wavelet based emg artifact removal from ecg signal...based cstd technique. r.shantha selva kumari,...

Journal of Engineering, Computers & Applied Sciences (JEC&AS) ISSN No: 2319-5606 Volume 2, No.8, August 2013 _________________________________________________________________________________

www.borjournals.com Blue Ocean Research Journals 55

Wavelet Based EMG Artifact Removal From ECG Signal

Josy Joy, Department of Electronics and Instrumentation Engineering, Karunya University, Coimbatore,

Tamilnadu, INDIA

P.Manimegalai, Department of Electronics and Instrumentation Engineering, Karunya University, Coimbatore,

Tamilnadu, INDIA

ABSTRACT Electrocardiogram recordings (ECG) are obtained from the heart. Some sections of the recorded ECG may be

corrupted by electromyography (EMG) noise from the muscle. In real situations, exercise test ECG recordings and

long term recordings, are often corrupted by muscle artifacts. These EMG noise needs to be filtered before data

processing. In this paper, wavelet transform is applied to remove the EMG noise from ECG signal. In this work, an

improved thresholding is proposed for removing EMG noise in ECG signal. The proposed method selects the

best suitable wavelet function based on DWT at the decomposition level of 5, using SNR. The advantage of the

improved thresholding de-noising method is that it retains both the geometrical characteristics of the original

ECG signal and variations in the amplitudes of various ECG waveforms effectively.

Keywords- ECG, EMG, DWT, THRESHOLDING, REALTIME, LabVIEW, MATLAB

Introduction Electrocardiogram (ECG) signal, the electrical

interpretation of the cardiac muscle activity, is very

easy to interfere with different noises while gathering

and recording. The most troublesome noise sources

are the Electromyogram (EMG) signal. Such noises

are difficult to removing typical filtering procedures.

The EMG, a high frequency component, is due to the

random contraction of muscles, while the abrupt

transients are due to sudden movement of the body.

Furthermore, the non-stationarybehaviour of the

ECG signal, that becomes severe in the cardiac

anomaly case, incites researchers to analyze the ECG

signal. Wavelet thresholding de-noising method

based on discrete wavelet transform (DWT) proposed

by Donoho et al. is often used in de-noising of ECG

signal [4, 5]. In 1999, Agnate used it in de-noising of

ECG signal.

In real situations, exercise test ECG recordings

and long term recording are often corrupted by

muscle artifacts due to restlessness of patients. In

such cases, it is not possible to ensure relaxed

conditions for the patient and muscular activity is

reflected.

Automatic interpretation, which is strongly

dependent on accurate detection of characteristics

ECG points and waves and measurement of signal

parameters, becomes an extremely difficult and often

virtually impossible task. EMG artifacts so obtained

from the same electrodes as the ECG are difficult to

remove, due to considerable overlapping of the

frequency spectra of these two types of signals.

EMG artifacts in ECG are quite common with

uncontrollable tremor, in disabled persons having to

exert effort in maintaining a position of their

extremities or a body posture, in children .These

EMG noise needs to be filters before data processing.

Adequate ECG denoising algorithms and

procedures should

a) Improve the signal to noise ratio (SNR) to obtain

clean and readily observable recordings, allowing the

subsequent use of straightforward approaches for

correct automatic detection of its specific waves and

complexes.

b) Preserve the original shape of the signal and

especially the amplitudes of sharp Q, R and S peaks,

without introducing distortions in the low- amplitude

ST-segment and P- and T-waves.

Wavelet thresholding de-noising methods deals

with wavelet coefficients using a suitable chosen

threshold value in advance. The wavelet coefficients

at different scales could be obtained by taking

DWT of the noisy signal. Normally, those wavelet

coefficients with smaller magnitudes than the

preset threshold are caused by the noise and are

replaced by zero, and the others with larger

magnitudes than the preset threshold are caused by

original signal mainly and kept (hard-thresholding

case) or shrunk (the soft-thresholding case). Then the

denoised signal could be reconstructed from the

resulting wavelet coefficients. These methods are

simple and easy to be used in de-noising of ECG

signal. But hard-thresholding de-noising method may



lead to the oscillation of the reconstructed ECG

signal and the soft-thresholding de-noising method

may reduce the amplitudes of ECG waveforms, and

especially reduce the amplitudes of the R waves. To

overcome the above said disadvantages an improved

thresholding de-noising method is proposed.

Methods ECG signal is easy to be contaminated by random

noises uncorrelated with the ECG signal, such as

EMG which can be approximated by a white

Gaussian noise source.

a) Decomposing of the noisy signals using

wavelet transform

Using the discrete wavelet transform by selecting

mother wavelet (db8), the noisy signal is

decomposed, at the decomposition level of 5. As a

result approximate coefficients and detail coefficients

are obtained.

b) Apply thresholding: It is done in order to obtain the estimated wavelet

coefficients For each level a threshold value is found,

and it is applied for the detailed coefficients.

1) Hard-Thresholding Method:

Where preset threshold is

Tj=σ√2log||dj|| ......... (a)

The σ can be estimated by the wavelet coefficients

with

σ = median d / 0.6745 .......... (b)

Here median denotes the median value of the

absolute values of wavelet detailed coefficients.

c) Reconstruction: Reconstructing the de-noised ECG signal x (n) by

inverse discrete wavelet transform (IDWT).

The same steps are to be followed for soft

thresholding, and improved thresholding de-

noising methods.

2) Soft-thresholding method:

3) Improved thresholding method:

Where β > 1 and ∈ R . Because the magnitudes of the

wavelet coefficients related to the Gauss white noise

decreases as the scale j increases, hence the threshold

value will be chosen as

Tj=σ√21log||dj||/log (j+1) ........ (c)

For each level, find the threshold value that gives the

minimum error between the detailed coefficients of

the noisy signal and those of original signal. The

improved wavelet thresholding denoising method has

the following characteristics:

It makes the reconstructed ECG signal remain the

characteristics of the original ECG signal and keep

the amplitudes of R waves effectively.

Equation (3) will be equivalent to hard-thresholding

when β → ∞ and will be equivalent to soft-

thresholding, when β →1. This shows that the

improved threshold denoising method can be adapted

to both hard- and soft-thresholding de-noising

methods. Therefore, the improved thresholding de-

noising method could be regarded as a compromise

between the hard- and soft-thresholding denoising

methods. So, that the improved thresholding

denosing method presented in this paper could

choose an appropriate β by trail –and-error to satisfy

the request of de-noising of the ECG signal from

EMG artifacts.

Evaluation Criteria We utilize output SNR value between the

constructed de-noised ECG signal and the original

ECG signal with DC offset rejected x(n) (the

reference ECG signal to evaluate our method.

Determination of SNR Criteria:

The output SNR is given by



Results To validate the superiority of the proposed improved

thresholding de-noising method, ECG signal in MIT-

BIH database is intercepted to be the original ECG

signal. The length of the original signal ECG signal

(i.e., the number of the sample points) is N=1000.

Gauss white noise is added to the original ECG

signal, the noisy ECG signal. Noise reduction

Procedures were implemented in Mat lab 7.0.1.

Thresholding was performed by trial and error

method.

The proposed method is based on choosing threshold

value by finding output SNR of denoised signal and

original wavelet sub signal (coefficients). Therefore,

high quality denoised signal can be accomplished.

Our study establishes particular approach to fit ECG

signal that has nonstationary clinical information. To

preserve the distinct ECG waves and different low

pass frequency shapes, the method thresholds

detailed wavelet coefficients only.

Fig1: De-noising of ECG signal from EMG noise using threshold methods with DB8:

Fig.2 shows the SNR values for different threshold

values:

Conclusion The wavelet transforms allow processing of non-

stationary signals such as ECG signal. The proposed

method shows a new experimental threshold value

for each decomposition level of wavelet detailed

coefficients.

This improved thresholding de-noising method in this

paper is superior to other traditional thresholding

denoising methods in many aspects. It retains

geometrical characteristics. This can be done in Lab

View.

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0 500 1000 1500-8

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contaminated ecg sinal with emg noise

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1st level decomposition

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4th level decomposition

0 10 20 30 40 50-5

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5th level decomposition

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Detail D5

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ecg signal after thresholding

Mother wavelet

Thresholding method

Threshold value

SNR

Db8 Improved

thresholding .05 59.517

Db8 Improved


Db8 Improved


Db8 Improved




N.nikolaev, institute of information technologies,

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wavelet based emg artifact removal from ecg signal...based cstd technique. r.shantha selva kumari,...

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