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Model-based Bayesian filtering of cardiac contaminants from biomedical recordings
, Article Physiological Measurement ; Volume 29, Issue 5 , 2008 , Pages 595-613 ; 09673334 (ISSN) ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
2008
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. © 2008 Institute of Physics and Engineering in Medicine