标题： 基于集合卡尔曼滤波的房性电生理模型快速校准方法使用高斯过程模拟器 显示英文标题

标题： Rapid calibration of atrial electrophysiology models using Gaussian process emulators in the ensemble Kalman filter

摘要： 心房颤动（AF）是一种常见的心脏心律失常，其特征是心房中存在无序的电活动。 标准治疗方法是导管消融术，这是一种侵入性和不可逆的治疗方式。 计算电生理学的最新进展为患者特定的模型提供了可能性，这些模型通常被称为数字孪生体，可用于指导临床决策。 为了具有实际价值，我们必须能够使用常规的临床测量数据快速校准基于物理的模型。 我们将这个校准任务视为一个静态反问题，目标是从可用的观测数据中推断出组织水平的电生理参数。 为了使这个问题易于处理，我们用高斯过程模拟器（GPEs）替换了昂贵的前向模型，并提出了一种用于静态非线性反问题的集合卡尔曼滤波器（EnKF）的新颖适应方法。 该方法生成的参数样本可以被解释为来自后验分布的最佳高斯近似。 我们将结果与使用马尔可夫链蒙特卡洛（MCMC）采样获得的结果进行比较，并证明了该方法在实现接近实时的患者特定校准方面的潜力，这是在临床时间尺度内预测AF治疗结果的关键步骤。 该方法可以广泛应用于科学和工程领域的各种静态反问题。 显示英文摘要

摘要： Atrial fibrillation (AF) is a common cardiac arrhythmia characterised by disordered electrical activity in the atria. The standard treatment is catheter ablation, which is invasive and irreversible. Recent advances in computational electrophysiology offer the potential for patient-specific models, often referred to as digital twins, that can be used to guide clinical decisions. To be of practical value, we must be able to rapidly calibrate physics-based models using routine clinical measurements. We pose this calibration task as a static inverse problem, where the goal is to infer tissue-level electrophysiological parameters from the available observations. To make this tractable, we replace the expensive forward model with Gaussian process emulators (GPEs), and propose a novel adaptation of the ensemble Kalman filter (EnKF) for static non-linear inverse problems. The approach yields parameter samples that can be interpreted as coming from the best Gaussian approximation of the posterior distribution. We compare our results with those obtained using Markov chain Monte Carlo (MCMC) sampling and demonstrate the potential of the approach to enable near-real-time patient-specific calibration, a key step towards predicting outcomes of AF treatment within clinical timescales. The approach is readily applicable to a wide range of static inverse problems in science and engineering.