Download Detection and Classification of Premature Ventricular Contraction

International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014
Detection and Classification of Premature
Ventricular Contraction using Cross Wavelet
Transform
R.Sunil kumar 1 M.Suchetha 2
1
PG Student, Department of Electronics And Communication, Vellammal Engineering College, Anna University, Chennai
2
Assistant Professor, Department of Electronics And Communication, Vellammal Engineering College, Anna University, Chennai
ABSTRACT
Premature
ventricular
contraction(PVC) is a cardiac problem generated
with improper heart beat. In this paper a method
using cross wavelet transforms(XWT) and the
wavelet coherence(WCOS) are used to identify the
patient with PVC disorder. Although many earlier
scholars have developed many algorithms for PVC
classification they have achieved efficiency less
than 93% The proposed method analyses the
Electrocardiogram(ECG) signal using XWT and
the parameters are extracted from the Wavelet
Cross Spectrum(WCS) and WCOS which are
obtained from the XWT. The pathologically
varying pattern from normal ECG to the
abnormal ECG identifies the presence of PVC. The
waveforms
are
selected
from
MIT/BIH
Arrhythmia database for evaluation and a
detection rate of true PVC close to 98% is
obtained.
The ECG signal analysis remains the crucial task
for the detection of cardiac arrhythmia. Several
methods has been proposed by various scholars. the
most of the algorithm are based on time domain
features[6], ECG morphology and heart beat interval
features[7], principle component analysis(PCA)[8],
hidden Markov models[9]-[11], self organizing
maps[12], wavelet and filter banks[13][14], statistical
classifier[15] and neural networks’[16][17]. Among
this most of the work is mainly concentrated on the
PVC detection. In the previous discussed literature the
two main drawbacks discussed are problem of finding
efficient feature sets and spotting out the optimum
architecture which has a unique solution.
INDEX TERMS- Cross Wavelet Transforms(XWT),
Premature ventricular contraction(PVC), wavelet
coherence(WCOS), wavelet cross spectrum(WCS),
Electrocardiogram(ECG)
I.
INTRODUCTION
Fig-1 Representation of a typical waveform.
Cardiovascular diseases(CVD) has became the
great threat for the people to cause death in the fast
developing world. The death rate due to CVD is 1 in
every 2.8 deaths[1]. Therefore it is very essential to
detect this CVD efficiently and start treatment but the
detection of this CVD in early stage is very tedious
process. The heart beat in the cardiac cycle of each
individual can be recorded in the ECG waveform. The
CVD can be identified by analysing the recorded ECG
waveform[2],[3].
ECG gave a clinical information regarding the
heart beat rate, morphology and the proper
functioning of heart at cheap rate and non-invasive
test[4]. Most of the ECG analysis is used for the
cardiac arrhythmias such as PVC. The immediate
detection and treatment is very important for the
patient with CVD because PVC's results from irritated
ectopic foci in the ventricular area of the heart. These
foci cause PVC which is independent of paceset by
the sino-atrial(SA) node[5]. PVC beats occurs when
ventricles starts to pumps prematurely or earlier to
atria. During this period in ECG waves the QRS
complex region will be expanded and P-waves will be
absent.
ISSN: 2249-2615
II.
ANTICIPATED SYSTEM
The combination of waveform shape and timing
interval classification
features gives better
classification results [11], which means the efficient
classification of arrhythmic beat are timing property
dependent rather than on waveform shape.
In this paper the cross wavelet transform(XWT)
is applied to the reference sample and the test sample
from which the spectral characters WCS and WCOS
are obtained. First the R-peak was detected for beat
segmentation followed by denoising and then R-peaks
are registered before they undergone into further
processing. The segmented heartbeat is then
normalized and interpolated with respect to time. In
this paper the normal case is taken as a reference
sample and the ECG with PVC is taken as a test
sample. The classification is done using WCS
parameters. An iterative based mathematical formula
but not an optimal one is used to extract the
parameters from WCS and WCOS.
III.
CROSS WAVELET TRANSFORM(XWT)
The two time series function x(n) and y(n)when it
is applied with Cross Wavelet Transform it can be
defined as
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International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014
---- (1)
where * denotes the complex conjugate. the cross
power wavelet can be defined as the modulus of the
above equation. the local relative phase between the
two time series function x(n) and y(n) can be found
from the argument of equation (1),
--- (2)
--- (3)
The cross wavelet power can be obtained
from the above equations and the coherence of two
time series can be defined from the XWT in its time
frequency space[18]. The WCOS of two time series
can be defined as
--- (4)
Here S is the smoothing operator and ‘s’ is a scale and
it is
IV.
MATERIAL
ROUTINE
AND
PROGRESSIVE
shown in fig-2 the WCS and WCOS are obtained
from the XWT of reference and sample signal. These
WCS and WCOS are analyzed to detect the presence
of PVC. The mother wavelet used here is Daubechies
wavelet since there is an existence of similarity
between morphological structure and QRS complex.
The time scale relationship between the two signals
can be obtained from XWT. For the analysis 512
scales are considered. The region of interest(ROI)
spectral component can be obtained from the resultant
WCS. The morphological similarity in QRS complex
and time difference can be easily extracted from this
analysis.
V.
PARAMETER ESTIMATION
The morphological features are registered
from the WCS and WCOS matrices which are
obtained from the resultant XWT. This section deals
with two procedures for parameter estimation.
Reference and sample data
(ECG signal from MIT-BIH
database)
Denoising
1. Data
All the ECG signal used in this paper has
been selected from MIT-BIH Arrhythmia database,
which was created in 1980 as a standard reference for
arrhythmia detector[19]. The database is comprised of
48 files each containing 30-minutes ECG segments
selected from 24 hours recording of 47 different
patients. The lead V1 or ML1 is used here for
analysis.
R-Peak detection
(Segmentation)
Normalization with respect to
Interpolation
2. Denoising And R-Peak Registration.
In order to increase the efficiency of the
algorithm the ECG signal must be denoised before it
is subjected to further analysis. The basic DWT based
denoising is used in this paper and the mother wavelet
used here is Daubechies wavelet and the feature
extraction is carried out by the method developed in
[20].
Reference
Sample
CWT
3. Normalization And Interpolation.
CWT
XWT
The R-peak has to be registered first, once it
has been registered then it must be normalised with
respect to time. In order to get the equal number of
beat rate in one second it must be interpolated based
on FFT technique. The interpolation has to done
because in the sample data the heat beat rate will vary
from one cardiac cycle to another.
WCS
WCOS
Parameter Estimation
4. XWT Analysis On ECG Beat.
The similarity between two signals can be
found out by cross correlation. The various characters
of the test sample are studied and implemented by
applying CWT to the reference and test sample. As
ISSN: 2249-2615
Test
Sample
Classification & Result
Fig-2 Flow chart for the progressive algorithm
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International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014
5.1. Selection Of Scales
--- (9)
This selection is done from the WCS. It is
done to know the tangible duration of the significant
analysis for further analysis to establish a time scale
relationship. This can be expressed as
--- 10)
The comparison list is stated in the below table
--- (5)
The variable Scales(S) represents the total
WCS value at each scale over the entire ECG beat.
The parameter is extracted in the P-Wave region and
QRS complex region.
5.2. Extraction Of Parameters
This extraction is based on the WCS and
WCOS matrices from the selected range of scales s1
to s2 over P-Region and QRS complex region. the
parameter can be extracted from the following
relations
--- (6)
--- (7)
The equation (6) represents the sum of WCS
and equation (7) represents the sum of WCOS.
Different sets of parameters are concidered with
respect to R1 and R2, and then the analysis is done on
the sample signal obtained from the lead ML2.
VI.
Exposure
Scheme
suggeste
d by
Time
domain
Statistics
Moody et
al [6]
-----
91.0
74.51
in
%
89.46
97.36
PCA
Moody et
al [7]
---
Hidden
Markov
model
Cheng et
al[9]
Andreao
et al[11]
Alfonso
et al[14]
my self
93%
93.12
92.73
92.85
94.76
96.92
95.26
94%
99.79
99.76
---
99.59
99.56
98%
97.3
98.8
Filter
Bank
Cross
Wavelet
Recital Rate
in %
Table-1 comparison of accuracy with existing method.
CLASSIFICATION
The classification for the presence or absence
of the PVC can be done by checking the occurrence of
P-Region and the type of PVC can be classified by
setting a threshold point. Different ways are available
for the selection of threshold point but in this paper
centroid points are chosen from the WCS and WCOS
matrices. The classification algorithm developed here
is the combination of threshold point with the
Scales(R1) and Scales(R2). The accuracy which is
obtained from this analysis is 98% and the tool used
for simulation is MATLAB.
VII.
SIMULATION RESULTS
The sample signal is taken from MIT-BIH
Arrhythmia database and the results are compared
with existing methods.
Accuracy - It is the major parameter in obtaining the
overall success of the proposed method. This can be
defined as
Fig-3 Comparision chart for accuracy and statistical
measures with the existing method. The lowest accuracy
value is taken as 85% for plotting the graph.
--- (8)
VIII.
Where NB denotes the total number of beats and 'e'
denotes the total number of classification errors.
Sensitivity And Specificity - These are the statistical
measures of the performance of a binary classification
test, also known as statistics as classification function.
ISSN: 2249-2615
CONCLUSION
In this paper the presence of PVC can be
spotted out easily and efficiently using XWT and
examining the CWT of both reference and sample
signal. A Daubechies wavelet is used as a mother
wavelet for initial analysis and further analysis are
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International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014
taken with respect to WCS and WCOS spectrogram of
normal and test data. the classification accuracy of
this method is 98% this result paves the way for
further analysis to be taken place with 12 lead type
and to spot the other type of CVD's.
[11]
[12]
[13]
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