Multi-Channel Electrocardiogram Denoising Using a Bayesian Filtering Framework

Reza Sameni (1, 2) , M.B. Shamsollahi (2) , Christian Jutten (1)

1 LIS - Laboratoire des images et des signaux
2 BiSIPL - Biomedical Signal and Image Processing Laboratory [Teheran]

In some recent works, model-based filtering approaches have been proved as effective methods for extracting ECG signals from single channel noisy recordings. The previously developed methods, use a highly realistic nonlinear ECG model for the construction of Bayesian filters. In this work, a multi-channel extension of the previous approach is developed, by using a three dimensional model of the cardiac dipole vector. The results have considerable improvement compared with the single channel approach. The method is hence believed to be applicable to low SNR multi-channel recordings.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Reza Sameni, M.B. Shamsollahi, Christian Jutten. Multi-Channel Electrocardiogram Denoising Using a Bayesian Filtering Framework. 33rd Annual International Conference on Computers in Cardiology, Sep 2006, Valencia, Spain. pp.185-188. ⟨hal-00174333⟩