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.

A New ISTA-net Solution to Inverse Problem of Electrocardiology

“…Fig. 2 The tth layer of iteration network After all the training processes, the results and parameters of the neural network can be preserved as the exclusive solving tool for the ecg inverse problem of this conductor model of human thoracic volume [10], which avoid the complex and imprecise L-curve method to calculate the parameter [11], and can obtain better generalization ability and more accurate results.…”