Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, the Netherlands.
Department of Cardiology, Erasmus University Medical Center, the Netherlands.
Comput Biol Med. 2021 Aug;135:104604. doi: 10.1016/j.compbiomed.2021.104604. Epub 2021 Jun 24.
Impaired electrical conduction has been shown to play an important role in the development of heart rhythm disorders. Being able to determine the conductivity is important to localize the arrhythmogenic substrate that causes abnormalities in atrial tissue. In this work, we present an algorithm to estimate the conductivity from epicardial electrograms (EGMs) using a high-resolution electrode array. With these arrays, it is possible to measure the propagation of the extracellular potential of the cardiac tissue at multiple positions simultaneously. Given this data, it is in principle possible to estimate the tissue conductivity. However, this is an ill-posed problem due to the large number of unknown parameters in the electrophysiological data model. In this paper, we make use of an effective method called confirmatory factor analysis (CFA), which we apply to the cross correlation matrix of the data to estimate the tissue conductivity. CFA comes with identifiability conditions that need to be satisfied to solve the problem, which is, in this case, estimation of the tissue conductivity. These identifiability conditions can be used to find the relationship between the desired resolution and the required amount of data. Numerical experiments on the simulated data demonstrate that the proposed method can localize the conduction blocks in the tissue and can also estimate the smoother variation in the conductivities. The conductivity values estimated from the clinical data are in line with the values reported in literature and the EGMs reconstructed based on the estimated parameters match well with the clinical EGMs.
电传导的损伤已被证明在心律失常的发展中起着重要作用。能够确定电导率对于定位引起心房组织异常的心律失常基质是很重要的。在这项工作中，我们提出了一种使用高分辨率电极阵列从心外膜电图（EGM）估计电导率的算法。有了这些阵列，就有可能同时在多个位置测量心脏组织的细胞外电势的传播。有了这些数据，从理论上讲，可以估计组织的电导率。然而，由于电生理数据模型中的未知参数数量众多，这是一个不适定的问题。在本文中，我们利用了一种称为验证性因素分析（CFA）的有效方法，将其应用于数据的互相关矩阵来估计组织的电导率。CFA 带有需要满足的可识别性条件，以解决问题，在这种情况下，就是估计组织的电导率。这些可识别性条件可用于确定所需分辨率与所需数据量之间的关系。对模拟数据的数值实验表明，所提出的方法可以定位组织中的传导块，并且还可以估计电导率的平滑变化。从临床数据估计的电导率值与文献中报道的值一致，并且基于估计的参数重建的 EGM 与临床 EGM 吻合良好。
