A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging.
A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging.
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无创心电成像最大间隔聚类方法与支持向量回归的混合模型
DOI：
10.1155/2012/436281
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发表时间：
2012
影响因子：
--
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Jiang M;Liu F;Wang Y;Shou G;Huang W;Zhang H
无创心电成像技术，如心肌跨膜电位（TMP）分布的重建，可以提供比体表电位（BSP）更详细、更复杂的电生理信息。然而，从BSP无创重建TMP是一个典型的逆问题。在这项研究中，这个逆心电图问题被视为一个回归问题与多输入（BSP）和多输出（TMP），这将是解决的最大间距聚类（MMC）支持向量回归（SVR）方法。首先，采用MMC方法对训练样本（一系列瞬时BSP）进行聚类，然后为每个聚类构建单独的SVR模型。对于每个测试样本，我们找到它的匹配聚类，然后使用相应的SVR模型来重建TMP。通过对测试样本的分析，发现MMC-SVR方法重构的TMP结果比单一的SVR方法重构的TMP结果更准确。MMC-SVR方法不仅提高了逆心电问题的求解精度，而且将训练样本分成小样本的聚类，提高了SVR模型训练的计算效率。
Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.
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影响因子：
4.6
作者：
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通讯作者：
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作者：
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作者：
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通讯作者：
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影响因子：
3.5
作者：
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通讯作者：
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