Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation

Zarzoso, Vicente; Nandi, Asoke K.

doi:10.1109/10.900244

Cited by 254 publications

(136 citation statements)

References 14 publications

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“…Many ICA methods have been adapted in order to fit the FECG extraction problem. The multireference adaptive noise cancelling (MRANC) method issued from the adaptive filtering solution by Widrow et al [4] and its adaptation to the FECG extraction problem is presented in [5]. In this work, the authors compare the results provided by the MRANC method with the ones obtained after application of BSS techniques.…”

Section: Introductionmentioning

confidence: 92%

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

Roussel

Haritopoulos

2015

IFMBE Proceedings

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Abstract-In this paper, the authors propose to estimate the contribution of the electrical activity of the foetal heart to each component of cutaneous electrocardiogram (ECG) recordings using an oblique projection technique. To date, related research work reported in the literature using Blind Source Separation (BSS) or Independent Component Analysis (ICA) methods rely on a widely used projection techniques family, which is the orthogonal projection. More recently, reported work in other areas of biomedical research is based on the use of oblique projection techniques in order to remove the artifacts from the available recordings and to estimate the contribution of the Signal of Interest (SoI) to the data set. The novel approach presented in this paper is tailored to the problem of foetal ECG (FECG) contribution estimation. The optimal steering vector's computation procedure for the oblique projection is based on the use of the cyclostationary properties of the SoI and on the concept of Multidimensional Independent Component Analysis (MICA). The latter is used to gather into independent subspaces the components estimated using a BSS/ICA method and corresponding to the foetus' heartbeats, the mother's heartbeats and various artifacts and noise sources. Early comparative results with the orthogonal approach obtained after application of both methods to synthetic non-invasive FECG recordings illustrate the reliability as well as the effectiveness of the proposed method. A discussion and perspectives for future research conclude this work.

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Section: Introductionmentioning

confidence: 92%

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

Roussel

Haritopoulos

2015

IFMBE Proceedings

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“…En tenant compte de ces hypothèses, le problème a été formalisé dans [46] D'autres travaux ont aussi abordé ce problème en utilisant les méthodes de SAS [34], [48], [128]. Dans [34], les auteurs ont mis en oeuvre l'algorithme de SAS qu'ils ont proposé dans [35].…”

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Séparation aveugle de sources en ingénierie biomédicale

Kachenoura

Albera

Senhadji

2007

IRBM

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Blind Source Separation (BSS) now raises great interest. In fact, BSS plays an important role in many diverse application areas, including medical diagnosis (separation of electroencephalogram signals for instance).Consequently, the BSS problem is addressed in this paper through i) the description of mathematical tools, ii) the bibliographical survey of classical methods and biomedical applications, and iii) the detailed presentation of six BSS algorithms.

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“…ICA decomposes the mixed signals into as many statistically independent components as possible. ICA has been used to extract FECG [2][3][4] [5]. However, very little literature is available on its performance in clinical environment.…”

Section: Introductionmentioning

confidence: 99%

Performance of Optimized Generalized Weighted Estimator ICA algorithm on Biomedical Signals Contaminated by Noise

Parmar¹,

Uhhelkar²

2010

IJCA

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This paper evaluates the performance of OGWE (Optimized Generalized Weighted Estimator) ICA (Independent Component Analysis) algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) are generated and then mixed linearly in the presence of white or pink noise to simulate a recording of electrocardiogram. While ICA has been used to extract FECG, very little literature is available on its performance in clinical environment. So there is a need to evaluate performance of these algorithms in Biomedical. To quantify the performance of OGWE algorithm, two scenarios, i.e., (a) different amplitude ratios of simulated maternal and fetal ECG signals, (b) different values of additive white Gaussian noise or pink noise, were investigated. Higher order and second order performances were measured by performance index and signal-to-error ratio respectively. The selected ICA algorithm separates the white and pink noises equally well. This paper reports on the performance of the ICA algorithm.

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Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation

Cited by 254 publications

References 14 publications

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

Séparation aveugle de sources en ingénierie biomédicale

Performance of Optimized Generalized Weighted Estimator ICA algorithm on Biomedical Signals Contaminated by Noise

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