International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Ventricular Fibrillation Detection using Empirical Mode
Decomposition and Approximate Entropy
Lakhvir Kaur1, Vikramjit Singh2
1
Research Scholar, 2Assistant Professor, Department of Electronics & Communication, Lovely Professional University, Punjab,
India.
On the other hand, death often occurs if normal sinus
rhythm is not restore within 90 seconds of the onset of VF,
especially if it has degenerated further into asystole.
The most common cause of VF is a heart attack.
However, VF can occur whenever the heart muscle does
not get enough oxygen. VF is treated by delivering a quick
electric shock through the chest using a device called an
external defibrillator. The electric shock can immediately
restore the heartbeat to a normal rhythm, and should be
done as quickly as possible. Sometimes it is not known
what causes ventricular tachycardia, especially when it
occurs in young people. But in most cases ventricular
tachycardia is caused by heart disease, such as a previous
heart attack, a congenital heart defect, hypertrophic or
dilated cardiomyopathy, or myocarditis. Sometimes
ventricular tachycardia occurs after heart surgery.
Both ventricular tachycardia and ventricular fibrillation
are usually life-threatening arrhythmias. Rapid heart rates
of the lower heart chambers prevent them from having
adequate time to fill with blood. As a result, the heart does
not pump effectively; the heart muscle, brain, and other
parts of the body do not get adequate blood supply, which
can result in fainting and even loss of life.
Both ventricular tachycardia and ventricular fibrillation
should be identified accurately. As VF is treated by high
energy electric shock and VT is treated by low energy
electric shock, so these should not be misidentified. If VF
is misidentified as VT, low energy shock can’t make heart
return to the normal state which may lead a fatal result.
Similarly if VT is misidentified as VF, high energy electric
shock may damage the heart. So accurate detection is
needed to prevent heart damage.[1]
Many techniques have been used for discriminating
ventricular fibrillation and ventricular tachycardia. These
were threshold crossing intervals (TCI) algorithm [2];
correlation and autocorrelation function based methods
differentiate VT and VF by quantifying regularity
parameters [3-4]; VF filter techniques calculate VF-filter
leakage as discriminator relying on the VF signal
approximating a sinusoidal waveform [5]; wavelet based
algorithms [6], and chaotic features [7].
Abstract— Efficient detection of ventricular fibrillation is
very important for clinical purposes as it is the most serious
cardiac rhythm disturbance that can be life threatening. This
paper presents a new method for detection of Ventricular
fibrillation by discriminating it with Ventricular tachycardia
using empirical mode decomposition (EMD) and Approximate
Entropy. First Intrinsic mode functions (IMFs) of each ECG
signal is used to distinct between them by calculating their
approximate Entropy. We have used MIT/BIH database to
validate the efficiency of our method. Simulations were
carried out in MATLAB environment. The result shows that
this method gives good result as accuracy of 91% is achieved
for detection of Ventricular fibrillation.
Keywords— Accuracy, Approximate Entropy, Empirical
mode decomposition, Ventricular fibrillation, Ventricular
tachycardia.
I. INTRODUCTION
Ventricular fibrillation (VF) is the most serious cardiac
rhythm disturbance that can be life threatening. VF is
characterized by uncoordinated contraction of the cardiac
muscle of ventricles in the heart, making them quiver rather
than contract properly and heart can’t pump any blood,
causing cardiac arrest. Ventricular tachycardia (VT) is a
rapid heart rhythm with pulse rate of more than 100 beats
per minute, with at least three irregular heartbeats in a row,
that starts in the lower part of the heart (ventricles). The
ventricles are the main pumping chambers of the heart.
This is potentially life threatening arrhythmia because it
may lead to ventricular fibrillation, asystole, and sudden
death. Ventricular fibrillation is a medical emergency that
must be treated immediately to save a person’s life. If this
arrhythmia continues for more than a few seconds, it will
likely degenerate further into asystole. This condition
results in cardiogenic shock and cessation of effective
blood circulation. As a consequence, sudden cardiac death
(SCD) will result in a matter of minutes. If the patient is
not revived after a sufficient period (within roughly 5
minutes at room temperature), the patient could sustain
irreversible brain damage and possibly become brain dead
due to the effects of cerebral hypoxia.
260
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
In this paper Empirical mode decomposition (EMD), a
new technique and approximate entropy for detection of
ventricular fibrillation by discriminating between
ventricular tachycardia and ventricular fibrillation is used.
As the frequency components of ECG vary during different
arrhythmias so EMD technique is used which provides the
time frequency distribution of biomedical signals which are
non-stationary and non linear and thus provides frequency
information.
All these signals are sampled with 250Hz, 12 bit. We
have used 42 VF and 32 VT database.
B. Preprocessing
As the signals are corrupted by noise, we used a 5th
order Butterworth band pass filter to remove the noise from
the signals.
C. VF Detection Method
To discriminate between VF and VT first decompose the
signal into IMFs using Empirical mode decomposition
(EMD). Figure 1 and 2 shows the original VF and VT
signal. Then these are filtered by Butterworth band pass
filter to remove the noise and it is shown in Figure 3 and 4
with their frequency spectrum.
II. METHODOLOGY
A. Database
For experimental study we have used the data from
MIT/BIH
Creighton
University
Ventricular
Tachyarrhythmia Database (MIT cudb), MIT/BIH
Malignant Ventricular Ecotype Database (MIT-BIH vfdb).
Figure 1 : Original VF signal
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Figure 2 : Original VT signal
Figure 3 : VF Signal after denoisining with its spectrum
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Figure 4 : VT Signal after denoisining with its spectrum
Xmi ={u(i),u(i+1),..., u(i+m-1)}, i=1,.. ,N-m+1
After removing noise from the signal, decompose the
signal into IMFs. First IMF is used to discriminate between
VT and VF. Figure 5 and 6 shows the VF and VT signals
decomposed into IMFs.
Approximate Entropy parameter is used to classify VF
and VT signals. Approximate Entropy of first IMF is
calculated for all the signals.
Approximate entropy (ApEn) was introduced by
Pincus (1991) to quantify the rate of generation of new
information in a time series. ApEn measures the
complexity or irregularity of signal and it can be applied to
typically short and noisy time series of clinical data. A
high value of the ApEn indicates high irregularity and
randomness , where as low value indicates that the time
series is deterministic and more regular.
ApEn can be computed as follows: [8]
For an N sample time series {u(i):1≤i≤N}, given m,
form vector sequences Xm1 through XmN-m+1 as
Where m is the length of compared window.
For each i≤N−m+1, let Cmi(r) be (N−m+1)-1 times the
number of vectors Xmj within r of Xmi. By defining
ϕm(r) = (N − m + 1)-1
Cmi(r)
Where ln is the natural logarithm, Pincus defined the
parameter:
ApEn(m, r) =
[ϕm(r) – ϕm+1(r)]
Figure 7 and 8 shows the first IMF of VF and VT signals
and their spectrum. It is clear from these that first IMF of
VF includes frequency component with high amplitude.
But VT includes low amplitude and high frequency
component.
263
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Figure 5 : IMFs of VF signal
Figure 6 : IMFs of VT signal
264
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Figure 7 : First IMF of VF signal with its spectrum
Figure 8: First IMF of VT signal with its spectrum
265
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
These are calculated as follows.
Sensitivity is the ability to detect VF or VT. It is given
by [2]
III. EXPERIMENTAL RESULTS
To evaluate the performance of detection method
approximate entropy values are calculated for every signal
of VF and VT, and then threshold is set to 0.25 to
differentiate between VF and VT signals. Table 1 shows
the classification results that out of 42 VF episodes 38 are
identified correctly and 29 out of 32 VT episodes are
identified correctly. According to this three parameters are
calculated to evaluate the performance of the detection
method that are Sensitivity, specificity and accuracy as
shown in table 1.
=
Where TP is number of true positive decision FN is the
number of false negative decision
Specificity is the probability to identify ―no VF‖
correctly and it is given by
=
Table I
Classification Results with ApEn
Type
VF
VT
Sensitivity
Specificity
Accuracy
VF
38
4
0.9047
0.9166
0.9117
VT
3
29
0.9062
0.9111
0.9080
total
-
-
0.9054
0.9134
0.9098
Where TN is number of true negative decision FP is the
number of false positive decision
Accuracy is the probability to obtain a correct decision
and it is given by
=
Figure 9 : Classification with ApEn (in red VF , in blue VT)
266
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013)
Figure 10 : Time-Frequency-Energy Plot of VF
Figure 9 shows the classification results with ApEn,
threshold is set at 0.25 which separates the VF (in red) and
VT (in blue) results. It is seen that out of 42 VF episodes
38 are identified correctly and 4 are identified as VT. Out
of 32 VT episodes 29 are identified correctly and 3 are
identified as VF.
According to Approximate Entropy high value indicates
high irregularity and randomness, where as low value
indicates that the time series is deterministic and more
regular. In our results we found high values for VF and low
values for VT. So VF is more irregular than VT. It is also
clear from the time-frequency-energy plot of VF and VT.
Figure 11 : Time-Frequency-Energy Plot of VT
267
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[2]
Figure 10 and 11 shows the time-frequency-energy plot
of VF and VT. It is clear from the amplitude of VF and VT
that VT is more periodic than VF. So VF is more irregular
and VT is more regular than VF.
[3]
IV. CONCLUSION
Empirical mode decomposition based Ventricular
fibrillation detection was proposed. Approximate entropy
(ApEn) of the first IMFs of each signal is used as
discriminator. According to ApEn VF is the most irregular
rhythm with high value of ApEn and VT is more regular
than VF with low value of ApEn. This method shows good
results as we obtain the accuracy of 91% for VF and 90%
for VT detection.
[4]
[5]
[6]
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