The combination of Self-Organizing Feature Maps and support vector regression for solving the inverse ECG problem
The combination of Self-Organizing Feature Maps and support vector regression for solving the inverse ECG problem
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DOI：
10.1016/j.camwa.2013.09.010
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
M. Jiang;Yaming Wang;L. Xia;Feng Liu;Shanshan Jiang-;Wenqing Huang
中科院分区：
文献类型：
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作者：
M. Jiang;Yaming Wang;L. Xia;Feng Liu;Shanshan Jiang-;Wenqing Huang
心脏的无创电成像旨在从体表电位(BSP)中定量重建跨膜电位(TMP)，这是典型的逆问题。传统的心电逆问题是通过正则化技术来解决的。在本研究中，将其视为具有多输入和多输出的回归问题。然后采用支持向量回归(SVR)方法和自组织特征映射(SOFM)技术相结合的混合方法求解回归问题。混合SOFM-SVR方法分两步进行：首先使用SOFM算法对训练样本进行聚类，然后使用个体支持向量机方法构建回归模型。对于每个测试样本，聚类操作可以有效地提高回归算法的效率，并有助于建立相应的支持向量机模型进行TMPS重建。使用我们之前开发的逼真的心脏-躯干模型对所开发的SOFM-SVR模型的性能进行了测试。实验结果表明，与传统的单一支持向量机方法相比，该方法在解决心电逆问题时，可以减少训练时间，提高重建精度。
Noninvasive electrical imaging of the heart aims to quantitatively reconstruct transmembrane potentials (TMPs) from body surface potentials (BSPs), which is a typical inverse problem. Classically, electrocardiography (ECG) inverse problem is solved by regularization techniques. In this study, it is treated as a regression problem with multi-inputs (BSPs) and multi-outputs (TMPs). Then the resultant regression problem is solved by a hybrid method, which combines the support vector regression (SVR) method with self-organizing feature map (SOFM) techniques. The hybrid SOFM–SVR method conducts a two-step process: SOFM algorithm is used to cluster the training samples and the individual SVR method is employed to construct the regression model. For each testing sample, the cluster operation can effectively improve the efficiency of the regression algorithm, and also helps the setup of the corresponding SVR model for the TMPs reconstruction. The performance of the developed SOFM–SVR model is tested using our previously developed realistic heart-torso model. The experiment results show that, compared with traditional single SVR method in solving the inverse ECG problem, the proposed method can reduce the cost of training time and improve the reconstruction accuracy in solving the inverse ECG problem.