Two Methods for Optimal MECG Elimination and FECG Detection from Skin Electrode Signals

Vanderschoot, J.; Callaerts, Dirk; Sansen, Willy; Vandewalle, Joos; Vantrappen, Gaston; Janssens, Jozef

doi:10.1109/tbme.1987.325949

Cited by 68 publications

(30 citation statements)

References 11 publications

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“…However, in general, there is no reason why bioelectrical sources of the heart should be spatially orthogonal to one another in the ECG. This orthogonality condition can only be forced through appropriate electrode placement, as was previously emphasized in the context of the fetal ECG extraction problem [26], [27] and the cancellation of artifacts in the electroencephalogram [16]. As a consequence, PCA is not expected to separate each source from the ECG with a quality similar to that of ICA.…”

Section: A Bss Principlesmentioning

confidence: 99%

Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation

Rieta¹,

Castells²,

Sánchez³

et al. 2004

IEEE Trans. Biomed. Eng.

230

196

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Abstract-This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA and VA present non-Gaussian distributions; and 3) the generation of the surface ECG potentials from the cardioelectric sources can be regarded as a narrow-band linear propagation process. To empirically endorse these claims, an ICA algorithm is applied to recordings from seven patients with persistent AF. We demonstrate that the AA source can be identified using a kurtosis-based reordering of the separated signals followed by spectral analysis of the sub-Gaussian sources. In contrast to traditional methods, the proposed BSS-based approach is able to obtain a unified AA signal by exploiting the atrial information present in every ECG lead, which results in an increased robustness with respect to electrode selection and placement.

show abstract

Section: A Bss Principlesmentioning

confidence: 99%

Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation

Rieta¹,

Castells²,

Sánchez³

et al. 2004

IEEE Trans. Biomed. Eng.

230

196

View full text Add to dashboard Cite

show abstract

“…Since its application to the ECG signal [6,7] the SVD method has been widely used in ECG morphology analysis. However, the effect of noise for measured parameters has not been systematically evaluated and tested.…”

Section: Discussionmentioning

confidence: 99%

“…The remaining components describe the non-dipolar components. The SVD method has been used to optimal MECG (mother ECG) elimination, FECG (foetal ECG) detection [6], to smooth and to find different T wave patterns [7], to eliminate noise [8] and separate dipolar and non-dipolar components [3] from the ECG data. The non-dipolar components have shown their usefulness in many studies [1,2,9 -11].…”

Section: Introductionmentioning

confidence: 99%

Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG

Lehtola

Karsikas

Koskinen

et al. 2008

Journal of Medical Engineering & Technology

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Singular value decomposition (SVD) based electrocardiogram (ECG) morphology analysis is a novel method in the assessment of subtle abnormalities in the T wave morphology of 12-lead ECG. As various types of noise contaminate the ECG signal and create a bias for the morphological analyses, this study was designed to estimate the effects of noise on the SVD method in an experimental setup. Ideal signals were generated by filtering real ECG signals several times with the Savitzky-Golay filter. Random and real noise samples were superimposed on the ideal signals. The noisy signals were filtered with a power line interference filter combined with the Savitzky-Golay or the wavelet filter. Results show that noise increased both the dipolar and non-dipolar components significantly unless filtering was applied. R-TWR (relative T wave residuum) and A-TWR (absolute T wave residuum) were four to eight times higher in noisy signals. The experiments with patient data demonstrated that certain types of noise may even lead to erroneous classification of patients. Filtering brings the median values closer to the correct ones and decreases significantly the variance of the values of parameters.

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“…At minimum repeatability contribution of the all left singular vectors is equal. The importance of 'time-orthogonality of left singular vectors and singular values concept' comes from the fact that left singular vectors represent a filter in space (Vanderschoot et al, 1987), so that the pattern of normalized singular value vector gives important information about repeatability of EMG LEs. The main purpose of the current study was to explore usability of the SVD based indices rather than comparative degree of them over VR.…”

Section: Discussionmentioning

confidence: 99%

Assessment of repeatability of surface electromyography signals by singular value decomposition

Soylu

2008

Journal of Electromyography and Kinesiology

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Two Methods for Optimal MECG Elimination and FECG Detection from Skin Electrode Signals

Cited by 68 publications

References 11 publications

Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation

Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation

Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG

Assessment of repeatability of surface electromyography signals by singular value decomposition

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