Reducing electrocardiographic artifacts from electromyogram signals with independent component analysis

Costa, José Dilermando; Ferreira, Danton Diego; Nadal, Jurandir; de, A.M.F.L. Miranda

doi:10.1109/iembs.2010.5626507

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Blind source separation, particularly ICA, is a powerful tool to extract signal from noise measurements and has been employed for ECG interference removal from trunk EMG by previous studies [ 19 , 35 ]. By assuming the EMG and ECG signals to be independent, the ICA approach can be formulated as

where

is the observed six-channel EMG-ECG mixtures;

is a m -dimensional random vector with its components representing different independent sources, e.g., EMG, ECG, and other noise; and

is the

mixing matrix.…”

Section: Methodsmentioning

confidence: 99%

“…Several methods have been proposed for ECG removal from the trunk EMG, including gating (GT) [ 14 ], high-pass filtering (HP) [ 15 ], template subtraction (TS) [ 14 , 16 ], wavelet transform (WT) [ 10 , 17 ], adaptive filtering (AF) [ 18 ], and blind source separation (BSS) [ 19 ]. Unfortunately, each method has its own limitation.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Peri

Vullings

et al. 2020

Sensors

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Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

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where

is the observed six-channel EMG-ECG mixtures;

is a m -dimensional random vector with its components representing different independent sources, e.g., EMG, ECG, and other noise; and

is the

mixing matrix.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Peri

Vullings

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In [17], ICA components corresponding to ECG were determined using the periodic characteristics of cardiac pulses (RR interval). This process was applied on trunk, spinal lumbar and diaphragm muscles in [17][18][19][20][21]. However, ECG and EMG contributions are not perfectly separated with ICA, and, when ECG components are totally removed, EMG substantial values are lost.…”

Section: Introductionmentioning

confidence: 99%

Efficient procedure to remove ECG from sEMG with limited deteriorations: Extraction, quasi-periodic detection and cancellation

Nougarou

Massicotte

Descarreaux

2018

Biomedical Signal Processing and Control

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The interpretations of the surface electromyography (sEMG) signals from the trunk region are strongly distorted by the heart activity (ECG), especially in case of low-amplitude EMG responses analyses. Many methods have been investigated to resolve this nontrivial problem, by using advanced data processing on the overall sEMG recorded signal. However, if they reduce ECG artifacts, those cancellation methods also deteriorate noiseless parts of the signal. This work proposes an original ECG cancellation method designed to limit the deterioration of sEMG information. To do that, the proposed techniques does not directly attempt to remove the ECG, but is based on two main steps: the localization of ECG and the cancellation of ECG but only where heart pulses have been detected. The phase of localization efficiently extracts the ECG contribution by combining the discrete wavelet transforms (DWT) and the method of independent component analysis (ICA). And finally, this phase takes advantage of quasi-periodic properties of ECG signals to accurately detect pulses localization with an original algorithm based on the fast Fourier transform (FFT). Intensive simulations were achieved in terms of relative errors, coherence and accuracy for different levels of ECG interference. And the correlation coefficients computed from the paraspinal muscles EMG signals of 12 healthy participants were also used to evaluate the developed method. The results from simulation and real data demonstrate that the proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals, even when both signals are strongly overlapped, and greatly limits the deterioration of the EMG.

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Independent Component Analysis for Reducing Electrocardiographic Interference in the Multichannel Electromyogram

et al. 2011

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Reducing electrocardiographic artifacts from electromyogram signals with independent component analysis

Cited by 10 publications

References 16 publications

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Efficient procedure to remove ECG from sEMG with limited deteriorations: Extraction, quasi-periodic detection and cancellation

Independent Component Analysis for Reducing Electrocardiographic Interference in the Multichannel Electromyogram

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