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 Latif

1, Boujemaa Nassiri

1, Azzedine Dliou

1, Mostafa

Laaboubi2, Fadel Maoulainine

3

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.

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ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), pp. 275-289 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com

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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.



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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

ii

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 fHf

CA n f a n f R a n fZ 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,

- { }TR 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 it

TVAR t fi p

a eiiπυ

σ

−=

=+ ∑ =

(8)

Where 2wσ 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 ftfST t f h t e e d

τπ

τπ

+∞

−∞

−−

−= ∫ (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 kx x k x k

k k

uSPAWD t f G u T t u f T t u f du

u u

µλ λ

λ λ

+∞

−∞

= Ψ − Ψ∫−

(10)

Where ( , ; )T t fx Ψ is the continuous wavelet transform, ( )uk

µ is a real positive function, 1

1( 1)

( ) ( )1

u

kk k u

k eu

eλ

−

−−

−=

− and

2( ) ( )

ih t e

πττΨ = 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:

121

( ( ) ( ))NnMSE x n x n

N== −∑ (11)

2

1

12

( ( ) ( ))*100

( )

Nn

Nn

x n x nPRD

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 tx t ae

φ= (13)

The instantaneous frequency (IF) is given by the following equation:

01

/2

f d dt f tφ βπ

= = + (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)

2ˆ ˆ( ( )) ( ( ))VAR f t f ti iε = ∆

(16)

Where ˆ ( )f ti∆ = ( )f ti - ( )ˆ tfi , ( )f ti and f̂i 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)

Fre

que

ncy

(H

z)

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)

Fre

quen

cy (

Hz)

100 200 300 400 500 600 700 800 900 1000

20

40

60

80

100

120

140

Time (Samples)

Fre

quen

cy (

Hz)

100 200 300 400 500 600 700 800 900 1000

50

100

150

200



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REFERENCES

[1] P. de Chazal, Maraia O’ Dwyer, Richard B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features: IEEE Transactions on Biomedical Engineering, 51 (7), 2004, 1196–1206. [2] G. Bortolan, Christian Brohet, Sergio Fusaro. Possibilities of using neural networks for ECG classification, Journal of Electrocardiology, 29, 1996, 10-16. [3] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung and H. H. Liu, The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proc. Roy. Soc. London 454A, 1998, 903–995. [4] P. Flandrin, G. Rilling and P. Goncalves, Empirical mode decomposition as a filter bank: IEEE Signal Process. Lett. 11, 2004, 112–114. [5] S. Elouaham, R. Latif, A. Dliou, F. M. R. Maoulainine, M. Laaboubi, analysis of biomedical signals by the empirical mode decomposition and parametric time-frequency techniques: International Symposium on security and safety of Complex Systems, May 2012, Agadir, Morocco. [6] G. Rilling, P. Flandrin, P. Goncalves, On Empirical Mode Decomposition and its Algorithms: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, June, 2003, Grado-Trieste, Italy. [7] JR. Yen, JS. Shieh, NE. Huang. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv Adapt Data Anal, 2010, 2(2), 135–56. [8] PH. Tsui, CC. Chang, NE. Huang. Noise-modulated empirical mode decomposition. Adv Adapt Data Anal 2010, 2(1), 25–37. [9] Z. Wu, NE. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009, 1(1), 1–41. [10] R. Larson, David. Unitary systems and wavelet sets Wavelet Analysis and Applications. Appl. Numer. Harmon. Anal. Birkhäuser. 2007, 143–171. [11] A.N. Akansu, W.A. Serdijn, and I.W. Selesnick, Wavelet Transforms in Signal Processing: A Review of Emerging Applications, Physical Communication, Elsevier, 3, issue 1, 2010, 1-18. [12] L. Sharma, S. Dandapat, A. Mahanta, Multiscale wavelet energies and relative energy based denoising of ecg signal, in: 2010 IEEE International Conference on Communication Control and Computing Technologies, 2010, 491–495. [13] C. Bigan, M.S. Woolfson, Time-frequency analysis of short segments of biomedical data: IEEE Proc.-Sci. Meas. Technol. 147 (6), 2000, 368–373, [14] R.H. Clayton, A. Murray. Estimation of the ECG signal spectrum during ventricular fibrillation using the fast Fourier transform and maximum entropy methods: Proceedings of the Computers in Cardiology, 1993, 867 – 870. [15] M.T. Özgen. Extension of the Capon’s spectral estimator to time–frequency analysis and to the analysis of polynomial-phase signals, Signal Process, 83, n.3, 2003, 575–592. [16] S. Elouaham, R. Latif, A. Dliou, M. Laaboubi, F. M. R. Maoulainine. Parametric and non-parametric time-frequency analysis of biomedical signals, International Journal of Advanced Computer Science and Applications, 4(1), 2013, 74-79. [17] Castanié F. Spectral Analysis Parametric and Non-Parametric Digital Methods, (ISTE Ltd, USA, 2006) 175-211 [18] P. Goncalves, F. Auger, P. Flandrin. 1995. Time-frequency toolbox.



289

[19] P. Flandrin, N. Martin, M. Basseville. Méthodes temps-fréquence (Trait. Signal 9, 1992). [20] M. Jachan, G. Matz, and F. Hlawatsch. Time-frequency-autoregressive random processes: Modeling and fast parameter estimation: IEEE ICASSP, VI, 2003, 125–128 [21] R. Stockwell, L. Mansinha, R. Lowe, Localization of the Complex Spectrum: The S. Transform, IEEE Transactions on Signal Processing, 44 (4), 1996, 998–1001. [22] R. Latif, E. Aassif, G. Maze, A. Moudden, B. Faiz, Determination of the group and phase velocities from time-frequency representation of Wigner-Ville. Journal of Non Destructive Testing & Evaluation International, 32, n.7, 1999. 415- 422. [23] R. Latif, E. Aassif, G. Maze, D. Decultot, A. Moudden, B. Faiz. Analysis of the circumferential acoustic waves backscattered by a tube using the time-frequency representation of wigner-ville, Journal of Measurement Science and Technology, 11, n. 1, 2000, 83-88. [24] R. Latif, E. Aassif, A. Moudden, B. Faiz, G. Maze. The experimental signal of a multilayer structure analysis by the time-frequency and spectral methods, NDT&E International, 39, n. 5, 2006, 349-355. [25] R. Latif, E. Aassif, A. Moudden, B. Faiz. High resolution time-frequency analysis of an acoustic signal backscattered by a cylindrical shell using a Modified Wigner-Ville representation, Meas. Sci. Technol.14, 2003, 1063-1067. [26] R. Latif, M. Laaboubi, E. Aassif, G. Maze. Détermination de l’épaisseur d’un tube élastique à partir de l’analyse temps-fréquence de Wigner-Ville, Journal Acta-Acustica, 95, n. 5, 2009, 843-848. [27] R.B Pachori, P. Sircar. A new technique to reduce cross-terms in the Wigner distribution, Digital Signal Processing, 17, 2007, 466–474. [28] A. Dliou, R. Latif, M. Laaboubi, F.M.R. Maoulainine. Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques, International Journal of Computer Applications, 41 (4), 2012, 25-30. [29] A. Dliou, R. Latif, M. Laaboubi, F. M. R. Maoulainie, S. Elouaham, Noised abnormal ECG signal analysis by combining EMD and Choi-Williams techniques: IEEE conference publications; International Conference on Complex Systems (ICCS), 5-6 Novembre, 2012 Agadir, Morocco. [30] H. Choi., W. Williams, Improved time-frequency representation of multicomponent signals using exponential kernels: IEEE Trans. on Acoustics, Speech and Signal Processing, 37, 1989, 862-871. [31] Physiobank, Physionet viewed August 2005 Physiologic signal archives for biomedical research, http://www. physionet.org/physiobank/. [32] Vivek S. chaudhari and Dr. P.B.Patil, “Electrocardiographic Detection of Left Ventricular Hypertrophy by the New “Sai” Criteria” International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2, 2012, pp. 51 - 57, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [33] Kavita L.Awade, “Design and Development of Microstrip Array Antenna for Wide Dual Band Operation”, International journal of Electronics and Communication Engineering &Technology (IJECET), Volume 1, Issue 1, 2010, pp. 33 - 43, ISSN Print: 0976-6464, ISSN Online: 0976-6472

analysis electrocardiogram signal using ensemble empirical mode decomposition and time

Technology

eemd techniques

electrocardiogram ecg

ecg signalsmakes

denoising ecg

ecg measurement

nonparametric techniques

periodogram techniques

signal components