Multiscale Adaptive Basis Function Modeling of Spatiotemporal Vectorcardiogram Signals
Multiscale Adaptive Basis Function Modeling of Spatiotemporal Vectorcardiogram Signals
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DOI：
10.1109/jbhi.2013.2243842
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发表时间：
2013-03-01
影响因子：
7.7
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Liu, Gang;Yang, Hui
心脏电信号的数学建模有助于模拟真实的心脏电行为、评估算法以及表征潜在的时空模式。然而，存在与模型功效、鲁棒性和通用性相关的实际问题。本文提出了一种多尺度自适应基函数建模方法，不仅可以描述心向量（VCG）信号的时间特性，而且可以描述其空间特性。模型参数自适应估计的“最佳匹配”的VCG特征波的投影到字典的非线性基函数。通过实验评估了模型性能，包括基函数的数量、不同类型的基函数（即，高斯、墨西哥帽、定制小波和厄米特小波）和各种心脏状况，包括80名健康对照和不同的心肌梗死（即，89例下、77例前间隔、56例前外侧、47例前和43例前外侧）。多因素方差分析表明，基函数和模型复杂度对模型性能有显著影响，而心脏状况对模型性能影响不显著。定制的小波被发现是一个最佳的基函数的建模的空时VCG信号。QT间期的比较显示较小的相对误差（
Mathematical modeling of cardiac electrical signals facilitates the simulation of realistic cardiac electrical behaviors, the evaluation of algorithms, and the characterization of underlying space-time patterns. However, there are practical issues pertinent to model efficacy, robustness, and generality. This paper presents a multiscale adaptive basis function modeling approach to characterize not only temporal but also spatial behaviors of vectorcardiogram (VCG) signals. Model parameters are adaptively estimated by the "best matching" projections of VCG characteristic waves onto a dictionary of nonlinear basis functions. The model performance is experimentally evaluated with respect to the number of basis functions, different types of basis function (i.e., Gaussian, Mexican hat, customized wavelet, and Hermitian wavelets), and various cardiac conditions, including 80 healthy controls and differentmyocardial infarctions (i.e., 89 inferior, 77 anterior-septal, 56 inferior-lateral, 47 anterior, and 43 anterior-lateral). Multiway analysis of variance shows that the basis function and the model complexity have significant effects on model performances while cardiac conditions are not significant. The customized wavelet is found to be an optimal basis function for the modeling of space-time VCG signals. The comparison of QT intervals shows small relative errors (