Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study.
Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study.
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DOI：
10.3389/fphys.2018.00404
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发表时间：
2018
影响因子：
4
通讯作者：
Sebastian R
中科院分区：
文献类型：
--
作者：
Godoy EJ;Lozano M;García-Fernández I;Ferrer-Albero A;MacLeod R;Saiz J;Sebastian R
局灶性房性心动过速常用射频消融术治疗，长期疗效可接受。虽然异位病灶的位置往往出现在特定的热点，但它们实际上可以位于任何心房区域。多电极体表心电图系统允许获取密集体表电位图（BSPM），用于心律失常的非侵入性治疗计划。然而，心房的激活可能受到纤维化的影响，因此基于BSPM的生物标志物需要考虑这些影响。我们的目的是分析纤维化对BSPM衍生指数的影响，以及它在预测心房异位病灶位置方面的潜在应用。方法：我们开发了一个三维心房模型，包括5个不同阶段左心房斑块性纤维化的5种分布。每个阶段对应不同程度的纤维化，范围从2%到40%不等。由此产生的25个3D模型用于模拟局灶性房性心动过速（FAT），从临床研究中描述的19个不同位置触发。所有模拟都获得了BSPM，并计算了体表电位积分图（BSPiM）来描述心房激活。使用监督学习模型和支持向量机开发了机器学习（ML）管道，以学习475个激活序列中的每个激活序列的BSPM模式，并将它们与FAT源的起源联系起来。结果：纤维化超过15%的阶段的激活图谱受到很大影响，产生传导阻滞和传播延迟。BSPiMs并不总是聚集成不重叠的组，因为BSPiMs受到传导阻滞的高度改变。从第3期（15%纤维化）开始，在肺静脉周围放置异位搏动时，BSPiMs表现出差异。当使用足够多的电极来绘制躯干时，所有研究的配置的分类结果大多在84%以上。然而，纤维化的存在增加了异位病灶位置的面积，因此降低了电生理学家的效用。结论：结果表明，所提出的ML管道是一种很有前途的方法，即使在存在纤维化的情况下，也可以从BSPM信号中进行非侵入性异位病灶定位。
Introduction: 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. Methodology: 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. Results: 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. Conclusions: 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.
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影响因子：
6.1
作者：
Boyle, Patrick M.;Zahid, Sohail;Trayanova, Natalia A.
通讯作者：
Trayanova, Natalia A.
影响因子：
4.6
作者：
Giffard-Roisin, Sophie;Jackson, Thomas;Sermesant, Maxime
通讯作者：
Sermesant, Maxime
影响因子：
3.4
作者：
Maleckar, Mary M.;Greenstein, Joseph L.;Trayanova, Natalia A.
通讯作者：
Trayanova, Natalia A.
DOI：
10.1046/j.1460-9592.2002.00049.x
发表时间：
2002-01-01
影响因子：
1.8
作者：
Hoffmann, E;Reitmann, C;Steinbeck, G
通讯作者：
Steinbeck, G
影响因子：
3.8
作者：
GESELOWITZ, DB;MILLER, WT
通讯作者：
MILLER, WT