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Article Dans Une Revue Physiological Measurement Année : 2008

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

Résumé

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.
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Dates et versions

hal-00285710 , version 1 (06-06-2008)

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Reza Sameni, Mohammad B. Shamsollahi, Christian Jutten. Model-based Bayesian filtering of cardiac contaminants from biomedical recordings. Physiological Measurement, 2008, 29 (5), pp.595-613. ⟨10.1088/0967-3334/29/5/006⟩. ⟨hal-00285710⟩
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