Analysis electrocardiogram signal using ensemble empirical mode decomposition and time

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Analysis electrocardiogram signal using ensemble empirical mode decomposition and time

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME275ANALYSIS ELECTROCARDIOGRAM SIGNAL USING ENSEMBLEEMPIRICAL MODE DECOMPOSITION AND TIME-FREQUENCYTECHNIQUESSamir Elouaham1, Rachid Latif1, Boujemaa Nassiri1, Azzedine Dliou1, MostafaLaaboubi2, Fadel Maoulainine31(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 AyyadUniversity, Marrakech, Morocco)ABSTRACTElectrocardiogram signals (ECG) are among the most important sources of diagnosticinformation in healthcare. During ECG measurement, there may be various noises which interferewith the ECG information identification that cause a misinterpretation of the ECG signal. In thispaper, the Empirical Mode Decomposition (EMD), the Ensemble Empirical ModeDecomposition (EEMD) and the Discrete Wavelet Transform (DWT) were used to overcomethese problems. These techniques are applied to a noisy electrocardiogram abnormal signalobtained by adding white noise. A comparative performance study of these three techniques interms of several standard metrics was used. The EEMD was chosen for its better localization ofthe components of the ECG signal. The non-stationary and non-linear nature of the ECG signalsmakes 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 bothtime and frequency which allows local, transient or intermittent components to be elucidated. Inthis paper, the parametric techniques used are periodogram (PE), capon (CA) and time-varyingautoregressive (TVAR) and non-parametric techniques used are Smoothed Pseudo Affine WignerDistributions (SPAWD) and S-transform (ST). The abnormal signal used is obtained from thepatient with an atrial fibrillation. The PE technique shows its superior performance, in terms ofresolution and interference-terms suppressing, as compared to other time-frequency techniquesused in this paper. From the obtained results, the EEMD technique is a more powerful tool forelimination and restoration of the original signal than the other techniques used in this paper. Thisstudy shows that the combination of the EEMD and the Periodogram techniques are a good issuein the biomedical field.INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING& TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 2, March – April (2013), pp. 275-289© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)www.jifactor.comIJCET© I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME276Keywords: EEMD, Time-frequency, PE, Non-stationary, ECG signals.INTRODUCTIONElectrocardiography is a commonly used, noninvasive procedure for recordingelectrical 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 beatof the heart. The waves in a normal record are named P, QRS complex, and T and follow inalphabetical order. The figure 1 shows the normal ECG signal [1-3]:Fig.1: A waves of the normal ECG signalThe ECG signal is often corrupted by various noises such as prolongedrepolarization, changes of electrode position, muscle contraction (electromyography) andpower line interference (electrode) [3]. Among the objectives of this study is to separate thesignal component from the undesired artifacts. These artifacts can’t facilitate a good, easyand accurate interpretation of the ECG signals that implies bad diagnostic given by theexpert. Therefore the elimination of noises is inevitable. To overcome this problem, severaltechniques are presented whose goal is the cancellation of the noises existing during therecordings 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 andbiomedical; these techniques give good results. The disadvantage of elliptic filter, medianfilter, 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 basisfunction that is controlled by the signal components that are relatively large in a frequencyband [10, 12]. And the disadvantage of Empirical Mode Decomposition (EMD) is theappearance of mode-mixing effect in signal restoration [4-6]. To overcome these problemsthe new technique called Ensemble Empirical Mode Decomposition (EEMD) is proposed byWu and Huang [7]. The choice of a powerful technique among of these techniques is relatedto the result obtained after denoising ECG signals; these results are the original characteristicwaveforms such as QRS complexes, the P and T waves and also Q, R and S waves. A meansquare error (MSE) and percent root mean square difference (PRD) between filter ECGoutput and clean ECG were used for giving the performance of the denoising techniquesused. The results obtained by the EEMD show high resolution, noise cancellation andpreservation of true waveforms of ECG signals more than the other techniques as EMD andDWT. Among the objectives presented in this paper is the choice of the useful technique forany application that needs the denoising of non-stationary and non-linear biomedical signalssuch as ECG, EEG, EOG and EMG in the pretreatment stage.
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME277The 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 toanalyze the ECG signals. Traditionally time-domain is based on the measurement of thesurface QRS and the amplitude of the P, the QRS complexes and the T waves. Thedisadvantage of this technique is that it cannot give the useful information of frequencycomponents 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 theanalysis 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 varywith time. The FFT technique is unsuitable. To tackle these limitations, the time-frequencytechniques are useful [15- 30]. In recent years the time-frequency methods constitute animportant amelioration in signals analysis specifically in the biomedical signals. Thesetechniques are Wavelets, Wigner-Ville, Choi-Williams and Born-Jordan [28]. In this paperwe present the parametric techniques such as periodogram (PE) capon (CA) and time varyingautoregressive (TVAR) and non parametric as S-transform (ST) and smoothed pseudo affineWigner distributions (SPAWD) [15-20]. The drawback of the SPAWD is the appearance ofthe cross-terms. These cross-terms hide useful information of interest in the signal that canhelp the expert to extract real features. The features obtained from ST are not completelydistinctive and don’t give clear information about the component of the signal. Thesedisadvantages are resolved by the PE time-frequency technique. The performance of thesetechniques is given by the calculation of the variance obtained by adding the noise of themodulated signal. The results show that the periodogram technique is more powerful than theother techniques. The electrocardiogram signals used are normal and abnormal. Theabnormal 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 andabnormal electrocardiogram signals. The obtained results of the denoising techniques aregiven in section IV. The section V gives the results of time-frequency techniques used.Finally, conclusion is provided in section VI.1. TECHNIQUES USED1.1 Denoising techniques1.1.1 EMDThe EMD was proposed by Huang and al. as a tool to adaptively decompose a signalinto a collection of AM–FM components [4-6]. The EMD method has no mathematicalfoundations and analytical expressions for the theoretical study. The various works havesuccessfully used the EMD to real data in several fields such as biomedicine, study of climatephenomena, seismology or acoustics [4-6]. These studies show satisfaction and matchingcondition 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 ModeFunctions IMFJ(t). These functions IMFJ(t) oscillate around zero and can express the signalx(t) by the expression:
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME2781( ) ( ) ( )kjjx 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 throughoutthe 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-orderIMFJ(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 thetechnical aspects of the EMD implementation and makes the five-step algorithm given by thefollowing [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 ofdecreasing frequency up to forward only a residue of low frequency.1.1.2 EEMDThe ensemble EMD method has been proposed to overcome mode mixing problemexisting in EMD technique [7]. The EMD technique allows giving all solutions that give thetrue IMF by repeating the decomposition processes. The procedure of the EEMD method isgiven 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 theamplitude 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 aresidue.1( )niix k c r== +∑) )(4)Where n represents the number of the IMFs, ic)is the ithIMF that is the ensemble mean of thecorresponding IMF obtained from all of the decomposition processes and r)is the mean ofthe residues from all of the decomposition processes.
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME2791.1.3 Discrete Wavelet TransformWavelet 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 ofapproximation signals and details of a start signal. In discrete wavelet transform (DWT), foranalyzing both the low and high frequency components in x(n), it is passed through a series oflow-pass and high-pass filters with different cut-off frequencies. The DWT is one of theoperations that provide a multi-resolution signals. The DWT satisfies the energy conservation.The original signal can be properly reconstructed via employing that technique, which gainedpopularity in ECG denoising [10]. In the wavelet domain, this term means the noise rejection bythresholding 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, whichrepresents the signal x(t) by:1( ) ( ) (2 )kj kjjx t w k t kψ∞= =−∞= −∑ ∑ (5)Where the function ( )tψ represents a discrete analysis wavelet and the coefficients ( )w kj representthe signal at level j. The Performance of DWT depends on the choice of the wavelet and itssimilarity to analyzed signal.1.2 Time-frequency techniquesThe time-frequency technique is a tool to treatment non-stationary signal, which used time andfrequency simultaneously to represent the non-stationary signal.1.2.1 Parametric techniquesThe parametric time-frequency techniques used in this work are the Capon (CA), thePeriodogram (PE) and the Time Varying autoregressive (TVAR).1.2.1.1 Capon techniqueThe estimator of minimum variance called Capon estimator (CA) does not impose a model on thesignal. At each frequency f, this method seeks a matched filter whose response is 1 for thefrequency f and 0 everywhere else [15-16].11( , ) ( , ) ( , ). .Hxx fHfCA 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 atthe 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 efπ π= is the steering vector,- ( 1)p+ is the number of filter coefficient, the exponent H is conjugate transpose and thesuperscript T for transpose.
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME2801.2.1.2 Periodogram techniqueThe Periodogram (PE) is the derivate of the Capon (CA) technique. The spectral estimator ofthis method is defined by the following equation [16-19]:2( , ) . . /((p+1) )Hf x fPE n f Z R Z= (7)The two previous techniques defined by the equations 6 and 7 can be applied slidingwindows. There is no theoretical criterion for choosing the filter order and duration of thewindow [17-20]. The parametric techniques depend on the signal so that the frequencyresponse has a different shape and then different properties according to the signalcharacteristics. 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 arewell suited to signals containing some strong spectral components such as ECG and EMGbiomedical signals.1.2.1.3 Time-Varying autoregressiveThe 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 noiseinput w(t), the power spectral density of the stationary signal is given by [21]:222( )( , )1 1wj itTVAR t fi pa eiiπυσ−==+ ∑ =(8)Where 2wσ is the variance of the white noise w(t).1.2.2 Non-parametric techniques1.2.2.1 S-transformThe S-transform (ST) is a time-frequency representation known for its local spectral phaseproperties. A key feature of the ST is that it uniquely combines a frequency dependentresolution 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 manyadvantageous characteristics. It also exhibits a frequency invariant amplitude response, incontrast to the wavelet transform. The ST technique is given by [21]:2 2( )22( , ) ( )2t fi ftfST t f h t e e dτπτπ+∞−∞−−−= ∫ (9)Where h(t) is analysis windows.1.2.2.2 Smoothed Pseudo Affine Wigner DistributionsThe affine Wigner distributions show great potential as flexible tools for time-varyingspectral analysis. However, for some distributions of the Cohen’s class, they present twomajor practical limitations: first the entire signal enters into the calculation of these
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME281distributions at every point (t, f), and second, due to their nonlinearity, interferencecomponents arise between each pair of signal components [18-19]. To overcome theselimitations, the time-windowing function h introduced to attenuate interference componentsthat oscillate in the frequency direction and for suppressing interference terms oscillating inthe time direction. We must smooth in that direction with a low-pass function G. Thesmoothed pseudo affine Wigner distribution (SPAWD) is given by equation:( ) *( , ) ( ) ( , ( ) ; ) ( , ( ) ; )( ) ( )k kx x k x kk kuSPAWD t f G u T t u f T t u f duu uµλ λλ λ+∞−∞= Ψ − Ψ∫−(10)Where ( , ; )T t fx Ψ is the continuous wavelet transform, ( )ukµ is a real positive function,11( 1)( ) ( )1ukk kuk eueλ−−−−=−and2( ) ( )ih t eπττΨ = is a band pass wavelet function.2. BIOMEDICAL SIGNALSThe biomedical signals such as EMG, EEG and ECG are non-stationary andnonlinear. The figure 2 presents the normal electrocardiogram signal; this signal presents theP, T waves and the QRS complex.Fig.2: Normal ECG signalThe abnormal signal used in this work is obtained by the patient who has anomaly namedatrial fibrillation. The figure 3 shows this abnormal signal [31]. The atrial rate exceeds 350beats per minute in this type of arrhythmias. This arrhythmia occurs because ofuncoordinated activation and contraction of different parts of the atrial. The higher atria rateand uncoordinated contraction leads to ineffective pumping of blood into the ventricles.Fig.3: Abnormal ECG signal
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME2823. RESULTS OF DENOISING TECHNIQUESThe ECG signals are often interfered by noises such as power interference noise andthe electromyography (EMG) noise caused by the muscle activity during recording. Theseartifacts strongly influence the utility of recorded ECG signals. In this work the EMD, EEMDand the DWT are presented for suppressing the noise that interferes with information; thewhite 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 othertechniques based on two metrics: Mean Square Error (MSE) and Percent Root Mean SquareDifference (PRD). These metrics are computed as follows:121( ( ) ( ))NnMSE x n x nN== −∑ (11)2112( ( ) ( ))*100( )NnNnx n x nPRDx n==−∑=∑(12)Where x(n) is the original ECG signal, ( )x n denotes the reconstruction of the ECG signal andN is the number of ECG samples used.The table 1 shows the MSE and PRD at different input SNR levels, the range of input SNRlevels is from -20 dB to 10 dB. The obtained results of the EEMD technique give thesmallest MSE and PRD which attests its capability to yield improved ECG signal with betterquality 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 usingdifferent 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 theEEMD are relatively lower for all abnormal signals than the other techniques. The obtainedresults show that EEMD technique is more effective than the other techniques at the level ofnoise suppression and recovery of a form of original signal.MSE PRDSignalusedSNR(db) EEMD EMD DWT EEMD EMD DWTAtrialfibrillation(040126)-20-15-10-5057100.70060.28050.07490.02740.00800.00420.00320.00300.91240.28840.09280.02910.00860.00440.00360.00331.09190.35600.10460.03890.01380.00710.00550.0038647.827409.901211.744128.12069.957449.953243.884442.1739739.256415.661235.730132.11271.176551.515146.601544.2632808.730461.761250.267152.66290.766065.199957.503947.6864Table 1: Comparison of the MSE and PRD obtained by using different techniques
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME283MSE PRDAbnormalECG SignalsusedEEMD EMD DWT EEMD EMD DWT04126 0.0274 0.0291 0.0389 128.120 132.112 152.66204746 0.0928 0.0943 0.1040 149.5683 150.7659 158.311804015 0.4104 0.4564 0.4725 129.7979 137.1329 139.530504043 0.1163 0.1396 0.1862 123.3458 126.4191 144.023304908 0.3313 0.3829 0.4211 130.3802 140.1509 146.985204936 0.1857 0.1980 0.2422 129.7550 133.9816 148.177505091 0.0660 0.0736 0.0872 131.7030 139.1122 151.3531Table 2: Comparison of the MSE and PRD obtained by using different techniques of thedifferent abnormal signals adding -5 dBAmong the goals of this study is to eliminate the noise that corrupts the original ECGsignal. The figure 4 shows the reconstruction of the original abnormal signal without noisesby the denoising techniques used. The noise added is 8 db. This noise is an artifact frequencycomponent, which will cause misinterpretation of the physiological phenomena. After thecancellation of artifacts by the EMD, EEMD and DWT techniques, the figure 4c given byEEMD technique presents the true shape of the T wave and the true area of the Q and theQRS complex. This artifact can be caused by breathing or movement of patients or byinstruments. It can be observed that the abnormal ECG signal largely restore the originalshape and clearly eliminates noises by EEMD technique (Fig. 4c). The figure 4a shows thatthe DWT technique doesn’t restore the true area of QRS complex, the true area of T wavesand also true area of the Q waves. One of the major drawbacks of the EMD technique is thefrequent appearance of mode mixing effect in signal restoration. This effect does is notrevealed by EEMD technique (Fig. 4c). It is also evident that the EEMD is more suitable forspecific abnormal ECG signal feature enhancement. The obtained results show theeffectiveness of the EEMD technique and its capability of extracting useful information fromECG signals affected by noises as compared to the other techniques used in this work such asEMD and DWT.
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME284Fig.4: Denoising abnormal ECG signal using DWT (a), EMD (b) and EEMD (c)4. RESULTS OF TIME-FREQUENCY TECHNIQUES4.1 Performance techniquesThe time-frequency techniques used in this study are applied to a monocomponentsignal to find the most performance technique. The monocomponent signal used is given bythe following equation:( )( ) j tx t ae φ= (13)The instantaneous frequency (IF) is given by the following equation:01/2f 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 samplingfrequency.The bias (B) and the variance (VAR) of the estimate present the most important factors thatdecide the quality of estimation. These two notions can be defined by the followingexpressions: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 ˆfi are the instantaneous frequency and instantaneousfrequency estimate respectively. The signal length used in the time-frequency techniques isN=256 samples and the total signal duration is 1 s. The sampling frequency was fs=2 NHz.
  11. 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME285Using different Signal-to-Noise Ratio (SNR), gaussian white noise samples are added to thesignal. 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=256samplesAccording to the results of the figure 5, the PE time-frequency technique has aminimal variance for all SNRs. The low minimum variance can indicate the performance ofthe time-frequency technique. The PE technique surpasses the other time-frequencytechniques in robustness where it gives the minimum of the variance at low SNR.4.2 Time-frequency imagesIn this section, we applied the parametric technique PE which has a minimal variance inparametric technique and the non parametric technique SPAWD that has a minimal variancein 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 PEand the SAPWD techniques respectively. We converted the normal and abnormal ECGsignals 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 identifythe frequency components over time in the normal signal with big difference between theboth. The PE technique (Fig 6a and 6a’) can follow and identify the different T waves andQRS complexes existing in the normal signal. The SPAWD technique (Fig 6b and 6b’) showsthe presence of the interference-term which doesnt allow finely the identification of the QRScomplexes and also it cannot show the T waves in this normal signal. We can conclude thatthe PE technique provides a good localization and visualization of the QRS complexes and Twaves over time.
  12. 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME286Time (Samples)Frequency(Hz)100 200 300 400 500 600 700 800 900 100050100150200250300ab’ba’Fig.6: Periodogram (a and a’) and SPAWD (b and b’) time-frequency images of a normal ECGSignalThe figure 7 presents the time-frequency images of the abnormal ECG signal in 2D (aand b) and 3D (a’ and b’). This abnormal signal is obtained from a patient with artialfibrillation. The parametric PE and non-parametric SPAWD techniques are able to give allQRS complexes of the abnormal signal in time-frequency images (2D and 3D) with bigdifference between the both. The non-parametric time-frequency image (figure 2D) presentsthe interference-terms that hide the good visualization of the QRS complex. The SPAWDtechnique can’t show the T wave in figure 7 (b and b’). The PE technique allows tracking thechange of the frequency components of each T wave and QRS complex. The obtained resultsof the parametric technique in figure 7 (2D and 3D) show the morphology of QRS complexesand T waves with clear and with good resolution and also we can note the overlappingbetween the QRS complexes and T waves (QRS2 and T2), (QRS3 and T3) and (QRS5 andT5). These overlaps indicate the abnormalities of this signal. The obtained results of theparametric time-frequency technique allow giving all frequency components of the normaland abnormal signals, with high time-frequency resolution and the interference-termssuppressing. The PE technique is expected to be more efficient in analyzing the ECG signalthan the other time-frequency techniques used in this work.
  13. 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME287Fig.7: Periodogram (a and a’) and SPAWD (b and b’) time-frequency images of an abnormalECG signal with atrial fibrillation5 CONCLUSIONIn this paper, we present a comparative performance study of the denoisingtechniques and the parametric and non-parametric ones. The techniques used in this work arethe 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 parametrictechnique is able to detect the different QRS complexes and T waves of the atrial fibrillationand normal signals with high resolution and cross-terms suppressing. The PE time-frequencytechnique is expected to be more efficient in analyzing the abnormal ECG than the othertechniques used. In this study, the EMD, EEMD and DWT techniques were applied to theabnormal ECG signal with atrial fibrillation, in order to eliminate the effects of noise whichhide the useful information. The main advantages of the EMD and EEMD techniques are thatthey do not make any prior assumption about the data being analyzed. The EEMD techniqueshows the cancellation of artifacts of abnormal signal which are due to different noises. TheDWT technique can’t restore the useful information of the different components of theelectrocardiogram signal as an area of QRS complex and T wave. The obtained result of theanalysis of ECG signal shows that the EEMD technique could be successfully applied for theattenuation noise. The combination of the EEMD and PE techniques can be a good issue inanalyzing the ECG signals.ab’ba’Time (Samples)Frequency(Hz)100 200 300 400 500 600 700 800 900 100020406080100120140Time (Samples)Frequency(Hz)100 200 300 400 500 600 700 800 900 100050100150200
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