Cardio Logical Signal Processing for Arrhythmia Detection with Comparative Analysis of Q-Factor

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
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Cardio logical Signal Processing for Arrhythmia Detection with
Comparative Analysis of Q-Factor
Ms. Sulata Bhandari1, Dr. Sandeep Kaur2, Rohit Gupta3
1 Asst. professor, PEC University of technology, Chandigarh, India
2 Asst. professor, PEC University of technology, Chandigarh, India
3 ME scholar, PEC University of technology, Chandigarh, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Electrocardiogram is a graph which measures
the electrical activity of the heart. Normal heart beat for
human is 70 cycles per minute. Any change in natural
sequence of activities of heart e.g. beating too fast, tooslow or
erratically is Arrhythmia, and this can be detected by
analyzing ECG of the subject. The recorded ECG potentials are
usually contaminated by power-line frequencies, which lie
within the frequency spectrum ofECG signalmaking itdifficult
to extract useful information from it, this interference is
suppressed using 50/60Hz notch filter. . ECG signals first
filtered by IIR notch, to remove the power line artifacts. It has
been shown that notch filter application deforms the QRS
complex of the electrocardiogram. Inthispaperacomparative
analysis of the impact for different values of Q-factor of the
notch filter, on QRS complex of Electrocardiogram has been
shown. After filtering, QRS complex of an ECG signalidentified.
For detection of QRS complex DOM (difference operation
method) is used. After successfullydetectionofQRScomplexits
R-peak, sharpness, slope and duration is calculated. For the
classification purpose linear classifier isused, inwhichtheECG
data were divided into two partition-one for trained the data
called training set in which we used 75% data to trained the
classifier and another for test the data called test set in which
we used 25% data to test the classifier and classify the normal
and arrhythmia signals.
Key Words: Electrocardiogram (ECG), Difference operation
method (DOM), QRS complex, Arrhythmia.
1. INTRODUCTION
Electrocardiogram(ECG)istherecordoftheheartmuscle
electric impulse. ECG machine is a device through which we
record this electrical activity. This device is connected by
wires to electrodes pasted on patient’s chests at particular
position [1].. Around 12 million deaths occur worldwide
each year due to cardiovascular diseases as stated by the
World Health Organization. Due to the insufficient supply of
blood in the heart the clogging occurs and thus Coronary
Heart Disease ( CHD) takes place [2][3][4]. The cardiac
arrhythmias accounts for ninety percent ofthedeathsdue to
cardiovascular diseases [5]. Arrhythmias are seen as an
abnormal function of the heart.
There have been several researches in the field of
arrhythmia detection. Adams and Choi [5] proposed a
method based on ANN toclassifydifferentarrhythmiasusing
the QRS complex as features of ECG. Anotherneural network
based classification of ECG for Premature Ventricular
Contractions using Wavelet transformwasdonebyInanetal
[6] with an accuracy of 88%. Patel et al [7] proposed
arrhythmia detection method based on peak detection of
QRS complex. They concluded that QR S complex is an
important feature for classification of arrhythmia. Rahman
and Nasor [8] used the QRS complex to define and classify
different types of arrhythmia. Li, Zheng an d Tai [9] detected
ECG characteristic points using wavelet transforms for the
detection of QRS, T, and P waves[43].
Fig.- 1: Schematic representation of normal ECG
waveform
Table -1: Amplitude and duration of waves,
intervals and segments of ECG signal.

ECG signal consists of a P wave, a QRS complex, and a T
wave. Before contraction the electric currents due to atrial
depolarization causes P ,while the depolarization due to
ventricle contraction causes QRS complex. During recovery
of the ventricles from the state of depolarization the T
wave is formed.
The T wave is characterized as the wave of repolarization.
The Figure 1 shows a representation of an ECG with the
waves[10].
2. NOISE IN ECG SIGNAL
Unfortunately the acquired ECG does not only consist of
the components derived from the electrical functionality of
the heart, but it is very often contaminated by artifacts that
can interfere or interrupt the signal and result in a loss of
information. Sometimes, these artifacts might even present
with similar morphology as the ECG[10] . The most
commonly found artifacts in the ECG are:
1. Powerline interference, which is characterized by a
frequency of 50 or 60 Hz depending on the country.
2. Steep voltage changes form the loss of contact between
the electrodes and the skin.
3. Electrical activity from muscle contractions that varies
from dc to10kHz.
4. Baseline drift which is usually causedfromrespirationat
very low frequencies, around 0.1v 0.3Hz.
3. POWER LINE INTERFERENCES
In this paper we will deal with Power line interferences
that contains 60 Hz pickup (in U.S.) or 50 Hz pickup (in
India) because of improper grounding.
Fig-2: 60 Hz Power line interference.
It is indicated as an impulse or spike at 60 Hz/50 Hz
harmonics, and will appear as additional spikes at integral
multiples of the fundamental frequency. Its frequency
content is 60 Hz/50 Hz and its harmonics, amplitude isupto
50 percent of peak-to-peak ECG signal amplitude. A 60 Hz
notch filter can be used remove the power line
interferences[10].
4. DATA USED
For this study, the data used is publicly available MIT-BIH
arrhythmia database at physionet.org [12] The complete
data set consist of 48 sets each containing set of 2 channels.
Each set is divided into normal and arrhythmia set. The
length of each recording is 27.7 seconds. The sampling rate
of data is 360 Hz. In most records, the upper signal is a
modified limb lead II (MLII), obtained by placing the
electrodes on the chest. The lower signal is usually a
modified lead V1 (occasionally V2 or V5, and in one instance
V4); as for the upper signal, the electrodes are alsoplacedon
the chest [13].
5. NOTCH FILTER
The Notch Filter, (BSF) is a frequency selective circuit that
functions oppositeway to theBandPassFilter.Thebandstop
filter,alsoknown asa band rejectfilter,passesallfrequencies
with the exception of those within a specified stop band
which are greatly attenuated.
If this stop band is very narrow and highly attenuated over a
few hertz, then the band stop filter is more commonly
referred to as a notch filter, as its frequency response shows
that of a deep notch with highselectivity (a steep-sidecurve)
rather than a flattened wider band.
Also, just like the band pass filter, the band stop (band reject
or notch) filter is a second-order (two-pole) filter havingtwo
cut-off frequencies, commonly known as the -3dB or half-
power points producing a wide stop band bandwidth
between these two -3dB points.
Then the function of a band stop filter is to pass all those
frequencies from zero (DC) up to its first (lower) cut-off
frequency point ƒL, and pass all those frequencies above its
second (upper) cut-off frequency ƒH, but block or reject all
those frequencies in-between. Then the filters bandwidth,
BW is defined as: (ƒH – ƒL)[15].
6. NOTCH FILTER DESIGNING IN MATLAB
[NUM,DEN] = IIRNOTCH(o,BW) designs a second-order
notch digital filter with the notch at frequency o and a
bandwidth of BW at the -3 dB level.
The bandwidth BW is related to the Q-factor of a filter by
BW = o/Q.
7. METHODOLOGY TO CLASSIFY ARRHYTHMIC
SIGNALS
7.1 Pre-processing
ECG signal mainly contain different types of noises or
artifacts which distorts the original signal that creates the

problems to extract the features from ECG signal so in this
step we basically remove the noise from ECG signals. There
are various kinds of artifacts similar to electrode contact
artifacts, musclesartifacts, power lineinterferenceandsoon.
so basically to remove all these noises we designed a filter
called IIR notch filter with sampling frequency of 360hz and
cut-off frequency of 60hz.So that we can extract the features
from ECG signals more precisely.
7.2 R-peak detection
After then pre-processing step, now we detected the R-peak
of an ECG signal which is most important peak of an ECG
signal. Any variation in R-peak can cause heart related
problem like arrhythmia or any kindofdiseases.Wedetected
the R-peak by means of thresholding. Signal isthresholdedat
suitable level such that peak other than QRS complex’s will
suppressed at threshold level so by this we only got R-peak
of an ECG signal. We set the threshold level at 150.
7.3 QRS detection
Now, After effectively recognition of R peak now we have to
find out the QRS complex of an electrocardiogram signal.
Magnitude of this complex is very large ascompared toother
waves. Its size vary from person to person, age and gender
etc. QRS complex is detected by using DOM (difference
operation method).In DOM ,it basically contains two parts
thresholding and subtraction. In last step we have done
thresholding now the threshold signal is subtracted by itself
such that each samples value is subtracted from its
predecessor sample value. This method is equivalent to
differentiation of a signalwhichisusedtochecktheslopeand
on differentiation the values at threshold level will give zero
slopes. And the values before peak will give positive slope
and after the peak give negative slope. Thus the point where
slope changes for the first time will give the R peak.
Difference operation method is used to get the Q and S peak.
Fig.-3: QRS complex of ECG signal
7.4 Features extraction
The signal was initially filtered to remove all the noise
present the signal after that ECG signal is analyzed by
detecting QRS complex, R-peak, Q and S peak. Because these
are used to calculate the features like slope of curve RS and
QR, sharpness of the peak, amplitude of peak and duration of
the QRS complex. A matrix was construct of 1x5 which
consists of amplitude of R-peak, QRS complex duration,
slopes and sharpness of QRS complex. Fig. 5 shows the
features of ECG signals.
Fig.-4: Block diagram for features extraction and
classification
Fig.-5 : Features of ECG signal
To retain consistency 12 peakschosen indiscriminatelyfrom
every subject for study. So it gave us a result in the form of
12x5matrix regarding to the 12 QRS peaks of an ECG signal.
All the calculation and programming done by using MATLAB
software. All the features explained below:
7.4.1 Slope
Analysis of QRS complex helps us to calculate the slopes
(Slope QR) between Q and R and (Slope RS) R and S. It helps
us to find out the arrhythmia because the slope of normal
signal is different from arrhythmic signals. So the normal

signal has less slope as compared to abnormal signal. The
slope is calculated as follow:
Slope QR =
Slope RS =
Here, D1 is the difference between Q and R and D2 is the
difference between R and S and Q, R and S are the peaks of a
QRS complex.
7.4.2 Sharpness
It also helps to classify the normal andabnormalECGsignalit
gives the sharpness of a peak. It determines the quality of a
peak.
Sharpness= slope QR - slope RS
7.4.3 Duration
Duration of an QRS complex leads to distinguish between
normal and abnormal signal.The timeintervalbetweentheQ
and R in the QRS complex is known as the duration of QRS
complex. It is denoted by ‘dur’ in fig. 5.
7.5 Classification
In this last step for the classification purpose we used linear
classifier. A linear classifier achieves this by making a
classification decision based on the value of a linear
combination of thecharacteristics.Anobject'scharacteristics
are also known as feature values and are typically presented
to the machine in a vector called a feature vector
Fig-.6: Linear classifier
In this case, the solid and empty dots can be correctly
classified by any number of linear classifiers. H1 (blue)
classifies them correctly, as does H2 (red). H2 could be
considered "better" in the sense that it is also furthest from
both groups. H3 (green) fails to correctly classify the dots.
8. RESULTS
The extracted features gave as a input to the classifier.
Efficiency of features and linear classifier combination for
automatic arrhythmia detection has been tested.
Classification of 8 subjects out of the 48 subjects taken
indiscriminately forthisresearch.ECGdataweredividedinto
two partition-one for trained the data called training set in
which weused 75% data to trained the classifierandanother
for test the data called test set in which we used 25% data to
test the classifier and classify the normal and arrhythmia
signals. Now we obtained the data after classification in the
form of 2 set i.e. Set_N and Set_A of 12x5 matrix. After that in
multi fold cross validation method matrix of 9 x 5(75%) is
taken for training and matrix of 3 x 5 (25%) is taken for
testing. Now the entire data isdividedintofouriterationsand
results obtained were averaged.Classification’sperformance
of classifier is measured with the help of confusion matrix
define below:
Confusion matrix =
Classifications outcomesareexplainedinthreeterminologies
specificity, sensitivity and accuracy defined as below:
Specificity =
Sensitivity =
Accuracy =
Here, True positive means the normal signals classified by
classifier as normal signals. True negative means the
abnormal signals classified by the classifier as abnormal
signals. False negative means abnormal signals wrongly
classified by the classifier asan normal signals.Falsepositive
means normal signals wrongly classified by the classifier as
abnormal signals.
Now here we have taken one subject for classification
purpose.
Removing 60Hz frequency Component
For Q=2

Fig-7: FFT spectrum of filtered and unfiltered ecg
Signal with Q=2
R,Q,S peaks for Q=2
Fig-8: Normal ECG signal with location of Q points, R
peaks and S points
Fig-9: Arrhythmic ECG signal with location of Q points, R
peaks and S points
Table -2: Accuracy, Sensitivity and Specificity of
classifier in case of Q=2.
For Q=35
Fig-10 FFT spectrum of filtered and unfiltered ecg
Signal with Q=35
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
-100
-50
0
50
100
150
200
250
300
ECG signal with location of R,Q and S point
time
amplitude(mv)
Fig-11: Normal ECG signal with location of Q points, R
peaks and S points with Q =35

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
-50
0
50
100
150
200
250
300
ECG signal with location of R,Q and S point
time
amplitude(mv)
Fig-12: Arrhythmic ECG signal with location of Q points, R
peaks and S points with Q=35
Comparison of notch filtered output of Q=35 and Q=15
Fig.7 FFT spectrum Comparison of filtered ecg
Signal with Q=15 and 60
Table -3 Results showing Index no. of R-peak for Q-
factor=2 and Q-factor=35
Table -4 Results showing percentage change in amplitude
of R-peak for Q-factor=2 and Q-factor=35
9. CONCLUSIONS
For the filtering of power line frequency (60Hz for U.S.A and
50Hz for India ) designing of notch filter is important step.
Only after this we can extract various features from ECG
signal i.e. R-R interval, Q and S peak, R peak etc. For notch
filter designing the value of Quality factor is crucial .The Fast
Fourier Transform spectrum of filtered and unfiltered signal
with different values of Q is shown above. As seen from the
results forlow value of Q deformation of frequencyspectrum
is more as compared to the higher Q-factor. All the plots
above reflects that for all values of Q there is significant
suppression of 60 Hz component. Results shown in table1
and 2 clearly indicates towards the deformation of QRS
complex due to the notch filter used for suppression of
power line frequency. Thus concluded thatdeciding Q-factor
forthe notch filterplaysimportantroleinitsimplementation.
However the sensitivity accuracy and specificity remains
same in both the cases.
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