• Physiological measurement · May 2008

    Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.

    • R Sameni, M B Shamsollahi, and C Jutten.
    • GIPSA-Lab, Department of Images and Signals, INPG, Grenoble Cedex, France. reza.sameni@gmail.com
    • Physiol Meas. 2008 May 1; 29 (5): 595-613.

    AbstractElectrocardiogram (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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