A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging

Jiang, Mingfeng, Liu, Feng, Wang, Yaming, Shou, Guofa, Huang, Wenqing and Zhang, Huaxiong (2012) A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging. Computational and Mathematical Methods in Medicine, 2012 436281.1-436281.9.


Author Jiang, Mingfeng
Liu, Feng
Wang, Yaming
Shou, Guofa
Huang, Wenqing
Zhang, Huaxiong
Title A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging
Journal name Computational and Mathematical Methods in Medicine   Check publisher's open access policy
ISSN 1748-670X
1748-6718
Publication date 2012
Sub-type Article (original research)
DOI 10.1155/2012/436281
Volume 2012
Start page 436281.1
End page 436281.9
Total pages 9
Editor Dingchang Zheng
Place of publication New York, United States
Publisher Hindawi Publishing Corporation
Collection year 2013
Language eng
Abstract Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.
Keyword Inverse Problem
L1-Norm Regularization
Reconstruction
Activation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 436281.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
 
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