Purpose

Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology.

Methods

19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with $\left|DF-HDF\right|\le 1Hz$ were segmented and characterized (size, area); the spatial distribution of the differences $|DF-atrialHDFestimate|$ was qualitatively analyzed. Phase singularity points (SPs) were detected on maps generated with Hilbert transform after band-pass filtering around the HDF (±1Hz). Connected SPs along time (filaments) and their histogram (heatmaps) were used for rotational activity characterization (duration, spatiotemporal stability). Results were reproduced in clinical layouts (252-12 leads) and with different rotations and translations of the atria within the torso, and compared with the original 567-lead outcomes using structural similarity index (SSIM) between maps, sensitivity and precision in SP detection and direct feature comparison. A random forest and least-square based algorithms were used to classify the arrhythmias and their mechanisms’ location, respectively, based on the obtained features.

Results

Frequency and phase analyses revealed distinct behavior between arrhythmias. AT and AFL presented uniform DF maps with low variance, while AF maps were more heterogeneous. Lower differences from the atrial HDF regions correlated with the driver location. Rotational activity was most stable in AFL, followed by AT and AF. Features were robust to lower spatial resolution layouts and modifications in the atrial geometry; DF and heatmaps presented decreasing SSIM along the layouts. The classification of the arrhythmias and their mechanisms’ location achieved balanced accuracy of 72.0% and 73.9%, respectively.

Conclusion

Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.

中文翻译：

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体表电位测绘中房性心律失常的特征：一项计算研究。

目的

房性心动过速（AT），扑动（AFL）和颤动（AF）是非常常见的心脏心律不齐，并且由可能是消融目标的局部来源驱动。非侵入性身体表面电位测绘（BSPM）对于早期诊断和消融计划可能很有用。我们旨在使用反映电生理的基本特征，从BSPM角度表征和区分AT，AFL和AF背后的心律失常机制。

方法

使用567导联BSPM的19个仿真来获得主频（DF）图，并使用最高DF（HDF）估算心房驱动频率。具有的地区$\left|dF-HdF\right|\le \mathrm{1个}Hž$进行细分和特征化（大小，面积）；差异的空间分布$|dF-\mathrm{一种}Ť\mathrm{[R}\mathrm{一世}\mathrm{一种}升HdFËsŤ\mathrm{一世}米\mathrm{一种}ŤË|$定性分析。在HDF（±1Hz）附近进行带通滤波后，在通过希尔伯特变换生成的地图上检测到相位奇异点（SP）。沿时间（细丝）连接的SP及其直方图（热图）用于表征旋转活动（持续时间，时空稳定性）。结果以临床布局（252-12条导联）进行再现，躯干内心房的旋转和平移不同，并使用地图之间的结构相似性指数（SSIM），SP检测的灵敏度和精度与原始567条导联进行比较和直接功能比较。基于获得的特征，使用随机森林和基于最小二乘的算法分别对心律失常及其机制的位置进行分类。

结果

频率和相位分析揭示了心律不齐之间的不同行为。AT和AFL呈现出具有低方差的均匀DF映射，而AF映射则更加异构。与心房HDF区域的差异较小，与驾驶员位置相关。旋转活动在AFL中最稳定，其次是AT和AF。这些功能对于降低空间分辨率的布局和修改心房几何形状非常有用；DF和热图显示沿布局的SSIM减少。心律失常的分类及其机制的位置分别达到了72.0％和73.9％的平衡准确度。

结论

基于现实模型的AT，AFL和AF的无创性特征突出了心律不齐之间的内在差异，从而增强了BSPM作为辅助临床工具的效用。