An Online Subspace Denoising Algorithm for Maternal ECG Removal from Fetal ECG Signals
Résumé
Noninvasive extraction of fetal electrocardiogram (fECG) from multichannel maternal abdomen recordings is an emerging technology used for fetal cardiac diagnosis. The strongest interference for the fECG is the maternal ECG (mECG), which is not totally removed through conventional methods including blind source separation (BSS). In this work, we address the problem of offline maternal cardiac signal removal and introduce an online subspace denoising procedure for mECG cancellation. The proposed method is a general online denoising framework, which can be used for the extraction of the signal subspace from noisy multichannel observations in low signal-to-noise ratios, using suitable prior information of the signal or noise. The method is fairly generic and may also be useful for the separation of other signals and noise even in the cases that BSS assumptions are not satisfied. The performance of the proposed technique is evaluated on both real and synthetic data and has shown significant outperformance as compared with the state-of-the-art methods.
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