Foetal PQRST extraction from ECG recordings using cyclostationarity-based source separation method

Haritopoulos, Michel; Capdessus, Cécile; Nandi, Asoke K.

doi:10.1109/iembs.2010.5627304

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…A cyclostationary signal is not strictly periodic, but some of its statistical properties are periodic. This same assumption of FECG cyclostationarity used in previous work [12], together with the a priori estimation of the foetus' fundamental cyclic frequency, provided very promising results with regard to the foetal PQRST extraction.…”

Section: A the Cyclostationarity Propertymentioning

confidence: 77%

Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings

et al. 2014

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Abstract-In this work, a novel method based on the cyclostationary properties of electrocardiogram (ECG) signals is introduced in order to classify independent subspaces into components reflecting the electrical activity of the foetal heart and those corresponding to mother's heartbeats, while the remaining ones are mainly due to noise. This research is inspired from multidimensional independent component analysis (MICA), a method that aims at grouping together into independent multidimensional components blind source separated signals from a set of observations. Given an input set of observations, independent component analysis (ICA) algorithms estimate the latent source signals which are mixed together. In the case of ECG recordings from the maternal thoracic and abdominal areas, the foetal ECGs (FECGs) are contaminated with maternal ECGs (MECG), electronic noise, and various artifacts (respiration, for example). When ICA-based methods are applied to these measurements, many of the output estimated sources have the same physiological origin: the mother's or the foetus' heartbeats. Thereby, we show that a procedure for automatic classification in independent subspaces of the extracted FECG and MECG components is feasible when using a criterion based on the cyclic coherence (CC) of the signal of interest.

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Section: A the Cyclostationarity Propertymentioning

confidence: 77%

Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings

et al. 2014

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“…By using skewness, cost function was defined by which weight vector updated and through this fetal ECG was extracted. Foetal electrocardiogram (FECG) extraction approach based on the cyclostationary properties of the signal of interest was carried out in [100].…”

Section: Other Methodsmentioning

confidence: 99%

Methodological Survey on Fetal ECG Extraction

Anisha¹,

Kumar²,

Benisha³

2014

IOSRJCE

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Fetal Electrocardiogram (FECG) signal, non-invasively taken from the Abdominal Electrocardiogram (AECG) of a pregnant woman is a efficient diagnostic tool for evaluating the health status of fetus. Clinically significant information in the Fetal Electrocardiogram signal is often masked by Maternal Electrocardiogram (MECG) considered as the most predominant interference, power line interference, and maternal Electromyogram (EMG), baseline wander etc. Fetal Electrocardiogram signal features may not be readily comprehensible by the visual or auditory systems of a human. Therefore Fetal Electrocardiogram should be extracted from composite Abdominal Electrocardiogram for clinical diagnosis. There are many powerful and well advanced methods for this purpose. A methodological study has been carried out to show the effectiveness of various methods which helps in understanding of Fetal ECG signal and its analysis procedures by providing valuable information.

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“…Recent applications are in the following subjects: analysis of genome signals [19], neuroscience [20], ballistocardiogram analysis [128], heart and lung sound separation [129], analysis of the embolic blood Doppler signal [132], foetal PQRST extraction from electrocardiogram (ECG) recordings [143], heart and respiration rates monitoring [183], heart sound signal selection [217], heart sound cancelation from lung sound [218], detection and characterization of a runner's fatigue [38,235], analysis of electromyographic signals [296], modeling and analysis of ground reaction force signals [97,300], and analysis of myoelectric signals [3].…”

Section: Biological Signalsmentioning

confidence: 99%