Filtering Noisy ECG Signals Using the Extended Kalman Filter Based on a Dynamic ECG Model

Reza Sameni (1, 2) , M.B. Shamsollahi (2) , Christian Jutten (1) , Massoud Babaie-Zadeh (3)

1 LIS - Laboratoire des images et des signaux
2 BiSIPL - Biomedical Signal and Image Processing Laboratory [Teheran]
3 MLab - Multimedia Lab.

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals.

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, Massoud Babaie-Zadeh. Filtering Noisy ECG Signals Using the Extended Kalman Filter Based on a Dynamic ECG Model. Proc. of the 32nd Annual International Conference on Computers in Cardiology, Lyon, France, Sep. 25-28, 2005, Sep 2005, Lyon, France. pp.1017-1020. ⟨hal-00174343⟩