ECG ARTIFACT REMOVAL FROM SURFACE EMG SIGNALS

BY COMBINING EMPIRICAL MODE DECOMPOSITION

AND INDEPENDENT COMPONENT ANALYSIS

Joachim Taelman, Bogdan Mijovic, Sabine Van Huffel

ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee, Belgium

Stéphanie Devuyst, Thierry Dutoit

TCTS Lab, Université de Mons, Mons, Belgium

Keywords: Single channel-blind source separation, Ensemble empirical mode decomposition, ECG interference artifact,

Data preprocessing.

Abstract: The electrocardiography (ECG) artifact in surface electromyography (sEMG) is a major source of noise

influencing the analyses. Moreover, in many cases the sEMG signal is the only available signal, making this

removal more complicated. We compare the performance of two recently described single channel blind

source separation methods with the commonly used template subtraction method on both simulations and

real-life data. These two methods decompose a single channel recording into a multichannel representation

before applying independent component analysis to these multichannel data. The decomposition methods

are the wavelet decomposition and ensemble empirical mode decomposition (EEMD). The EEMD based

single channel technique shows better performance compared to template subtraction and the wavelet based

alternative for both high and low signal-to-artifact ratio and for simulated and real-life data, but at the

expense of a higher computational load. We conclude that the EEMD based method has its potential in

eliminating spike-like artifacts in electrophysiological signals.

1 INTRODUCTION

The interference of the electrical activity of the heart

on the surface electromyography (sEMG) in the

shoulder girdle is a major source of noise

influencing its analysis. Several applications require

detection of small changes in sEMG signals (Zhou,

2006. There is certainly a need to remove the

electrocardiogram (ECG) artifact. In many cases

however, the sEMG is the only available signal,

making this task more complex.The difficulty of

ECG interference removal is mainly due to the large

overlap between the ECG interference spectrum and

that of the considered sEMG signal (0-75Hz for

ECG, 5-500Hz for sEMG).

A new trend in biomedical signal processing is

employing blind source separation (BSS) to unmix a

set of recorded signals into its original sources.

Independent Component Analysis (ICA) is one of

these BSS techniques assuming independency

between the sources. These techniques are only

applicable to multichannel data. Recently, several

approaches to extend this idea to single channel data

are published in the literature. A first approach,

single channel ICA (SCICA), was presented by

Davies and James (Davies, 2007). The original data

is chopped into several blocks of equal length and

ordered in a matrix before applying the ICA

algorithm. This algorithm separates successfully the

sources of interest provided they have perfect

disjoint spectra. The algorithm also requires

stationary data. Both limitations are not fulfilled in

this specific application. Another approach to enable

the use of ICA in single channel analysis is to

decompose the signal into a multichannel

representation before applying ICA. Several

decomposition methods exist. Mijovic et al

(Mijovic, 2010) combined ICA with either of two

decompositions, Ensemble Empirical Mode

Decomposition (EEMDICA) (Huang, 1998) and

wavelets (wICA), and compared their performance

421

Taelman J., Mijovic B., Van Huffel S., Devuyst S. and Dutoit T..

ECG ARTIFACT REMOVAL FROM SURFACE EMG SIGNALS BY COMBINING EMPIRICAL MODE DECOMPOSITION AND INDEPENDENT

COMPONENT ANALYSIS.

DOI: 10.5220/0003136804210424

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 421-424

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

with the SCICA method. The wICA method has

already been shown successful in removing the ECG

artifact (Azzerboni, 2004).

The aim of this paper is to verify whether

EEMD-ICA can be applied to single channel sEMG

excerpts to remove the ECG interference signal on a

bigger data set. Moreover, we compare its

performance to wICA and template subtraction,

which is to our opinion, still the golden standard in

removing the ECG artifact.

2 METHODS

2.1 Algorithms

2.1.1 Ensemble Empirical Mode

decomposition-Independent

Component Analysis (EEMD-ICA)

The idea behind the algorithm is to decompose a

single channel measurement into different

components before applying a blind source

separation technique. Here, the single channel is

decomposed using Ensemble Empirical Mode

Decomposition (EMD) before applying ICA

(Mijovic, 2010).

EMD (Huang, 1998) is a novel signal analysis

tool which is able to decompose any complicated

time series into a set of spectrally independent

oscillatory modes, called Intrinsic Mode Functions

(IMFs). In contrast with wavelets, EMD is a data

driven algorithm that decomposes the signal in a

natural way where no a priori knowledge about the

signal of interest embedded in the data series is

needed. The advantage of EMD is that this technique

is able to deal with nonstationary and nonlinear data.

A major drawback of the EMD algorithm is its

sensitivity to noise. Therefore, a more robust, noise-

assisted version of the EMD algorithm, called

Ensemble EMD (EEMD) (Huang, 1998) is used.

The algorithm defines the IMF set for an ensemble

of trials, each one obtained by applying EMD to the

signal of interest with added independent, identically

distributed white noise of the same standard

deviation (SD). The ratio of the noise SD to the SD

of the signal will be further referred to as a noise

parameter (np). These parameters were set to 0.2 for

the np and 100 for the number of trials. After EEMD

is performed and a set of averaged IMFs is derived,

independent component analysis (ICA) is applied.

The goal of ICA is to separate instantaneously

mixed signals from the channel matrix X into their

independent sources S, such that X = MS, where M

is called the mixing matrix, without prior

knowledge. We used FastICA algorithm, based on a

fixed-point iteration scheme for finding a maximum

of the non-Gaussianity of the sources is used

(Hyvarinen, 2000). ICA is applied to the whole set

of IMFs. The number of independent components in

FastICA to be extracted was set to 5 according to the

study by Mijovic et al (Mijovic, 2010).

Afterwards, the independent sources that

represent the ECG artifact signal are set to zero

before reconstruction of the cleaned sEMG signal

without the ECG contamination.

2.1.2 Wavelet-Independent Component

Analysis (wICA)

This algorithm is similar to the EEMD-ICA

algorithm, but the single channel signal is

decomposed into components of disjoint spectra

using the discrete wavelet decomposition instead of

EEMD. For this study we chose the Daubechies 6

wavelet, but similar conclusion holds for other

mother wavelets. The order of decompositions was

set to 8 according to a previous study (Taelman,

2007). The algorithm was originally proposed by

Azzerboni et al (Azzerboni, 2004), who decomposed

different, simultaneously recorded sEMG channels

via the discrete wavelet transform before performing

independent component analysis (ICA) to select

independent components of interest.

2.1.3 Template Subtraction

Template subtraction is a method that subtracts a

data driven template of the artifact from its

occurrence (Bartolo, 1996) in the signal. This

method has proven its ability to remove the ECG

contamination artifact in previous studies. The

algorithm involves three steps: localization of the

artifact, construction of the template and subtraction

of the template from the occurrence of the artifact.

This method is, to our opinion, still the golden

standard in removing the ECG artifact.

2.2 Data

The simulation signals are derived from real-life

contamination-free recordings. The sEMG signals

are 60 second excerpts from measurements of the

right M. Biceps Brachii, selected from three

different sEMG recordings at different contraction

levels. The ECG artifact templates were extracted

from representative real-life contaminated sEMG

measurements of the left and right M. trapezius.

Using these templates, seven artificially

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

422

contaminated ECG signals are generated. All

reference sEMG and artificial ECG signals are

normalized. By mixing up the reference sEMG

signals and the generated ECG signals for different

SNR, the simulation data set is defined.

To validate the simulation data, the relative root

mean square error (RRMSE) is calculated to

compare the performance of the different algorithms.

We were able to fully automate the EEMD-ICA

and wICA algorithms. After performing the ICA on

the signal decompositions by both algorithms, the

selection of the ECG sources needs to be done.

Since we have the artificial ECG signal available

during the analysis, the independent ECG sources

can be estimated by calculating the correlation

between the independent sources and the artificial

ECG signal. This correlation is high compared to

that between the non-ECG sources and the artificial

ECG signal.

3 RESULTS

Figure 1 shows a fragment of the original

contaminated sEMG data. After applying

EEMDICA, 5 independent components are derived

(Figure 2). The ECG sources correspond to sources

number 2 and 4. These two sources are set to zero

and the cleaned sEMG signal is reconstructed with

sources 1, 3 and 5. Figure 3 shows the sEMG signal

after reconstruction with removed ECG interference

sources. The ECG interference signal is visibly

removed completely and the sEMG signal shows

almost no distortion. These figures show that the

EEMD-ICA algorithm is able to remove the ECG

artifact.

Figure 1: Typical sEMG with the ECG interference signal.

The performance of the three algorithms on the data

set is presented in Figure 4. For a changing SNR the

results are presented with their mean and standard

error. The results of the simulations can be split up

in two parts. Around the SNR of 2dB, the

simulations reveal no difference between the three

algorithms with a relative RMS error close to 10%.

For SNR higher or lower, specific trends can be

seen. When looking at higher SNR, meaning that the

power in the sEMG signal is higher than the power

in the ECG interference signal, the RRMSE is lower

than 10% for all three algorithms. The error made by

the template subtraction and EEMD-ICA is similar

to each other and is lower compared to that of

wICA. For the lower SNR, both ICA based

algorithms perform much better compared to the

template subtraction resulting in a clearly lower

RRMSE from -5dB on.

Figure 2: 5 Independent sources after performing ICA on

the EEMD decomposition. Source 2 and 4 are related to

the ECG interference signal.

Figure 3: Cleaned sEMG after ECG interference removal.

Figure 4: Comparison of algorithm performances in

function of the RRMSE (in %) for the described

simulation. The results are presented as mean and standard

error for the different SNR.

4 DISCUSSION

Both ICA based methods are able to remove the

ECG artifact from the sEMG channel and perform

better compared to template subtraction as soon as

ECG ARTIFACT REMOVAL FROM SURFACE EMG SIGNALS BY COMBINING EMPIRICAL MODE

DECOMPOSITION AND INDEPENDENT COMPONENT ANALYSIS

423

the ECG artifacts become more dominant (lower

SNR). This can be explained by the limitations of

the template subtraction technique. The algorithm

uses the quasi-periodic property of the ECG artifact

but assumes a constant waveform of successive heart

beats. Furthermore, perfect localization of the

occurrence of the heart beat is needed. If one of

these assumptions is not fulfilled, the algorithm will

introduce subtraction artifacts. In reality, the

successive waveforms are slightly varying and

perfect localization in the sEMG signal itself is

difficult. Thus, the larger the ECG interference

signal is compared to the background sEMG signal,

the larger these subtraction artifacts are. This

explains the higher RRMSE for lower SNR. These

limitations do not hold for both ICA based

algorithms as these algorithms exploit statistical

properties of both underlying signals to separate

them.

The difference in performance between the

results of wICA and EEMD-ICA can be explained

via differences in decomposing the original signal.

The EEMD is a data-driven method and has a more

natural decomposition that is able to cope with

nonstationarities in the signal. Contrary to the

wavelet decomposition, the extracted intrinsic mode

functions can be spectrally overlapping. This leads

to a more natural selection of the independent

sources of the ICA afterwards, explaining the small

differences in favor for the EEMD-ICA.

A major drawback of the EEMD-ICA algorithm

is its computational cost. The empirical mode

decomposition is a data driven, iterative process of

selecting local maxima and minima for each

empirical mode. This is a computationally intensive

decomposition. The noise robust extension of EMD,

called ensemble EMD (EEMD), needs more time as

the algorithm ensembles the outcome of at least 100

trials of a single EMD. In contrary, the wavelet

decomposition is a straight-forward method based

on a predefined wavelet waveform. The

computational load of wICA is similar to that of

template subtraction, while EEMD-ICA is in the

order of 100 times slower. This high computational

load makes a real-time implementation impossible.

5 CONCLUSIONS

In this paper, we reported on applying EEMD-ICA

to remove the ECG interference signal from single

channel sEMG recordings. The algorithm shows

better performance compared to template subtraction

and wavelet based ICA for both high and low signal-

to-artifact, but at the expense of a high

computational load. We can conclude that this

method has great potential in eliminating spike-like

artifacts in electro-physiological signals.

ACKNOWLEDGEMENTS

Research supported by: Research Council KUL:

GOA Ambiorics, GOA MaNet, CoE EF/05/006

Optimization in Engineering (OPTEC), IDO 05/010

EEG-fMRI, IDO 08/013 Autism; Belgian Federal

Science Policy Office: IUAP P6/04 (DYSCO,

`Dynamical systems, control and optimization',

2007-2011; EU: FAST (FP6-MC-RTN-035801),

Neuromath (COST-BM0601)

REFERENCES

Azzerboni, B., Carpentieri, M., La Foresta, F., Morabito,

F. C., 2004. Neural-ICA and Wavelet Transform for

artifacts removal in EMG. In Proc. Of the IEEE

International Joint Conference on Neural Networks,.

4, 3223–3228.

Bartolo, A., Roberts, C., Dzwonczyk, R., Goldman, E.,

1996. Analysis of diaphragm EMG signals:

comparison of gating vs. subtraction for removal of

ECG contamination. In J. Appl. Physiol, 80, 1898–

1902.

Davies, M. E., James, C. J., 2007. Source separation using

single channel ICA. In Signal Processing, 87 (8), 1819

– 1832.

Huang, N. E., Wu, M. L., Long, S. R., Shen, S. S., Qu, W.

D., Gloersen, P., Fan, K. L., 1998. The Empirical

Mode Decomposition and the Hilbert spectrum for

nonlinear and non-stationary time series analysis. In

Proceedings of Royal Society Lond, 454A, 1971, 903–

993.

Hyvarinen, A., Oja, E., 2000. Independent Component

Analysis: algorithms and applications. In Neural

Networks, 13 (4-5), 411 – 430.

Mijovic, B., De Vos, M., Gligorijevic, I., Taelman J., Van

Huffel, S., 2010. Source separation from single-

channel recordings by combining empirical mode

decomposition and independent component analysis.

In IEEE transactions on biomedical engineering.

Accepted.

Taelman, J., Spaepen, A., Van Huffel, S., 2007. Wavelet

Independent Component Analysis to remove

electrocardiography contamination in surface

electromyography. In Proceedings of Engineering in

Medicine and Biology Society, 2007, pp. 682–685.

Zhou, P., Kuiken, T. A., 2006 Eliminating cardiac

contamination from myoelectric control signals

developed by targeted muscle reinnervation. In

Physiol Meas, 27, 1311–1327.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

424