion of all the three types of ventricular arrhythmias.
Table 1. A comparison between the actual and detected ven-
tricular arrhythmias in terms of the number of patterns.
Actual Ventricular
Arrhythmia VT VFL VFIB 49
VT 30 0 0 30
VFL 1 13 1 15
VFIB 0 04 4
A. S. Subramanian et al. / J. Biomedical Science and En gineering 2 (2009) 439-444
SciRes Copyright © 2009 http://www.scirp.org/journal/JBISE/
Table 2sitivity and positive predictivity r the testing s
Ven Ar-TP FP FN sitive Pr
ctivity (%Sensitivity (%)
. Senfoet.
Openly accessible at
Po e-
VT 30 1 0 96.77 100
Tabl shows that te BP network misclashe
properythmi mcasee VFL
classified as VFIB, this is ause energy level in the
equency bands is high and common between VFL
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