ECG denoising using a dynamical model and a marginalized particle filter

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

doi:10.1109/ACSSC.2011.6190306

ECG denoising using a dynamical model and a marginalized particle filter

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

Office of the Provost

TéSA Laboratory
Université Fédérale Toulouse Midi-Pyrénées

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

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.

Original language	English
Title of host publication	Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages	1679-1683
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2011.6190306
State	Published - 2011
Event	45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States Duration: Nov 6 2011 → Nov 9 2011

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Country/Territory	United States
City	Pacific Grove, CA
Period	11/6/11 → 11/9/11

Keywords

denoising
ECG dynamical model
extended Kalman filter
Marginalized particle filter

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10.1109/ACSSC.2011.6190306

Cite this

Lin, C., Bugallo, M., Mailhes, C., & Tourneret, J. Y. (2011). ECG denoising using a dynamical model and a marginalized particle filter. In Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 (pp. 1679-1683). Article 6190306 (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2011.6190306

@inproceedings{a0d1704379c048d0a0625ae983466220,

title = "ECG denoising using a dynamical model and a marginalized particle filter",

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 = "denoising, ECG dynamical model, extended Kalman filter, Marginalized particle filter",

author = "Chao Lin and M{\'o}nica Bugallo and Corinne Mailhes and Tourneret, \{Jean Yves\}",

year = "2011",

doi = "10.1109/ACSSC.2011.6190306",

language = "English",

isbn = "9781467303231",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "1679--1683",

booktitle = "Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011",

note = "45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 ; Conference date: 06-11-2011 Through 09-11-2011",

}

Lin, C, Bugallo, M, Mailhes, C & Tourneret, JY 2011, ECG denoising using a dynamical model and a marginalized particle filter. in Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011., 6190306, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1679-1683, 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011, Pacific Grove, CA, United States, 11/6/11. https://doi.org/10.1109/ACSSC.2011.6190306

ECG denoising using a dynamical model and a marginalized particle filter. / Lin, Chao; Bugallo, Mónica; Mailhes, Corinne et al.
Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011. 2011. p. 1679-1683 6190306 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - ECG denoising using a dynamical model and a marginalized particle filter

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AU - Bugallo, Mónica

AU - Mailhes, Corinne

AU - Tourneret, Jean Yves

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - denoising

KW - ECG dynamical model

KW - extended Kalman filter

KW - Marginalized particle filter

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U2 - 10.1109/ACSSC.2011.6190306

DO - 10.1109/ACSSC.2011.6190306

M3 - Conference contribution

AN - SCOPUS:84861325305

SN - 9781467303231

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1679

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BT - Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011

T2 - 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011

Y2 - 6 November 2011 through 9 November 2011

ER -

ECG denoising using a dynamical model and a marginalized particle filter

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