Circuits and Systems (CAS) Group, Delft University of Technology, the Netherlands.
Circuits and Systems (CAS) Group, Delft University of Technology, the Netherlands.
Comput Biol Med. 2019 Apr;107:284-291. doi: 10.1016/j.compbiomed.2019.02.012. Epub 2019 Feb 21.
Finding the hidden parameters of the cardiac electrophysiological model would help to gain more insight on the mechanisms underlying atrial fibrillation, and subsequently, facilitate the diagnosis and treatment of the disease in later stages. In this work, we aim to estimate tissue conductivity from recorded electrograms as an indication of tissue (mal)functioning. To do so, we first develop a simple but effective forward model to replace the computationally intensive reaction-diffusion equations governing the electrical propagation in tissue. Using the simplified model, we present a compact matrix model for electrograms based on conductivity. Subsequently, we exploit the simplicity of the compact model to solve the ill-posed inverse problem of estimating tissue conductivity. The algorithm is demonstrated on simulated data as well as on clinically recorded data. The results show that the model allows to efficiently estimate the conductivity map. In addition, based on the estimated conductivity, realistic electrograms can be regenerated demonstrating the validity of the model.
寻找心脏电生理模型的隐藏参数有助于深入了解房颤的发生机制，从而在后期为疾病的诊断和治疗提供便利。在这项工作中，我们旨在通过记录的电图来估计组织的电导率，以作为组织（功能）障碍的指示。为此，我们首先开发了一个简单但有效的正向模型来替代控制组织中电传播的计算密集型反应扩散方程。使用简化模型，我们提出了一种基于电导率的紧凑矩阵模型来表示电图。随后，我们利用紧凑模型的简单性来解决估计组织电导率的不适定逆问题。该算法在模拟数据和临床记录数据上进行了验证。结果表明，该模型可以有效地估计电导率图。此外，基于估计的电导率，可以生成逼真的电图，证明了模型的有效性。
