Analysis electrocardiogram signal using ensemble empirical mode decomposition and time

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
275
ANALYSIS ELECTROCARDIOGRAM SIGNAL USING ENSEMBLE
EMPIRICAL MODE DECOMPOSITION AND TIME-FREQUENCY
TECHNIQUES
Samir Elouaham1
, Rachid Latif1
, Boujemaa Nassiri1
, Azzedine Dliou1
, Mostafa
Laaboubi2
, Fadel Maoulainine3
1
(ESSI, National School of Applied Sciences, Ibn Zohr University Agadir, Morocco)
2
(High School of technology, Ibn Zohr University Guelmim, Morocco)
3
(Team of Child, Health and Development, CHU, Faculty of Medicine, Cadi Ayyad
University, Marrakech, Morocco)
ABSTRACT
Electrocardiogram signals (ECG) are among the most important sources of diagnostic
information in healthcare. During ECG measurement, there may be various noises which interfere
with the ECG information identification that cause a misinterpretation of the ECG signal. In this
paper, the Empirical Mode Decomposition (EMD), the Ensemble Empirical Mode
Decomposition (EEMD) and the Discrete Wavelet Transform (DWT) were used to overcome
these problems. These techniques are applied to a noisy electrocardiogram abnormal signal
obtained by adding white noise. A comparative performance study of these three techniques in
terms of several standard metrics was used. The EEMD was chosen for its better localization of
the components of the ECG signal. The non-stationary and non-linear nature of the ECG signals
makes the use of time-frequency techniques inevitable. The parametric and non-parametric time–
frequency techniques allow giving simultaneous interpretation of the non-stationary signal in both
time and frequency which allows local, transient or intermittent components to be elucidated. In
this paper, the parametric techniques used are periodogram (PE), capon (CA) and time-varying
autoregressive (TVAR) and non-parametric techniques used are Smoothed Pseudo Affine Wigner
Distributions (SPAWD) and S-transform (ST). The abnormal signal used is obtained from the
patient with an atrial fibrillation. The PE technique shows its superior performance, in terms of
resolution and interference-terms suppressing, as compared to other time-frequency techniques
used in this paper. From the obtained results, the EEMD technique is a more powerful tool for
elimination and restoration of the original signal than the other techniques used in this paper. This
study shows that the combination of the EEMD and the Periodogram techniques are a good issue
in the biomedical field.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 2, March – April (2013), pp. 275-289
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Keywords: EEMD, Time-frequency, PE, Non-stationary, ECG signals.
INTRODUCTION
Electrocardiography is a commonly used, noninvasive procedure for recording
electrical changes in the heart. The record, which is called an electrocardiogram (ECG),
shows the series of waves that relate to the electrical impulses which occur during each beat
of the heart. The waves in a normal record are named P, QRS complex, and T and follow in
alphabetical order. The figure 1 shows the normal ECG signal [1-3]:
Fig.1: A waves of the normal ECG signal
The ECG signal is often corrupted by various noises such as prolonged
repolarization, changes of electrode position, muscle contraction (electromyography) and
power line interference (electrode) [3]. Among the objectives of this study is to separate the
signal component from the undesired artifacts. These artifacts can’t facilitate a good, easy
and accurate interpretation of the ECG signals that implies bad diagnostic given by the
expert. Therefore the elimination of noises is inevitable. To overcome this problem, several
techniques are presented whose goal is the cancellation of the noises existing during the
recordings of the biomedical signals. The techniques such as elliptic filter, median filter,
Wiener filter, Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD)
and Ensemble Empirical Mode Decomposition (EEMD) are the solutions proposed [4-12].
The DWT, EMD and EEMD techniques are used in several fields as acoustic, climatic and
biomedical; these techniques give good results. The disadvantage of elliptic filter, median
filter, Wiener filter is that they eliminate the high frequency components of ECG signals.
The drawback of DWT is its non-adaptive basis due to the selection process of the basis
function that is controlled by the signal components that are relatively large in a frequency
band [10, 12]. And the disadvantage of Empirical Mode Decomposition (EMD) is the
appearance of mode-mixing effect in signal restoration [4-6]. To overcome these problems
the new technique called Ensemble Empirical Mode Decomposition (EEMD) is proposed by
Wu and Huang [7]. The choice of a powerful technique among of these techniques is related
to the result obtained after denoising ECG signals; these results are the original characteristic
waveforms such as QRS complexes, the P and T waves and also Q, R and S waves. A mean
square error (MSE) and percent root mean square difference (PRD) between filter ECG
output and clean ECG were used for giving the performance of the denoising techniques
used. The results obtained by the EEMD show high resolution, noise cancellation and
preservation of true waveforms of ECG signals more than the other techniques as EMD and
DWT. Among the objectives presented in this paper is the choice of the useful technique for
any application that needs the denoising of non-stationary and non-linear biomedical signals
such as ECG, EEG, EOG and EMG in the pretreatment stage.

277
The biomedical signals such as electromyography (EMG), electroencephalogram
(EEG) and electrocardiogram (ECG) are non linear and non stationary. Due to multi-
component signals, expert can’t give a good diagnostic. There are several techniques to
analyze the ECG signals. Traditionally time-domain is based on the measurement of the
surface QRS and the amplitude of the P, the QRS complexes and the T waves. The
disadvantage of this technique is that it cannot give the useful information of frequency
components in the time domain [13]. To overcome this problem the Fast Fourier Transforms
(FFT) is presented for giving a frequency-domain representation of a signal where the
analysis can identify the change of the frequency components of the abnormal ECG signals
[14]. This technique also has a disadvantage; the frequency component is not revealed vary
with time. The FFT technique is unsuitable. To tackle these limitations, the time-frequency
techniques are useful [15- 30]. In recent years the time-frequency methods constitute an
important amelioration in signals analysis specifically in the biomedical signals. These
techniques are Wavelets, Wigner-Ville, Choi-Williams and Born-Jordan [28]. In this paper
we present the parametric techniques such as periodogram (PE) capon (CA) and time varying
autoregressive (TVAR) and non parametric as S-transform (ST) and smoothed pseudo affine
Wigner distributions (SPAWD) [15-20]. The drawback of the SPAWD is the appearance of
the cross-terms. These cross-terms hide useful information of interest in the signal that can
help the expert to extract real features. The features obtained from ST are not completely
distinctive and don’t give clear information about the component of the signal. These
disadvantages are resolved by the PE time-frequency technique. The performance of these
techniques is given by the calculation of the variance obtained by adding the noise of the
modulated signal. The results show that the periodogram technique is more powerful than the
other techniques. The electrocardiogram signals used are normal and abnormal. The
abnormal cardiac signal was taken from a patient with atrial fibrillation [31].
This paper is organized as follows: Section II talks about the denoising techniques,
parametric and non parametric time-frequency techniques. Section III provides normal and
abnormal electrocardiogram signals. The obtained results of the denoising techniques are
given in section IV. The section V gives the results of time-frequency techniques used.
Finally, conclusion is provided in section VI.
1. TECHNIQUES USED
1.1 Denoising techniques
1.1.1 EMD
The EMD was proposed by Huang and al. as a tool to adaptively decompose a signal
into a collection of AM–FM components [4-6]. The EMD method has no mathematical
foundations and analytical expressions for the theoretical study. The various works have
successfully used the EMD to real data in several fields such as biomedicine, study of climate
phenomena, seismology or acoustics [4-6]. These studies show satisfaction and matching
condition used in non-stationary signal processing. The EMD decomposes adaptively a non-
stationary signal into a sum of functions oscillatory band-limited d(t) called Intrinsic Mode
Functions IMFJ(t). These functions IMFJ(t) oscillate around zero and can express the signal
x(t) by the expression:

278
1
( ) ( ) ( )
k
j
j
x t d t r t
=
= +∑ (1)
Where r(t) is the residue of low frequency.
Each IMFJ(t) must satisfy two conditions:
- The number of zero crossings and the number of extreme signal must be equal throughout
the analyzed signal,
- At any point, the average of the envelopes defined by local extreme of the signal must be 0.
The higher order IMFJ(t) corresponds to low oscillation components, while lower-order
IMFJ(t) represents fast oscillations. For different decomposed signals the number of IMFJ(t)
is variable. It also depends on the spectral content of the signal. The Rilling study presents the
technical aspects of the EMD implementation and makes the five-step algorithm given by the
following [4-6]:
a) Extract the extreme of the signal x(t),
b) Deduce an upper envelope emax (t) (resp. lower emin (t)) by interpolation of the maxima
(resp. minima),
c) Define a local average m(t) as the sum of the half-envelopes by the expression:
max min( ) ( ( ) ( ))/2m t e t e t= + (2)
d) Deduce a local detail dJ(t)=IMFJ(t) by the expression:
( ) ( ) ( )d t x t m t= − (3)
e) The iteration is given by the expression (1).
The first IMF contains the terms of higher frequencies and contains the following terms of
decreasing frequency up to forward only a residue of low frequency.
1.1.2 EEMD
The ensemble EMD method has been proposed to overcome mode mixing problem
existing in EMD technique [7]. The EMD technique allows giving all solutions that give the
true IMF by repeating the decomposition processes. The procedure of the EEMD method is
given as follow:
Step 1: Add white noise with predefined noise amplitude to the signal to be analyzed.
Step 2: Use the EMD method to decompose the newly generated signal.
Step 3: Repeat the above signal decomposition with different white noise, in which the
amplitude of the added white noise is fixed.
Step 4: Calculate the ensemble means of the decomposition results as final results.
The signal x(k) is decomposed into a finite number of intrinsic mode functions (IMFs) and a
residue.
1
( )
n
i
i
x k c r
=
= +∑
) )
(4)
Where n represents the number of the IMFs, ic
)
is the ith
IMF that is the ensemble mean of the
corresponding IMF obtained from all of the decomposition processes and r
)
is the mean of
the residues from all of the decomposition processes.

279
1.1.3 Discrete Wavelet Transform
Wavelet theory appeared in early 1990 [10-12]. It affects many areas of mathematics,
particularly signal processing and image. The multi-resolution analysis provides a set of
approximation signals and details of a start signal. In discrete wavelet transform (DWT), for
analyzing both the low and high frequency components in x(n), it is passed through a series of
low-pass and high-pass filters with different cut-off frequencies. The DWT is one of the
operations that provide a multi-resolution signals. The DWT satisfies the energy conservation.
The original signal can be properly reconstructed via employing that technique, which gained
popularity in ECG denoising [10]. In the wavelet domain, this term means the noise rejection by
thresholding adequate. The contamination of the noise signal is concentrated in the details [10-
12]. We have used the orthogonal DWT for signal decomposition on the time-scale plan, which
represents the signal x(t) by:
1
( ) ( ) (2 )
k
j k
j
jx t w k t kψ
∞
= =−∞
= −∑ ∑ (5)
Where the function ( )tψ represents a discrete analysis wavelet and the coefficients ( )w kj represent
the signal at level j. The Performance of DWT depends on the choice of the wavelet and its
similarity to analyzed signal.
1.2 Time-frequency techniques
The time-frequency technique is a tool to treatment non-stationary signal, which used time and
frequency simultaneously to represent the non-stationary signal.
1.2.1 Parametric techniques
The parametric time-frequency techniques used in this work are the Capon (CA), the
Periodogram (PE) and the Time Varying autoregressive (TVAR).
1.2.1.1 Capon technique
The estimator of minimum variance called Capon estimator (CA) does not impose a model on the
signal. At each frequency f, this method seeks a matched filter whose response is 1 for the
frequency f and 0 everywhere else [15-16].
1
1
( , ) ( , ) ( , )
. .
H
x
x f
H
f
CA n f a n f R a n f
Z R n Z−
  
= = (6)
Where
- ( ),CA n f is the output power of the Capon filter, excited by the discrete signal x(n) sampled at
the period te,
- ( ) ( )0, ,..., pa n f a a= is the impulse response of the filter at frequency n,
- { }T
R n E x n x nx
     
     = is the autocorrelation matrix of crossed x(n) of dimension ( 1)*( 1)p p+ + ,
- ( ) ( )( ),...,x n x n p x n 
  = − is the signal at time n,
- ( )2 21, ,...,
H i ft i ft pe eZ e ef
π π= is the steering vector,
- ( 1)p+ is the number of filter coefficient, the exponent H is conjugate transpose and the
superscript T for transpose.

280
1.2.1.2 Periodogram technique
The Periodogram (PE) is the derivate of the Capon (CA) technique. The spectral estimator of
this method is defined by the following equation [16-19]:
2
( , ) . . /((p+1) )H
f x fPE n f Z R Z= (7)
The two previous techniques defined by the equations 6 and 7 can be applied sliding
windows. There is no theoretical criterion for choosing the filter order and duration of the
window [17-20]. The parametric techniques depend on the signal so that the frequency
response has a different shape and then different properties according to the signal
characteristics. The choice of the window is more crucial to the time-frequency resolution.
The CA and PE estimators usually have a better frequency resolution. Both techniques are
well suited to signals containing some strong spectral components such as ECG and EMG
biomedical signals.
1.2.1.3 Time-Varying autoregressive
The time-varying frequency can be extracted from its parameters ( )tai . Since the non-
stationary signal is modeled as the output of the TVAR process, with a zero-mean white noise
input w(t), the power spectral density of the stationary signal is given by [21]:
2
2
2
( )
( , )
1 1
w
j i
t
TVAR t f
i p
a eii
πυ
σ
−
=
=
+ ∑ =
(8)
Where 2
wσ is the variance of the white noise w(t).
1.2.2 Non-parametric techniques
1.2.2.1 S-transform
The S-transform (ST) is a time-frequency representation known for its local spectral phase
properties. A key feature of the ST is that it uniquely combines a frequency dependent
resolution of the time-frequency space with absolutely referenced local phase information.
This allows to define the meaning of phase in a local spectrum setting and results in many
advantageous characteristics. It also exhibits a frequency invariant amplitude response, in
contrast to the wavelet transform. The ST technique is given by [21]:
2 2( )
22
( , ) ( )
2
t f
i ftf
ST t f h t e e d
τ
π
τ
π
+∞
−∞
−
−
−
= ∫ (9)
Where h(t) is analysis windows.
1.2.2.2 Smoothed Pseudo Affine Wigner Distributions
The affine Wigner distributions show great potential as flexible tools for time-varying
spectral analysis. However, for some distributions of the Cohen’s class, they present two
major practical limitations: first the entire signal enters into the calculation of these

281
distributions at every point (t, f), and second, due to their nonlinearity, interference
components arise between each pair of signal components [18-19]. To overcome these
limitations, the time-windowing function h introduced to attenuate interference components
that oscillate in the frequency direction and for suppressing interference terms oscillating in
the time direction. We must smooth in that direction with a low-pass function G. The
smoothed pseudo affine Wigner distribution (SPAWD) is given by equation:
( ) *
( , ) ( ) ( , ( ) ; ) ( , ( ) ; )
( ) ( )
k k
x x k x k
k k
u
SPAWD t f G u T t u f T t u f du
u u
µ
λ λ
λ λ
+∞
−∞
= Ψ − Ψ∫
−
(10)
Where ( , ; )T t fx Ψ is the continuous wavelet transform, ( )ukµ is a real positive function,
1
1
( 1)
( ) ( )
1
u
k
k ku
k e
u
e
λ
−
−
−
−
=
−
and
2
( ) ( )
i
h t e
πτ
τΨ = is a band pass wavelet function.
2. BIOMEDICAL SIGNALS
The biomedical signals such as EMG, EEG and ECG are non-stationary and
nonlinear. The figure 2 presents the normal electrocardiogram signal; this signal presents the
P, T waves and the QRS complex.
Fig.2: Normal ECG signal
The abnormal signal used in this work is obtained by the patient who has anomaly named
atrial fibrillation. The figure 3 shows this abnormal signal [31]. The atrial rate exceeds 350
beats per minute in this type of arrhythmias. This arrhythmia occurs because of
uncoordinated activation and contraction of different parts of the atrial. The higher atria rate
and uncoordinated contraction leads to ineffective pumping of blood into the ventricles.
Fig.3: Abnormal ECG signal

282
3. RESULTS OF DENOISING TECHNIQUES
The ECG signals are often interfered by noises such as power interference noise and
the electromyography (EMG) noise caused by the muscle activity during recording. These
artifacts strongly influence the utility of recorded ECG signals. In this work the EMD, EEMD
and the DWT are presented for suppressing the noise that interferes with information; the
white noise is used. These techniques are applied to the abnormal electrocardiogram signal.
We compare the performance of the EEMD technique quantitatively with respect to the other
techniques based on two metrics: Mean Square Error (MSE) and Percent Root Mean Square
Difference (PRD). These metrics are computed as follows:
1
21
( ( ) ( ))N
nMSE x n x n
N
== −∑ (11)
2
1
1
2
( ( ) ( ))
*100
( )
N
n
N
n
x n x n
PRD
x n
=
=
−∑
=
∑
(12)
Where x(n) is the original ECG signal, ( )x n denotes the reconstruction of the ECG signal and
N is the number of ECG samples used.
The table 1 shows the MSE and PRD at different input SNR levels, the range of input SNR
levels is from -20 dB to 10 dB. The obtained results of the EEMD technique give the
smallest MSE and PRD which attests its capability to yield improved ECG signal with better
quality than the other techniques used at different inputs of the SNRs.
The table 2 gives the obtained results of the MSE and PRD for the denoising techniques using
different abnormal ECG signals under consideration at a particular input SNR level of -5 dB.
The EEMD technique outperforms other denoising techniques; the MSE and PRD of the
EEMD are relatively lower for all abnormal signals than the other techniques. The obtained
results show that EEMD technique is more effective than the other techniques at the level of
noise suppression and recovery of a form of original signal.
MSE PRD
Signal
used
SNR
(db) EEMD EMD DWT EEMD EMD DWT
Atrial
fibrillati
on
(040126)
-20
-15
-10
-5
0
5
7
10
0.7006
0.2805
0.0749
0.0274
0.0080
0.0042
0.0032
0.0030
0.9124
0.2884
0.0928
0.0291
0.0086
0.0044
0.0036
0.0033
1.0919
0.3560
0.1046
0.0389
0.0138
0.0071
0.0055
0.0038
647.827
409.901
211.744
128.120
69.9574
49.9532
43.8844
42.1739
739.256
415.661
235.730
132.112
71.1765
51.5151
46.6015
44.2632
808.730
461.761
250.267
152.662
90.7660
65.1999
57.5039
47.6864
Table 1: Comparison of the MSE and PRD obtained by using different techniques

283
MSE PRD
Abnormal
ECG Signals
used
EEMD EMD DWT EEMD EMD DWT
04126 0.0274 0.0291 0.0389 128.120 132.112 152.662
04746 0.0928 0.0943 0.1040 149.5683 150.7659 158.3118
04015 0.4104 0.4564 0.4725 129.7979 137.1329 139.5305
04043 0.1163 0.1396 0.1862 123.3458 126.4191 144.0233
04908 0.3313 0.3829 0.4211 130.3802 140.1509 146.9852
04936 0.1857 0.1980 0.2422 129.7550 133.9816 148.1775
05091 0.0660 0.0736 0.0872 131.7030 139.1122 151.3531
Table 2: Comparison of the MSE and PRD obtained by using different techniques of the
different abnormal signals adding -5 dB
Among the goals of this study is to eliminate the noise that corrupts the original ECG
signal. The figure 4 shows the reconstruction of the original abnormal signal without noises
by the denoising techniques used. The noise added is 8 db. This noise is an artifact frequency
component, which will cause misinterpretation of the physiological phenomena. After the
cancellation of artifacts by the EMD, EEMD and DWT techniques, the figure 4c given by
EEMD technique presents the true shape of the T wave and the true area of the Q and the
QRS complex. This artifact can be caused by breathing or movement of patients or by
instruments. It can be observed that the abnormal ECG signal largely restore the original
shape and clearly eliminates noises by EEMD technique (Fig. 4c). The figure 4a shows that
the DWT technique doesn’t restore the true area of QRS complex, the true area of T waves
and also true area of the Q waves. One of the major drawbacks of the EMD technique is the
frequent appearance of mode mixing effect in signal restoration. This effect does is not
revealed by EEMD technique (Fig. 4c). It is also evident that the EEMD is more suitable for
specific abnormal ECG signal feature enhancement. The obtained results show the
effectiveness of the EEMD technique and its capability of extracting useful information from
ECG signals affected by noises as compared to the other techniques used in this work such as
EMD and DWT.

284
Fig.4: Denoising abnormal ECG signal using DWT (a), EMD (b) and EEMD (c)
4. RESULTS OF TIME-FREQUENCY TECHNIQUES
4.1 Performance techniques
The time-frequency techniques used in this study are applied to a monocomponent
signal to find the most performance technique. The monocomponent signal used is given by
the following equation:
( )
( ) j t
x t ae φ
= (13)
The instantaneous frequency (IF) is given by the following equation:
0
1
/
2
f d dt f tφ β
π
= = + (14)
Where a=1, fo=0.05fs, β = 0.4fs, ( )tφ is the analytic signal phase and fs = 1/T is the sampling
frequency.
The bias (B) and the variance (VAR) of the estimate present the most important factors that
decide the quality of estimation. These two notions can be defined by the following
expressions:
ˆ ˆ( ( )) ( )B f t f ti iε  = ∆
 
(15)
2
ˆ ˆ( ( )) ( ( ))VAR f t f ti iε  = ∆
 
(16)
Where ˆ ( )f ti∆ = ( )f ti - ( )ˆ tfi , ( )f ti and ˆfi are the instantaneous frequency and instantaneous
frequency estimate respectively. The signal length used in the time-frequency techniques is
N=256 samples and the total signal duration is 1 s. The sampling frequency was fs=2 NHz.

285
Using different Signal-to-Noise Ratio (SNR), gaussian white noise samples are added to the
signal. The figure 5 shows the performance of the PE, CA, TVAR, ST and SPAWD time-
frequency techniques applied to a linear FM signal with 256 points.
Fig.5: Performance of variance of various techniques of a linear FM signal with length N=256
samples
According to the results of the figure 5, the PE time-frequency technique has a
minimal variance for all SNR's. The low minimum variance can indicate the performance of
the time-frequency technique. The PE technique surpasses the other time-frequency
techniques in robustness where it gives the minimum of the variance at low SNR.
4.2 Time-frequency images
In this section, we applied the parametric technique PE which has a minimal variance in
parametric technique and the non parametric technique SPAWD that has a minimal variance
in non-parametric techniques on ECG signals. The figures 6 and 7 present the time-
frequency images of the normal and abnormal ECG signals (figure 2 and 3). These time-
frequency images are obtained by using the calculation of the equation 7 and 10 of the PE
and the SAPWD techniques respectively. We converted the normal and abnormal ECG
signals into their analytical forms by using Hilbert transform first, then we apply the non-
parametric technique.
The figure 6 presents the time-frequency images of the normal ECG signal in 2D (a and b)
and 3D (a’ and b’). The PE and SPAWD time-frequency techniques are capable to identify
the frequency components over time in the normal signal with big difference between the
both. The PE technique (Fig 6a and 6a’) can follow and identify the different T waves and
QRS complexes existing in the normal signal. The SPAWD technique (Fig 6b and 6b’) shows
the presence of the interference-term which doesn't allow finely the identification of the QRS
complexes and also it cannot show the T waves in this normal signal. We can conclude that
the PE technique provides a good localization and visualization of the QRS complexes and T
waves over time.

286
Time (Samples)
Frequency(Hz)
100 200 300 400 500 600 700 800 900 1000
50
100
150
200
250
300
a
b’
b
a’
Fig.6: Periodogram (a and a’) and SPAWD (b and b’) time-frequency images of a normal ECG
Signal
The figure 7 presents the time-frequency images of the abnormal ECG signal in 2D (a
and b) and 3D (a’ and b’). This abnormal signal is obtained from a patient with artial
fibrillation. The parametric PE and non-parametric SPAWD techniques are able to give all
QRS complexes of the abnormal signal in time-frequency images (2D and 3D) with big
difference between the both. The non-parametric time-frequency image (figure 2D) presents
the interference-terms that hide the good visualization of the QRS complex. The SPAWD
technique can’t show the T wave in figure 7 (b and b’). The PE technique allows tracking the
change of the frequency components of each T wave and QRS complex. The obtained results
of the parametric technique in figure 7 (2D and 3D) show the morphology of QRS complexes
and T waves with clear and with good resolution and also we can note the overlapping
between the QRS complexes and T waves (QRS2 and T2), (QRS3 and T3) and (QRS5 and
T5). These overlaps indicate the abnormalities of this signal. The obtained results of the
parametric time-frequency technique allow giving all frequency components of the normal
and abnormal signals, with high time-frequency resolution and the interference-terms
suppressing. The PE technique is expected to be more efficient in analyzing the ECG signal
than the other time-frequency techniques used in this work.

287
Fig.7: Periodogram (a and a’) and SPAWD (b and b’) time-frequency images of an abnormal
ECG signal with atrial fibrillation
5 CONCLUSION
In this paper, we present a comparative performance study of the denoising
techniques and the parametric and non-parametric ones. The techniques used in this work are
the denoising methods: EEMD, EMD and DWT and also the time-frequency methods: PE,
CA, TVAR, SPAWD and ST. In the case of the time-frequency methods, the parametric
technique is able to detect the different QRS complexes and T waves of the atrial fibrillation
and normal signals with high resolution and cross-terms suppressing. The PE time-frequency
technique is expected to be more efficient in analyzing the abnormal ECG than the other
techniques used. In this study, the EMD, EEMD and DWT techniques were applied to the
abnormal ECG signal with atrial fibrillation, in order to eliminate the effects of noise which
hide the useful information. The main advantages of the EMD and EEMD techniques are that
they do not make any prior assumption about the data being analyzed. The EEMD technique
shows the cancellation of artifacts of abnormal signal which are due to different noises. The
DWT technique can’t restore the useful information of the different components of the
electrocardiogram signal as an area of QRS complex and T wave. The obtained result of the
analysis of ECG signal shows that the EEMD technique could be successfully applied for the
attenuation noise. The combination of the EEMD and PE techniques can be a good issue in
analyzing the ECG signals.
a
b’
b
a’
Time (Samples)
Frequency(Hz)
100 200 300 400 500 600 700 800 900 1000
20
40
60
80
100
120
140
Time (Samples)
Frequency(Hz)
100 200 300 400 500 600 700 800 900 1000
50
100
150
200

288
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