Godoy Eduardo Jorge, Lozano Miguel, García-Fernández Ignacio, Ferrer-Albero Ana, MacLeod Rob, Saiz Javier, Sebastian Rafael
Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain.
Department of Bioengineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States.
Front Physiol. 2018 May 18;9:404. doi: 10.3389/fphys.2018.00404. eCollection 2018.
Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria. We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source. Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist. The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.
局灶性房性心动过速通常采用射频消融治疗，长期成功率尚可。尽管异位灶的位置往往出现在特定的热点区域，但实际上它们可位于心房的任何区域。多电极体表心电图系统能够获取密集的体表电位图（BSPM），用于心律失常的无创治疗规划。然而，心房的激活可能会受到纤维化的影响，因此基于BSPM的生物标志物需要考虑这些影响。我们旨在分析纤维化对BSPM衍生指数的影响及其在预测心房异位灶位置方面的潜在应用。我们开发了一个三维心房模型，该模型包括左心房在5个不同阶段的5种斑片状纤维化分布。每个阶段对应不同程度的纤维化，范围从2%到40%。由此产生的25个三维模型用于模拟局灶性房性心动过速（FAT），其触发点来自临床研究中描述的19个不同位置。对所有模拟均获取了BSPM，并计算了体表电位积分图（BSPiM）以描述心房激活情况。开发了一种使用监督学习模型和支持向量机的机器学习（ML）管道，以学习475个激活序列中每个序列的BSPM模式，并将它们与FAT源的起源相关联。纤维化超过15%的阶段的激活图受到极大影响，产生传导阻滞和传播延迟。BSPiM并不总是聚集成不重叠的组，因为BSPiM会因传导阻滞而发生很大改变。从第3阶段（15%纤维化）开始，BSPiM显示出肺静脉区域周围异位搏动的差异。当使用足够数量的电极来绘制躯干图时，所有研究配置的分类结果大多高于84%。然而，纤维化的存在增加了异位灶定位的区域，因此降低了对电生理学家的效用。结果表明，所提出的ML管道是一种有前途的方法，即使存在纤维化，也可用于从BSPM信号进行无创异位灶定位。
