title: Noninvasive reconstruction of cardiac transmembrane potentials using a kernelized extreme learning method.
abstract: Non-invasively reconstructing the cardiac transmembrane potentials (TMPs) from body surface potentials can act as a regression problem. The support vector regression (SVR) method is often used to solve the regression problem, however the computational complexity of the SVR training algorithm is usually intensive. In this paper, another learning algorithm, termed as extreme learning machine (ELM), is proposed to reconstruct the cardiac transmembrane potentials. Moreover, ELM can be extended to single-hidden layer feed forward neural networks with kernel matrix (kernelized ELM), which can achieve a good generalization performance at a fast learning speed. Based on the realistic heart-torso models, a normal and two abnormal ventricular activation cases are applied for training and testing the regression model. The experimental results show that the ELM method can perform a better regression ability than the single SVR method in terms of the TMPs reconstruction accuracy and reconstruction speed. Moreover, compared with the ELM method, the kernelized ELM method features a good approximation and generalization ability when reconstructing the TMPs.
authors: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors: Mingfeng
authors: M
authors: Jiang
authors: Heng
authors: H
authors: Zhang
authors: Lingyan
authors: L
authors: Zhu
authors: Li
authors: L
authors: Cao
authors: Yaming
authors: Y
authors: Wang
authors: Ling
authors: L
authors: Xia
authors: Yinglan
authors: Y
authors: Gong
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006321:Heart / Q000503:physiopathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008564:Membrane Potentials; D008955:Models, Cardiovascular; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012984:Software