Analysis of ECG Signal Denoising Algorithms in DWT and ... · Analysis of ECG Signal Denoising...

Analysis of ECG Signal Denoising Algorithms in

DWT and EEMD Domains

Neelam Bhardwaj1, Sanjeev Nara

2, Sunita Malik

1, and Geeta Singh

2

1Department of Electronics and Communication, Deenbandhu Chhotu Ram University of Science & Technology,

Murthal, Haryana, India 2Department of Biomedical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal,

Haryana, India

Email: {neelam.2149, sanjeevnara91}@gmail.com, {sunitamalik.ece, geetasingh.bme}@dcrustm.org

Abstract—Accurate analysis of ECG signals becomes

difficult when a lot of noise such as AC (Power line)

Interference, Electromyogram (EMG), Baseline wandering,

channel noise, electrode motion, motion artifact, Gaussian

noise & high frequency noise based on the frequency

variation are present in the ECG signal. Thus, for better

analysis and characterization of ECG, noise removal

becomes an essential part. Denoising of ECG signals plays a

very important role in diagnosis and detection of various

cardiovascular diseases. The various methods available for

denoising of ECG signals include linear filtering, Empirical

Mode Decomposition (EMD), Independent and Principal

Component Analysis, Neural networks, adaptive filtering etc.

In recent studies by several researchers compared to the

above mentioned denoising methods Discrete Wavelet

Transform (DWT) and Ensemble Empirical Mode

reducing noise from ECG signal. This paper presents the

performance analysis on ECG denoising algorithms in

EEMD and wavelet domains by evaluating Signal to Noise

Ratio (SNR) and Root Mean Square Error (RMSE) in order

to compare the effectiveness of these two methods in

reducing the noise.

Index Terms—EEMD, DWT, ECG denoising, SNR, RMSE

I. INTRODUCTION

Electrocardiography (ECG or EKG) is the electrical

activity of the heart externally recorded with the help of

skin surface electrodes. It is a non invasive recording

produced by an electrocardiographic device. Monitoring

of these signals has great clinical significance as they

characteristically precede the normal mechanical function

[1]. It provides valuable information about a wide range

of cardiac disorders such as presence of inactive part or

enlargement of heart muscle. Artifacts in ECG signal that

are extremely common include poor contact between skin

and electrode, electrical interference by outside sources,

electrical noise from the body and machine malfunction.

Removal of these artifacts is necessary to prevent false

interpretation of a heart's rhythm. Filtration of ECG

Signal is also important in order to extract important

features that are used for recognizing variability in heart’s

activity. Clean ECG signal provide detailed information

Manuscript received January 1, 2015; revised December 8, 2015.

about the electrophysiology of the heart diseases and

ischemic changes that may occur [2], [4]. It acts as

decision tool for clinical decision-making.

In order to extract efficient morphology of ECG

signals conventional filtering, Empirical Mode

Decomposition (EMD), Independent and Principal

Component Analysis, Neural networks, adaptive filtering

etc. are methods usually used for ECG denoising. Low

pass, high pass and band pass filtering are conventional

filtering techniques that are used to reduce undesired

artifacts only when noise spectrum is known. These

techniques are inefficient when the noise spectrum is

random in nature [2]. Linear filtering introduces the

ringing effect on the ECG signal analysis. In order to

rectify this limitation cubic spline filter is used for noise

removal from ECG signals [3]. Adaptive filtering well

known for its faster filter response is used for removal of

power line interferences but filtering it requires a

reference signal to be recorded together with the ECG [4].

Independent Component Analysis (ICA) is also employed

for removing the noises from physiological signals but it

does not allow the prior information about the signals for

efficient filtering [5]. Empirical Mode Decomposition

(EMD) is a versatile time–frequency data analysis

method for extracting signals from data synthesized in

noisy nonlinear and non-stationary processes. The

frequent appearance of mode mixing defined as a single

Intrinsic Mode Function (IMF) that either consists of

signals of widely disparate scales or a signal of a similar

scale residing in different IMF components is a major

drawback of this method [6].

Comparatively, the denoising methods based on

EEMD and that based on wavelet are considered to be

more effective and versatile in reducing noise from the

ECG signals. These methods are used to resolve the

limitations that are presented during efficient noise

removal from ECG signals using above mentioned

filtering methods. Ensemble Empirical mode

decomposition is a data analysis method which represents

substantial improvement over the EMD method. In it, by

using the statistical characteristics of white noise, the

ECG denoising is done. In wavelet transform, a signal is

analyzed as a linear combination of the sum of the

product of the wavelet coefficients and mother wavelet [7]

International Journal of Signal Processing Systems Vol. 4, No. 5, October 2016

©2016 Int. J. Sig. Process. Syst. 442doi: 10.18178/ijsps.4.5.442-445

Decomposition (EEMD) are found to be more effective in

and does not introduce any artificial information to the

original signal. However, selection of appropriate wavelet

function and thresholding method plays an important role

in signal denoising. In this paper, a comparison is carried out between

DWT and EEMD methods of ECG denoising in order to

determine their effectiveness reducing the noise. The

performance of methods under evaluation has been

verified by calculating the signal to noise ratio and root

mean square error.

II. METHODOLOGY

A. Participants

7 students were recruited as participants with 20-24

years of age. No exclusion criteria were used. Each

participant was provided written informed consent before

participating.

B. Instrumentation

For ECG data acquisition MP 100 system of BIOPAC

Inc. was used by utilizing AcqKnowledge 4.1.1 software

for data recording and storage. Disposable Electrodes

were placed on the subject's body to measure the

Electrocardiogram (ECG). Specifically Lead II

configuration was used in measuring ECG because it

closely resembles the normal pathway of current of flow

in the heart and therefore shows an upright complex with

an ideal signal [8]. Normal ECG measurements were then

obtained from each participant.

C. Processing of Signals

In order to carry out the comparison, processing of

signals was done through two methods - Discrete

Wavelet Transform and Ensemble Empirical mode

decomposition as described earlier:

1) Discrete wavelet transform

A wavelet transform is mathematical function used to

decompose a signal and provide us with time frequency

representation of signal. This time-frequency

representation is necessary so to be able to analyze

frequency components of signal as well as times at which

they occur. Discrete Wavelet Transform (DWT)

expresses a signal as a combination of sum of the product

of the wavelet coefficients and mother wavelet [7].

A family of mother wavelet is available having energy

spectrum as that of ECG signal. DWT decomposes a

signal into approximate and detail coefficients that helps

to scrutinize the signal at different frequency bands with

different resolutions. ECG signal denoising using DWT

consists of three steps as shown in Fig. 1.

Figure 1. Flowchart of DWT algorithm

The performance of ECG signal denoising using

Wavelet transform depends on the estimation of threshold

value, γ. This threshold value is proposed by Donoho and

Johnstone and is given as in (1).

= 2 logγ N (1)

where φ is standard deviation of detailed DWT

coefficients of wavelet level and N is the length of vector

of DWT coefficients [6]. After determination of γ,

thresholding can be divided into two categories-soft and

hard thresholding. In hard thresholding, signal values

smaller than γ are turned to zero while in soft

thresholding, signal values smaller than γ are turned to

zero and γ is subtracted from signal values greater than γ

that results in no discontinuity of the signal [3], [7]. The

implementation of DWT through MATLAB (2013a) is

shown in Fig. 2.

Figure 2. Implementation of DWT in MATLAB

2) Ensemble empirical mode decomposition

This method introduced by Huang et al. successfully

eliminates the mode mixing phenomenon and nature of

signals can be accurately reflected. EEMD is an adaptive

technique that is different from all previous methods of

data analysis as it is based on the decomposition of the

original data.

In this method, data are collected by separate

observations, each of which contains different noise in

order to improve the accuracy of measurements [9]. The

white noise added is treated as the possible random noise

that can be encountered in the measurement process and

helps in avoiding the mode mixing. The principle behind

this is very simple- the added white noise would populate

the whole time-frequency space uniformly with the

constituting components of different scales. When signal

is added to this uniformly distributed white background,

the bits of signal of different scales are automatically

projected onto proper scales of reference established by

the white noise in the background [10]. As a result, each

individual trial may produce very noisy outputs, for each

of the noise-added decompositions consists of the signal

and the added white noise. Since the noise in each trial is

different in separate trials, it is canceled out in the

ensemble mean [10], [11].


©2016 Int. J. Sig. Process. Syst. 443

The EEMD algorithm used for enhancement of ECG

signals and reduction of noise is shown in Fig. 3.

Figure 3. Flow chart of EEMD algorithm

This algorithm was implemented with the help of code

developed in MATLAB and the series of output are

shown in Fig. 4.

0 500 1000 1500 2000 2500 3000 3500 4000 4500

-0.5

0

0.5

1Original Signal

0 500 1000 1500 2000 2500 3000 3500 4000 4500-0.4

-0.2

0

0.2

0.4Residual Signal

0 500 1000 1500 2000 2500 3000 3500 4000 4500-1

-0.5

0

0.5

1received Signal

0 500 1000 1500 2000 2500 3000 3500 4000 4500-0.5

0

0.5

1Original Signal

Figure 4. Implementation of EEMD in MATLAB

III. RESULT

The main aim of this paper was to evaluate the

effectiveness of DWT and EEMD in signal enhancement.

The performance of the above two methods is evaluated

based on the Signal to Noise Ratio (SNR) and Root Mean

Square Error (RMSE). The SNR can be defined as in (2).

(2)

where x(t) is the signal and n(t) is the noise. RMSE is

defined as in (3).

(3)

where the numerator part is the square error, x ̂(t) is the

reconstructed ECG signal and M is the length of signal.

Figure 5. Comparison of SNR values

Fig. 5 shows the difference between SNR values of

DWT and EEMD denoising methods of ECG of different

subjects. This figure implies that the higher SNR is

Original Signal

Successive IMFs



obtained by using DWT denoising method for all the

ECG signals.

Fig. 6 represents the comparison of the RMSE results

obtained for the same group of ECG signals as used for

comparison of SNR values.

Figure 6. Comparison of RMSE values

It is vivid from Fig. 5 and Fig. 6 that for a particular

ECG signal, wavelet method yields the smallest RMSE

and higher SNR value that confirms its ability to provide

enhanced ECG signal with better quality.

IV. CONCLUSION

Many researchers have already revealed that DWT and

EEMD and DWT based denoising methods are more

effective in reduction of noise from ECG signals as

compared to conventional signal enhancing algorithms on

basis of their higher SNR and lower RMSE values. This

paper analyzed both denoising schemes and verifies their

effectiveness in noise reduction. The comparison carried

out between these two algorithms using SNR and RMSE

values reveal that ECG signal enhancement using wavelet

domain is far more effective than that of EEMD.

ACKNOWLEDGMENT

We are extremely grateful to the Department of

Biomedical Engineering, DCRUST, Murthal, Haryana for

allowing us to acquire signals in their lab.

Acknowledgement would be incomplete without the

mention of our student volunteers who enthusiastically

supported us by giving their ECG signals.

REFERENCES

[1] P. Karthikeyan, M. Murugappan, and S. Yaacob, “ECG signal

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[3] E. S. El-Dahshan, “Genetic algorithm and wavelet hybrid scheme for ECG signal denoising,” Telecommunication Systems, vol. 46,

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[4] Md. A. Kabir and C. Shahnaz, “An ECG signal denoising method based on enhancement algorithms in EMD and wavelet domains,”

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[6] Z. Zhao, Y. Luo, and Q. Lu, “Adaptive noise removal of ECG signal based on ensemble empirical mode decomposition,” in

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using wavelet transform,” WSEAS Transactions on Biology and

Biomedicine, vol. 7, no. 2, pp. 62-72, April 2010. [8] Task Force of the European Society of Cardiology and the North

American Society of Pacing and Electrophysiology, “Heart rate

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[9] N. E. Huang, et al., “The empirical mode composition and the Hilbert spectrum for nonlinear and non-stationary time series

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1998. [10] Z. Wu and N. E. Huang, “Ensemble empirical mode

decomposition: A noise-assisted data analysis method,” Advances

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Neelam Bhardwaj is a post graduate student in Electronics &

Communication at Deenbandhu Chhotu Ram University of Science and

Technology, Murthal, Sonepat, Haryana. She completed her Bachelors in Biomedical Engineering from Maharshi Dayanand University,

Rohtak in 2009. Her areas of interest are Biomedical Instrumentation

and Signal processing Email: [email protected]

Sanjeev Nara is a post graduate student in Biomedical Engineering at

Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana. He completed his Bachelors in Biomedical

Engineering from Maharshi Dayanand University, Rohtak in 2012. His

areas of interest are Medical Image processing, Biosignal processing and Medical Imaging.

Email: [email protected]

Ms. Sunita Malik is employed as Assistant Professor in Electronics &

Communication Engineering Department, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonepat), Haryana,

India. Her research area includes signal processing, VLSI, embedded

systems, and microcontrollers. She has contributed research papers in refereed national and international journals.


Dr. Geeta Singh is employed as Assistant Professor in Biomedical Engineering Department, Deenbandhu Chhotu Ram University of

Science and Technology, Murthal (Sonepat), Haryana, India. Her

research area includes Biomaterials and Polymers for drug delivery systems. She has contributed research paper in refereed national and

international journals.




mailto:[email protected]

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