title: Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.
abstract: 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.
authors.affiliation: GIPSA-Lab, Department of Images and Signals, INPG, Grenoble Cedex, France. reza.sameni@gmail.com
authors.forename: R
authors.initials: R
authors.lastname: Sameni
authors.forename: M B
authors.initials: MB
authors.lastname: Shamsollahi
authors.forename: C
authors.initials: C
authors.lastname: Jutten
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001450:Ballistocardiography / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D053798:Magnetocardiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity