Model-based Bayesian filtering of cardiac contaminants

doi:10.1088/0967-3334/29/5/006

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

R Sameni et al 2008 Physiol. Meas. 29 595-613 doi: 10.1088/0967-3334/29/5/006

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R Sameni^1,2, M B Shamsollahi² and C Jutten¹
¹ GIPSA-Lab, Department of Images and Signals, INPG, 46 Avenue Félix Viallet, 38031 Grenoble Cedex, France
² Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
E-mail: reza.sameni@gmail.com, mbshams@sharif.ir and christian.jutten@inpg.fr

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.

Keywords: model-based filtering, ECG/MCG denoising, EEG denoising, EMG denoising, fetal ECG/MCG extraction

Print publication: Issue 5 (May 2008)
Received 17 December 2007, accepted for publication 2 April 2008
Published 7 May 2008

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Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

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