IEEE Trans Biomed Eng. 2020 Oct;67(10):2905-2915. doi: 10.1109/TBME.2020.2973471. Epub 2020 Feb 12.
Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition.
We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data: Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data.
VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029 mV, yielding a median VFF attenuation of 142. In a different setup, only VFF data acquired at distances of more than 5 mm to the atrial endocardium were used to estimate the model parameters. The VFF component was then extrapolated for a layer of 5 mm thickness lining the endocardial tissue. A median deviation of 0.082 mV (median VFF attenuation of 49x) was achieved under realistic noise conditions.
It is feasible to model the VFF component in a personalized way and effectively remove it from uEGMs.
Application of our novel, simple and computationally inexpensive methods allows immediate diagnostic assessment of uEGM data without prolonging data acquisition.
在电解剖标测期间测量的心房内单极心内电图（uEGM）包含有关心脏兴奋和组织特性的诊断信息。心室远场（VFF）由心室去极化引起，会影响这些信号。当前的信号处理技术需要几秒钟的局部 uEGM 来去除 VFF 分量，从而延长临床标测过程。我们开发了一种使用在初始解剖采集期间获得的数据去除 VFF 分量的方法。
我们开发了两种模型，可以根据采集到的 EGM 数据来近似 VFF 分量的时空分布：多项式拟合和偶极子拟合。这两种模型都在两种人类心脏模型的心脏兴奋模拟中进行了基准测试，并应用于临床数据。
使用一个心房中的 VFF 数据来估计模型参数。在现实噪声条件下，偶极子模型以 0.029 mV 的中位数偏差来近似 VFF，从而产生 142 的 VFF 衰减中位数。在另一种设置中，仅使用距离心房心内膜超过 5mm 的 VFF 数据来估计模型参数。然后将 VFF 分量外推到覆盖心内膜组织的 5mm 厚的层中。在现实噪声条件下，实现了 0.082 mV 的中位数偏差（中位数 VFF 衰减 49x）。
以个性化方式对 VFF 分量进行建模并从 uEGM 中有效地去除是可行的。
我们的新型、简单且计算成本低的方法的应用允许立即对 uEGM 数据进行诊断评估，而不会延长数据采集时间。
