Brooks D H, Nikias C L, Siegel J H
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115.
J Biomed Eng. 1990 Nov;12(6):503-18. doi: 10.1016/0141-5425(90)90061-q.
The use of several mathematical methods for estimating epicardial ECG potentials from arrays of body surface potentials has been reported in the literature; most of these methods are based on least-squares reconstruction principles and operate in the time-space domain. In this paper we introduce a general Bayesian maximum a posteriori (MAP) framework for time domain inverse solutions in the presence of noise. The two most popular previously applied least-squares methods, constrained (regularized) least-squares and low-rank approximation through the singular value decomposition, are placed in this framework, each of them requiring the a priori knowledge of a 'regularization parameter', which defines the degree of smoothing to be applied to the inversion. Results of simulations using these two methods are presented; they compare the ability of each method to reconstruct epicardial potentials. We used the geometric configuration of the torso and internal organs of an individual subject as reconstructed from CT scans. The accuracy of each method at each epicardial location was tested as a function of measurement noise, the size and shape of the subarray of torso sensors, and the regularization parameter. We paid particular attention to an assessment of the potential of these methods for clinical use by testing the effect of using compact, small-size subarrays of torso potentials while maintaining a high degree of resolution on the epicardium.
文献中已报道了几种用于从体表电位阵列估计心外膜心电图电位的数学方法；这些方法大多基于最小二乘重建原理，并在时空域中运行。在本文中，我们介绍了一种在存在噪声的情况下用于时域逆解的通用贝叶斯最大后验（MAP）框架。之前应用最广泛的两种最小二乘法，即约束（正则化）最小二乘法和通过奇异值分解的低秩近似法，被置于此框架中，它们各自都需要“正则化参数”的先验知识，该参数定义了应用于反演的平滑程度。给出了使用这两种方法的模拟结果；它们比较了每种方法重建心外膜电位的能力。我们使用了从CT扫描重建的个体受试者的躯干和内部器官的几何构型。测试了每种方法在每个心外膜位置的准确性，将其作为测量噪声、躯干传感器子阵列的大小和形状以及正则化参数的函数。我们特别关注通过测试使用紧凑、小尺寸的躯干电位子阵列同时保持心外膜上的高分辨率程度来评估这些方法在临床应用中的潜力。
