A Nonlinear Bayesian Filtering Framework for ECG Denoising

Abstract : In this paper a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy ECG recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as band-pass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real non-stationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.
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Reza Sameni, Mohammad Shamsollahi, Christian Jutten, Gari Clifford. A Nonlinear Bayesian Filtering Framework for ECG Denoising. IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2007, 54 (12), pp.2172-85. ⟨hal-00174330⟩

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