A Nonlinear Bayesian Filtering Framework for ECG Denoising

544 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning (2012, methodology): “Up to now, various signal-processing and machinelearning methods have been proposed to obtain FECGs, such as adaptive filtering [3], wavelet analysis [4], and blind source separation (BSS)/independent component analysis (ICA) [5].”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “Bayes filters are not directly implementable due to their complexity; however they are approximated through Kalman filters and particle filters, the former of which have been used for EEG artifact removal in for instance [78] and in a nonlinear fashion in [79].”
Artifact Removal in Physiological Signals—Practices and Possibilities (2012, methodology): “The EKF was also utilized by Sameni et al in 2005 [71] and 2007 [72] with simulated ECG signals.”