title: ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter.
abstract: In this paper an efficient filtering procedure based on the Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics.
authors.affiliation: Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran. osayadi@ee.sharif.edu
authors.forename: Omid
authors.initials: O
authors.lastname: Sayadi
authors.forename: Reza
authors.initials: R
authors.lastname: Sameni
authors.forename: Mohammad B
authors.initials: MB
authors.lastname: Shamsollahi
mesh_terms: D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D008954:Models, Biological; D012815:Signal Processing, Computer-Assisted; D012984:Software