A Hybrid Model of Maximum Margin Clustering Method and Support Vector Regression for Noninvasive Electrocardiographic Imaging

Mingfeng Jiang,Wenqing Huang,Feng Liu,Yaming Wang,Huaxiong Zhang,Guofa Shou

doi:10.1155/2012/436281

Mingfeng Jiang, Wenqing Huang + Show 4 more

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https://doi.org/10.1155/2012/436281

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Abstract

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.

Highlights

The technique of noninvasive imaging of the heart’s electrical activity from the body surface potentials (BSPs) constitutes one form of the inverse problem of ECG [1, 2]
L1-norm regularization method can overcome this drawback of L2norm regularization method, which has been applied for epicardial potential reconstruction [13,14,15]
The inverse ECG solutions are shown in Figure 5; in contrast to the conventional regularization methods, such as zero order Tikhonov regularization method and LSQR regularization method, the single Support Vector Regression (SVR) method can yield rather better results with lower relative errors (REs) and higher CC

Summary

Introduction

The technique of noninvasive imaging of the heart’s electrical activity from the body surface potentials (BSPs) constitutes one form of the inverse problem of ECG [1, 2]. Approaches to solving the inverse ECG problem have been usually based on either an activation-based model or a potential-based model, which includes epicardial, endocardial, or transmembrane potentials. The potential-based models are used to evaluate the potential values on the cardiac surface [5,6,7] or within the myocardium [8] at certain time instants. We explore a new solution for ECG inverse problem using the potential-based approach. Due to its inherent ill-posed property, the inverse ECG problem is usually solved by “regularization” techniques. Numerous regularization methods have been proposed to solve this ill-posed problem, including truncated total least squares (TTLS) [9], GMRes [10], and the LSQR [11, 12].

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