The combination of self-organizing feature maps and support vector regression for solving the inverse ECG problem

Jiang, Mingfeng, Wang, Yaming, Xia, Ling, Liu, Feng, Jiang, Shanshan and Huang, Wenqing (2013). The combination of self-organizing feature maps and support vector regression for solving the inverse ECG problem. In: Proceedings - ICNC-FSKD 2012. The 2012 8th International Conference on Natural Computation The 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China, (1981-1990). 29 -31 May 2012. doi:10.1016/j.camwa.2013.09.010


Author Jiang, Mingfeng
Wang, Yaming
Xia, Ling
Liu, Feng
Jiang, Shanshan
Huang, Wenqing
Title of paper The combination of self-organizing feature maps and support vector regression for solving the inverse ECG problem
Conference name The 2012 8th International Conference on Natural Computation The 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery
Conference location Chongqing, China
Conference dates 29 -31 May 2012
Proceedings title Proceedings - ICNC-FSKD 2012   Check publisher's open access policy
Journal name Computers and Mathematics with Applications   Check publisher's open access policy
Place of Publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1016/j.camwa.2013.09.010
Open Access Status
ISSN 0898-1221
1873-7668
Volume 66
Issue 10
Start page 1981
End page 1990
Total pages 10
Collection year 2014
Language eng
Abstract/Summary Noninvasive electrical imaging of the heart aims to quantitatively reconstruct transmembrane potentials (TMPs) from body surface potentials (BSPs), which is a typical inverse problem. Classically, electrocardiography (ECG) inverse problem is solved by regularization techniques. In this study, it is treated as a regression problem with multi-inputs (BSPs) and multi-outputs (TMPs). Then the resultant regression problem is solved by a hybrid method, which combines the support vector regression (SVR) method with self-organizing feature map (SOFM) techniques. The hybrid SOFM-SVR method conducts a two-step process: SOFM algorithm is used to cluster the training samples and the individual SVR method is employed to construct the regression model. For each testing sample, the cluster operation can effectively improve the efficiency of the regression algorithm, and also helps the setup of the corresponding SVR model for the TMPs reconstruction. The performance of the developed SOFM-SVR model is tested using our previously developed realistic heart-torso model. The experiment results show that, compared with traditional single SVR method in solving the inverse ECG problem, the proposed method can reduce the cost of training time and improve the reconstruction accuracy in solving the inverse ECG problem.
Subjects 1703 Computational Theory and Mathematics
2611 Modelling and Simulation
2605 Computational Mathematics
Keyword Inverse ECG problem
Self organizing feature map
Support vector regression
Transmembrane potentials (TMPs)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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