ECG denoising using a dynamical model and a marginalized particle filter

Author

Lin, Chao ; Bugallo, Mónica ; Mailhes, Corinne ; Tourneret, Jean-Yves

Author_Institution

TeSA Lab., Toulouse, France

fYear

2011

fDate

6-9 Nov. 2011

Firstpage

1679

Lastpage

1683

Abstract

The development of robust ECG denoising techniques is important for automatic diagnoses of cardiac diseases. Based on a previously suggested nonlinear dynamic model for the generation of realistic synthetic ECG, we introduce a modified ECG dynamical model with 18 state variables to further include morphology variations. A marginalized particle filter is proposed for tracking this modified nonlinear state-space model which has linear substructures. Quantitative evaluations on the MIT-BIH database show that the proposed algorithm outperforms the extended Kalman filter-based algorithms and can better handle non-Gaussian distributions.

Keywords

Kalman filters; cardiology; diseases; electrocardiography; medical signal processing; particle filtering (numerical methods); patient diagnosis; signal denoising; MIT-BIH database; cardiac diseases automatic diagnosis; extended Kalman filter-based algorithms; linear substructures; marginalized particle filter; modified ECG dynamical model; nonGaussian distributions; nonlinear dynamic model; nonlinear state-space model; robust ECG denoising techniques; Electrocardiography; Equations; Kalman filters; Mathematical model; Noise measurement; Signal to noise ratio; ECG dynamical model; Marginalized particle filter; denoising; extended Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4673-0321-7

Type

conf

DOI

10.1109/ACSSC.2011.6190306

Filename

6190306