title: Study on parameter optimization for support vector regression in solving the inverse ECG problem.
abstract: 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.
authors: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China. m.jiang@zstu.edu.cn
authors: Mingfeng
authors: M
authors: Jiang
authors: Shanshan
authors: S
authors: Jiang
authors: Lingyan
authors: L
authors: Zhu
authors: Yaming
authors: Y
authors: Wang
authors: Wenqing
authors: W
authors: Huang
authors: Heng
authors: H
authors: Zhang
mesh_terms: D000465:Algorithms; D018780:Body Surface Potential Mapping / Q000706:statistics & numerical data; D003198:Computer Simulation; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006801:Humans; D008564:Membrane Potentials; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D060388:Support Vector Machine
references: Math Biosci. 2007 Dec;210(2):523-53
references: 17822722
references: IEEE Trans Neural Netw. 1999;10(5):988-99
references: 18252602
references: Phys Med Biol. 2011 Mar 21;56(6):1727-42
references: 21346274
references: IEEE Trans Neural Netw. 2002;13(5):1225-9
references: 18244520
references: Phys Med Biol. 2007 Mar 7;52(5):1277-94
references: 17301454
references: IEEE Trans Biomed Eng. 2008 Apr;55(4):1327-35
references: 18390323
references: Comput Methods Programs Biomed. 2012 Nov;108(2):570-9
references: 21968203
references: IEEE Trans Biomed Eng. 2009 Apr;56(4):923-31
references: 19272916
references: Ann Biomed Eng. 2003 Sep;31(8):981-94
references: 12918913
references: Nat Med. 2004 Apr;10(4):422-8
references: 15034569