Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.
翻译:心脏的电动振动和恢复模型已变得越来越详细,但在临床环境中尚未经常使用,以指导病人的个性化干预。主要挑战之一是校准病人在标准临床程序期间可以进行的有限测量的模型。在这项工作中,我们提出了一个新框架,用于利用局部的心电振动性测量,对心脏左侧心电物理参数进行概率性校准。参数字段以高斯过程的形式在方块上标出,并通过从本地参数值到测量的替代功能与测量相连接。随后获得参数字段的外表分布。我们表明,我们的方法可以回收用于生成有效再相容期间本地合成测量的参数。我们的方法适用于通过临床协议收集的其他测量类型,更一般地适用于模型参数在多个不同的区域进行校准。