Application of kernel principal component analysis and support vector regression for reconstruction of cardiac transmembrane potentials
Application of kernel principal component analysis and support vector regression for reconstruction of cardiac transmembrane potentials
复制标题
核主成分分析和支持向量回归在心脏跨膜电位重建中的应用
DOI：
10.1088/0031-9155/56/6/013
复制
发表时间：
2011-03-21
影响因子：
3.5
通讯作者：
Crozier, Stuart
中科院分区：
文献类型：
--
作者：
Jiang, Mingfeng;Zhu, Lingyan;Crozier, Stuart
从体表电位（BSP）无创重建跨膜电位（TMP）构成了ECG逆问题的一种形式，该逆问题可以被视为具有多输入多输出的回归问题，并且可以使用支持向量回归（SVR）方法来求解。在开发有效的SVR模型时，特征提取是对原始输入数据进行预处理的重要任务。提出了将主成分分析（PCA）和核主成分分析（KPCA）应用于支持向量回归机（SVR）的特征提取方法。此外，遗传算法和单纯形优化方法被调用，以确定超参数的支持向量回归机。基于真实的心脏-躯干模型，采用等效双层源方法生成用于训练和测试支持向量回归模型的数据集。实验结果表明，结合特征提取的支持向量回归方法（PCA-SVR和KPCA-SVR）在重建外表面和内表面的TMP时，性能优于不提取特征提取的方法（单一SVR）。此外，与PCA-SVR相比，KPCA-SVR在重构TMP时具有更好的逼近能力和泛化能力。
Non-invasively reconstructing the transmembrane potentials (TMPs) from body surface potentials (BSPs) constitutes one form of the inverse ECG problem that can be treated as a regression problem with multi-inputs and multi-outputs, and which can be solved using the support vector regression (SVR) method. In developing an effective SVR model, feature extraction is an important task for pre-processing the original input data. This paper proposes the application of principal component analysis (PCA) and kernel principal component analysis (KPCA) to the SVR method for feature extraction. Also, the genetic algorithm and simplex optimization method is invoked to determine the hyper-parameters of the SVR. Based on the realistic heart-torso model, the equivalent double-layer source method is applied to generate the data set for training and testing the SVR model. The experimental results show that the SVR method with feature extraction (PCA-SVR and KPCA-SVR) can perform better than that without the extract feature extraction (single SVR) in terms of the reconstruction of the TMPs on epi- and endocardial surfaces. Moreover, compared with the PCA-SVR, the KPCA-SVR features good approximation and generalization ability when reconstructing the TMPs.