Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms.
Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms.
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DOI：
10.1109/tmi.2018.2880092
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发表时间：
2019-05
影响因子：
10.6
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Alawad M;Wang L
构建数据驱动模型以定位来自12导联心电图（ECG）的心室激动的起源需要解决个体之间较大的解剖学和生理学差异的挑战。然而，患者特异性模型的替代方案难以在临床实践中实施，因为训练数据必须通过侵入性程序获得。在这里，我们提出了一种新的方法，克服了这个问题的临床数据的稀缺性，从一个大的病人特定的模拟数据集的知识转移，同时利用域适应，以解决模拟和临床数据之间的差异。我们已经开发的方法量化非均匀分布的模拟误差，然后将其纳入到域适应过程中的分类和回归的背景下。这产生了一个定量模型，该模型通过添加来自每个患者的12导联ECG数据，提供了心室激动起源的逐渐改善的患者特异性定位。我们评估了所提出的方法在三个体内室性早搏（PVC）患者的75个起搏部位的定位性能。我们发现，相对于单独在临床ECG数据上训练的模型或在不考虑域偏移的情况下在组合模拟和临床数据上训练的模型，所提出的模型显示出定位精度的提高。此外，我们证明了所提出的模型的能力，以提高实时预测的起源心室激动与每个添加的临床心电图数据，逐步引导临床医生对目标部位。
Building a data-driven model to localize the origin of ventricular activation from 12-lead electrocardiograms (ECG) requires addressing the challenge of large anatomical and physiological variations across individuals. The alternative of a patient-specific model is, however, difficult to implement in clinical practice because training data must be obtained through invasive procedures. Here, we present a novel approach that overcomes this problem of the scarcity of clinical data by transferring the knowledge from a large set of patient-specific simulation data while utilizing domain adaptation to address the discrepancy between simulation and clinical data. The method that we have developed quantifies non-uniformly distributed simulation errors, which are then incorporated into the process of domain adaptation in the context of both classification and regression. This yields a quantitative model that, with the addition of 12-lead ECG data from each patient, provides progressively improved patient-specific localizations of the origin of ventricular activation. We evaluated the performance of the presented method in localizing 75 pacing sites on three in-vivo premature ventricular contraction (PVC) patients. We found that the presented model showed an improvement in localization accuracy relative to a model trained on clinical ECG data alone or a model trained on combined simulation and clinical data without considering domain shift. Further, we demonstrated the ability of the presented model to improve the real-time prediction of the origin of ventricular activation with each added clinical ECG data, progressively guiding the clinician towards the target site.