ECG Signal De-noising by using Empirical Wavelet Transform ...Empirical Mode Decomposition, Band pass Filter(BPF), DiscreteWaveletTransform(DWT),EmpiricalWaveletTrans-form, Adaptive

ECG Signal De-noising by usingEmpirical Wavelet Transform and

Extended Kalman Filter

K.Praveen1, H.D.Praveena2,Dr. C.Subhas3,

1PG Scholar, 2Assistant Professor3Professor

1,2,3Depatment of ECE1,2,3Sree Vidynikethan Engg.College

[email protected]@gmail.com

[email protected]

August 15, 2018

Abstract

Isolating a records bearing signal from the backgroundnoise is a preferred issue in signal processing. In clinicaldiscipline at some stage in Statistics acquisition of ECGsign, various noise assets inclusive of power line interference,baseline wander and muscle artifacts contaminated with thedata bearing ECG sign. For better assessment and inter-pretation, the ECG sign ought to be free of noise. We haveconventional strategies like EWT (Empirical Wavelet Trans-form) and EMD (Empirical Mode Decomposition) with Adap-tive Filter were used to take away the energy line interfer-ence but those algorithms and strategies are futile to reducethe Power Line Interference (PLI) and provide much lessSNR and computational time is greater. So, we proposed anew technique that’s EWT (Empirical wavelet transform)+EKF (Extended Kalman Filter) to eliminate the PLI .To

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validate the proposed strategies, the recordings from MIT/BIH (Physionet) database were used. The EWT+EKF de-noising techniques have much less computational complex-ity and are more green compared with the EMD+ AdaptiveFilter and EWT+Adaptive filter primarily based de-noisingmethods.

Key Words:Electrocardiogram, Power line Interference,Empirical Mode Decomposition, Band pass Filter(BPF),Discrete Wavelet Transform(DWT) , Empirical Wavelet Trans-form, Adaptive Filter, Extended Kalman Filter.

1 Introduction

The electrocardiogram is the most ordinarily known indicative biomed-ical signal. It is the graphical portrayal of the capacity of the humanheart and can be recorded effortlessly with surface anodes put onthe limbs and chest. Typically, the frequency range of an ECG sig-nal is 0.05-100Hz and amplitude range is 5-10mV.Among differentthings, an ECG can be used to measure the charge and rhythm ofheartbeats, the scale and function of the coronary heart chambers,the presence of any damage to the heart’s muscle cells or conduc-tion device. It extensively utilized to degree the results of cardiacpills, and the function of implanted pacemakers. The ECG signalconsists of three basic waves, those are P, QRS and T. These wavescorrespond to the far field induced by the specific electrical phe-nomena on the cardiac surface, Specifically atrial depolarization (Pwave), the ventricular depolarization (QRS complicated), and theventricular re-polarization (T wave).Figure 1 shows that the origi-nal ECG signal with different waves and their intervals.

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Figure 1. ECG signal

One of the Precept issues in biomedical indicators, just like theelectrocardiogram(ECG), is the division of the preferred sign fromnoises[2] Due to power line interference, muscle artifacts, baselinewandering and motion artifacts. The Powerline interference fre-quency range is 50/60 Hz and which lies in the ECG signal.ThisPLI[6] is caused by improper grounding of ECG machine, Stray af-tereffects of the rotating current fields because of loops inside thecables,shielding and amplifier design. So removing of PLI noisefrom the ECG signal is very crucial.A overall of 3 strategies for PLIdiscount primarily based on EWT and EKF are analyzed and theirresults are offered on this paper.

2 RELATED WORK

1) EMD and Adaptive filter-based power-line interferencereduction.EMD (Empirical Mode Decomposition)Empirical Mode Decomposition (EMD)[5] is a recently introducedtechnique and it is also used for processing non-linear and non-stationary signals in addition to stationary signals. EMD hastheproperty of adaptive and signal-dependency, making this techniquewell suited for biomedical signal analysis. It is an iterative algo-rithm that computes the maxi-mum and minimum extreme. TheEMD is based on the sequential extraction of energy associated withvarious intrinsic time scalesof the signal, starting from finer tem-poral scales (high-frequency modes) to coarser ones (low-frequency

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modes). Thus, accordingto this Decomposition any sign can berepresented because the sumof intrinsic mode features (IMFs) anda residue. An IMF[5] [7]is described as a characteristic with equalvariety of intense and zerocrossings with its envelopes, as definedby means of all of the neighborhood maxima and minima.Adaptive FilterAn adaptive filter [3]is a self-designing device in that theadaptiveFilter relies for its operation on a recursive algorithm,which makesit possible for the filter to carry out satisfactorily in an environ-ment where complete information of the signal traits is not to behad.. Least suggest square (LMS) and recursive least rectangular(RLS)are the two major adaptive algorithms in the theory ofadap-tive Filters. An Adaptive filter is a self-getting to know virtualfilter that adjusts its filter coefficients so one can minimize an blun-ders characteristic. The electricity line interference noise can bereduced with the combination of EMD and Adaptive filter[7]. Theempirical mode decomposition (EMD) decomposes a sign accord-ing to its contained information. Even though it is highly adaptivein nature and is useful for many applications, the main issue withEMD method is its lack of mathematical theory. EMD is a verycomplex and slow process.2) EWT and AF (Adaptive Filter)- based power-line in-terference reductionEWT (Empirical Wavelet Transform)Here we are implementing EWT[4] (Empirical Wavelet Transform)to overcome the demerits of EMD.If we take the Fourier point ofview, this construction is equivalent to building a set of bandpassfilters. Empirical wavelet changes which unequivocally assemblesa versatile wavelet channel bank to decay a given flag into variousmodes.Separating the different modes is equivalent.To section theFourier spectrum and to apply some filtering corresponding to ev-ery detected assist. The EWT plays nearby maxima detection ofthe Fourier spectra of the signal, then performs spectrum segmen-tation primarily based on detected maxima and finally constructsa corresponding wavelet.clear out financial institution. The keyconcept right here is to separate special quantities of the Fourierspectrum which corresponding to different Modes. The Empiricalscaling characteristic and the empirical wavelets are defined through

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equations (1) and a pair of(2)

∅n (ω) =

1if |ω| ≤ ωn ≤ τn

cos cos[π2β(

12τn

(|ω| − ωn − τn))]

ifωn − τn ≤ |ω| ≤ ωn + τn

0(1)

(2)

The detailed coefficients are given by the inner products with theempirical waveletsWs (n, t) = s,n = ∫ s (τ)ϕn (τ − t) dτ = f−1 [(s (ω)ϕn (ω))]

(3)

Where f-1 denotes the inverse Fourier transform. The reconstructedsignal is obtained as

(4)

Finally the extracted modes through EWT are given by

The EWT-based de-noising strategies have tons less computationalcomplexity and are greater efficient in comparison with the EMD-based absolutely de-noising techniques [5][7]. Adaptive filter[4]

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based powerline interference Cancellation techniques require a refer-ence sign for noise cancellation. The Empirical Wavelet Transform(EWT) aims to decompose a sign or an photograph on wavelet tightframes which might be built adaptively. After decomposition of sig-nal it sends to the adaptive clear out to dispose of the power lineinterference noise. For higher noise elimination we upload Bandbypass filter and Discrete Wavelet Transform based smootheningbefore the Adaptive filter out. The aggregate of EWT with Adap-tive filter [1] supply better effects when compared to EMD withadaptive filter out. To conquer the troubles of Adaptive clear outin the operation of eliminating the PLI noise from ECG sign we goto proposed method i.e. The use of of Extended Kalman Filter. Byusing this Extended Kalman filter we can reduce the interferenceas well as we can increase SNR compared to the state of the artmethods.

3 PROPOSED METHOD

Kalman filtering [8], which utilizes a state space show for the bois-terous flag and permits sufficient separation between the ECG flagand the irritation notwithstanding amid nonstationarities. A model,which was proposed to track the abundancy, recurrence and periodof a quasi stationarysinusoid, is utilized to diminish the Powerlineimpedance by including another state encoding the ECG flow. Inestimation concept, the prolonged Kalman Filter (EKF)[9] [10]isthe nonlinear version of the Kalman Filter which linearizes aboutan estimate of the modern-day imply and covariance. In the case ofnicely-defined transition fashions, the EKF has been taken into con-sideration thedefacto standard inside the idea of nonlinear countryestimation, navigation systems and GPS. The Extended KalmanFilter (EKF)is a nonlinear expansion of conventional Kalman Filterthat has been particularly produced for structures having nonlineardynamic models. For a discrete nonlinear framework with the king-dom vector xk and perception vector yk, the dynamic adaptationmight be figured as takes after:{xk+1 = f (xk, wk, k)yk = g (xk, vk, k)

(7)

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Where wk.and vk are The procedure and estimation commotionsseparately with covariance matrices

In order to apply a Kalman Filter formalism for this system ,it isnecessary to derive a linear approximation of equation(7) close toa preferred factors(xk, w k, v k)

(9)

(10)

The Extended Kalman filter (EKF) offers an approximation of theoptimal estimate. The non-linearitys of the systems dynamics areapproximated by a linearized Model of the non-linear machine ver-sion across the remaining country estimate. For this approximationto be legitimate, this linearization should be a terrific approxima-tion of the non-linear model in all the uncertainty domain associ-ated with the state estimate. In this proposed work we explain threemethods to Cast off power line interference from the ECG sign.Those are EWT+EKF, EWT+BPF+EKF, EWT+DWT+EKFThese three methods dispose of the PLI noise from ECG signal anddeliver better consequences over separately.Method 1:Powerline interference eliminated from ECG signal with the mix-ture of EKF along with EWT. Noisy ECG signal is given as inputfor EWT which transforms signal to wavelet domain and used asfeature extraction of ECG signal. EKF removes noisein ECG sig-nal and inverse EWT is applied to reconstruct ECG signal fromwavelet domain.

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Figure 2 Block diagram representing Method 1

Method 2:Here EWT is applied as feature extractor and EKF is applied alongwith BPF for better results. Band pass filter here acts as smooth-ing parameter for ECG signal. In method II BPF have been usedon estimated mode for smoothing. The estimate of the powerline-frequency obtained through EWT is passed through BPF beforefeeding it to the Extended Kalman Filter.


Method 3:Here we are implementing DWT for smoothing ECG signal andEWT as feature extractor EKF applied along with EWT and DWTyields better results compared to state-of art scenario. Universalthresh holding is applied on 3rd level detail coefficients of waveletdecomposition to obtain better estimates of power line interference,which is then feed to the Extended Kalman filter [10]. Here theestimated mode is decomposed using DWT up to level3.Coiflet-4wavelet is used in the decomposition structure because of the simi-larity of its scaling function to the shape of the QRS-complex.Theoutput from the EKF is de-noised signal that is PLI noise free sig-nal.


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In this method 3 the PLI noise ECG signal is de noised and also, weget the clean ECG signal better than the method 2. The Kalmanfilter has three parameters identified with the joining and clamordismissalabilities of the estimation. These parameters are the pro-cedure commotion covariance matrix Q, the estimation clamor co-variance matrix R and the underlying blunder covariance P. To ac-tualize the EKF, the time engendering is finished the utilization ofthe interesting nonlinear condition, in the meantime as the Kalmanget out pick up and the covariance matrix are ascertained from thelinearized conditions. The principle shortcomings of these relatedworks, based on least minimum squares, come from their lack ofrobustness in the estimation procedure and insufficient discrimina-tion between the ECG signal and the powerline interference [6].These factors motivate the application of more effective estimationalgorithms such as the Kalman filter [8], which could separate thesignal components from noise, and overcome the difficulties of non-stationarities. The procedure to extract the sinusoid from ECGinvolves the estimation of the clean ECG signal and the interferingsinusoid from the original noisy signal with the extended Kalmanfilter using the state space model described

4 RESULTS

We implement our algorithm on ECG signal which has taken fromphysio nets [11] MIT BIH database and we added power line inter-ference(PLI) as noise for our signal with frequency of 50 Hz. We canadd the different noise levels to the ECG signal like 10%,15%.,20%,25% etc. We prove that our algorithm yields better results byconsidering SNR and correlation coefficients as metrics for our pro-posed methodology. Two parameters are chosen for comparison:output signal-to-noiseratio (SNR) and correlation coefficient () be-tween clean ECG signal and the de-noised ECG signal. From themetrics table we observed that the SNR and Correlation coefficientvalues are better in the proposed method i.e. EWT with EKF com-pare to the EMD and EWT with Adaptive Filter for removing ofPLI noise from ECG signal.

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Figure 5 Original ECG Signal

Figure 5 shows that original ECG signal of patient. We add the 10% of noise to the original ECG signal. Figure 6 shows that NoisyECG signal by PLI.

Figure 6: ECG Signal with PLI, Figure 7: De Noised signalthrough EWT+EKF

Powerline interference can be removed from ECG signal by us-ing of EWT with EKF is shown in figure 7 and figure 8 showsthat PLI noise removing by using EWT and Band pass filter withEKF. Finally figure 9 shows that PLI noise free ECG signal throughEWT and Discrete Wavelet Transform with EKF. Comparison ofSNR and Correlation Coefficient for existing methods and proposedmethods are shown in the Table 1 and Table 2.

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Figure 80: Removing of PLI by EWT+BPF+EKF, Figure 9:Reduction of PLI in ECG Signal using EWT+DWT+EKF

Table-1 Comparison of SNR (dB) between EMD, EWT withAdaptive Filter(AF) and EWT with Extended Kalman

Filter(EKF)

Table-2 Comparison of Correlation Coefficient between EMD,EWT with Adaptive Filter(AF) and EWT with Extended

KalmanFilter(EKF)

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5 CONCLUSION

This paper presents the utility of Empirical wavelet Transform withExtended Kalman Filter for Power line reduction in ECG signal.A Shortcoming of EMD and EWT with Adaptive Filters were veryslow process and high complexity. We proposed a strategy for im-provement and structure keeping requirement to beat the confine-ments of Empirical Mode Decomposition and Empirical WaveletTransform with Adaptive Filter (EMD+ Adaptive Filter, EWT+Adaptive Filter). Experimental result shows that the proposed al-gorithm provides the better metric values and less computational-timeas compared with the EMD and EWT with Adaptive Filterbased de-noising methods.

References

[1] OmkarSinghand Ramesh kumarsunkaria dept. of Electronicsand Communication Engineering, National Institute of Tech-nology Jalandhar,Journal of medical engineering & TechnologyIndia 2015. ‘

[2] SnehalThalkar and Prof.Dhananjay Pusan Various techniquesfor removal of Power Line Interference from ECG signalDecember-2013

[3] Haykin S., ”Adaptive Filter Theory”, 3rdEdition, PrenticeHall, 2002.

[4] Empirical Wavelet Transform by Jerome Gilles IEEE Trans-form on Signal Processing, February 2013

[5] Agrawal, S., and Gupta, A., 2013, Fractal and EMD based re-movalof baseline wander and powerline interference from ECGsignals. Computers in Biology and Medicine, 43, pp: 18891899

[6] Mitov, I.P., 2004, A method for reduction of power line inter-ference in the ECG. Medical Engineering Physics, 26, 879887.

[7] Suchetha, M., and Kumaravel, N., 2013, Empirical modedecomposition-based filtering techniques for powerline inter-ference reduction in electrocardiogram using various adaptive

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structures and subtraction methods. Biomedical Signal Pro-cessing and Control, 8, pp:575585.

[8] Haykin S. Kalman Filtering and Neural Networks. Adaptiveand learning systems for signal processing,communications andcontrol, first edition. Wiley Interscience, 2001.

[9] Scala BL, Bitmead RR. Design of an extended Kalman filterfrequency tracker. IEEE Transactions Signal Processing Vol 44No 3, 1996.

[10] BittantiS, Savaresi SM. On the parameterization and design ofan extended Kalman filter frequency tracker. IEEE Transac-tions on Automatic Control Vol 45 No 9 , 2000.

[11] http://physionet.org

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Documents

ECG Signal De-noising by using Empirical Wavelet Transform ...Empirical Mode Decomposition, Band pass Filter(BPF), DiscreteWaveletTransform(DWT),EmpiricalWaveletTrans-form, Adaptive