Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA.
Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Europace. 2021 Mar 4;23(23 Suppl 1):i88-i95. doi: 10.1093/europace/euaa397.
Ventricular activation patterns can aid clinical decision-making directly by providing spatial information on cardiac electrical activation or indirectly through derived clinical indices. The aim of this work was to derive an atlas of the major modes of variation of ventricular activation from model-predicted 3D bi-ventricular activation time distributions and to relate these modes to corresponding vectorcardiograms (VCGs). We investigated how the resulting dimensionality reduction can improve and accelerate the estimation of activation patterns from surface electrogram measurements.
Atlases of activation time (AT) and VCGs were derived using principal component analysis on a dataset of simulated electrophysiology simulations computed on eight patient-specific bi-ventricular geometries. The atlases provided significant dimensionality reduction, and the modes of variation in the two atlases described similar features. Utility of the atlases was assessed by resolving clinical waveforms against them and the VCG atlas was able to accurately reconstruct the patient VCGs with fewer than 10 modes. A sensitivity analysis between the two atlases was performed by calculating a compact Jacobian. Finally, VCGs generated by varying AT atlas modes were compared with clinical VCGs to estimate patient-specific activation maps, and the resulting errors between the clinical and atlas-based VCGs were less than those from more computationally expensive method.
Atlases of activation and VCGs represent a new method of identifying and relating the features of these high-dimensional signals that capture the major sources of variation between patients and may aid in identifying novel clinical indices of arrhythmia risk or therapeutic outcome.
心室激活模式可以通过提供心脏电激活的空间信息，或者通过衍生的临床指数，直接为临床决策提供帮助。本研究旨在从模型预测的三维双心室激活时间分布中得出心室激活主要变化模式的图谱，并将这些模式与相应的心向量图（VCG）相关联。我们研究了由此产生的降维如何能够改善和加速从表面电图测量中估计激活模式。
使用主成分分析（PCA），对基于八个患者特定双心室几何结构的模拟电生理模拟数据集，对激活时间（AT）和 VCG 图谱进行了分析。图谱提供了显著的降维，并且两个图谱中的变化模式描述了相似的特征。通过与它们对抗来解析临床波形，评估了图谱的实用性，并且仅使用不到 10 个模式，VCG 图谱就能够准确地重建患者的 VCG。通过计算紧凑雅可比矩阵，对两个图谱之间进行了敏感性分析。最后，通过改变 AT 图谱模式生成的 VCG 与临床 VCG 进行比较，以估计患者特异性激活图，并且临床和基于图谱的 VCG 之间的误差小于更昂贵的计算方法。
激活和 VCG 图谱代表了一种新的方法，可以识别和关联这些高维信号的特征，这些特征捕获了患者之间的主要变异性来源，并且可能有助于识别心律失常风险或治疗效果的新临床指数。
