The Application of Support Vector Regression Method for Solving the Inverse ECG Problem

Abstract:

The problem of noninvasive computing the epicardial surface potentials from torso surface potentials constitutes one form of the inverse problem of ECG, which can be acted as a regression problem with multi-input and multi-output. In this study, the SVR method is invoked to predict the inverse solutions, which compared with the common regularization methods. To build an effective SVR model, the hyper-parameters of SVR are set carefully by using the grid search optimization method. The experiment results shows that SVR method is an effective way for solving the inverse ECG problem, which can reconstruct more accurate epicardial surface potentials distribution than the common regularization method, such as Tikhonv method and LSQR method.

• Periodical: Advanced Materials Research (Volumes 108-111)
• Pages: 828-833
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.108-111.828
• Online since: May 2010
• Authors: Ming Feng Jiang, Chen Qun Wang, Wen Qing Huang, Xia Cai
• Keywords: ECG, Inverse Problem, Regularization Method, SVR

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