Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping
Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping
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DOI：
10.1109/tbme.2016.2629849
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发表时间：
2017-09-01
影响因子：
4.6
通讯作者：
Sermesant, Maxime
中科院分区：
文献类型：
--
作者：
Giffard-Roisin, Sophie;Jackson, Thomas;Sermesant, Maxime
目的：我们使用非侵入性数据(体表电位映射，BSPM)来个人化心脏电生理(EP)模型的主要参数，以预测不同起搏条件下的反应。方法：首先，将Mitchell-Schaeffer跨膜电位模型与电流偶极子模型相结合，提出了一个有效的正演模型。然后，我们估计了心脏模型的主要参数：激活起始位置和组织电导率。生成了模拟的BSPM的大型特定于患者的数据库，从中提取特定的特征来训练机器学习算法。根据Kernel Ridge回归计算激活起始位置，并且第二回归校准全局心室电导率。结果：结果的评估是基于一名室性早搏(PVC)患者的基准数据集和五名总共21种不同起搏条件的非缺血植入性心脏再同步治疗(CRT)患者。在起搏部位(平均距离误差为20.3 mm)、起搏部位(平均距离误差为21.7 mm)和CRT患者(平均距离误差为24.6 mm)方面，获得了良好的个性化结果。我们测试了个性化模型对双室起搏的预测能力，结果表明，根据BSPM信号，我们可以很好地预测新的电活动模式。结论：我们已经个性化了心脏EP模型，并预测了新的患者特有的起搏条件。意义：这是朝着无创术前预测不同起搏条件的反应迈出的令人鼓舞的第一步，以帮助临床医生选择CRT患者和制定治疗计划。
Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.