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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 158056, 9 pages
Research Article

Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem

1School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2The Dongfang College, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Received 9 May 2013; Revised 26 June 2013; Accepted 2 July 2013

Academic Editor: Kayvan Najarian

Copyright © 2013 Mingfeng Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs.