Monaci Sofia, Gillette Karli, Puyol-Antón Esther, Rajani Ronak, Plank Gernot, King Andrew, Bishop Martin
King's College London, London, United Kingdom.
Division of Biophysics, Medical University of Graz, Graz, Austria.
Front Physiol. 2021 Jul 1;12:682446. doi: 10.3389/fphys.2021.682446. eCollection 2021.
Focal ventricular tachycardia (VT) is a life-threating arrhythmia, responsible for high morbidity rates and sudden cardiac death (SCD). Radiofrequency ablation is the only curative therapy against incessant VT; however, its success is dependent on accurate localization of its source, which is highly invasive and time-consuming. The goal of our study is, as a proof of concept, to demonstrate the possibility of utilizing electrogram (EGM) recordings from cardiac implantable electronic devices (CIEDs). To achieve this, we utilize fast and accurate whole torso electrophysiological (EP) simulations in conjunction with convolutional neural networks (CNNs) to automate the localization of focal VTs using simulated EGMs. A highly detailed 3D torso model was used to simulate ∼4000 focal VTs, evenly distributed across the left ventricle (LV), utilizing a rapid reaction-eikonal environment. Solutions were subsequently combined with lead field computations on the torso to derive accurate electrocardiograms (ECGs) and EGM traces, which were used as inputs to CNNs to localize focal sources. We compared the localization performance of a previously developed CNN architecture (Cartesian probability-based) with our novel CNN algorithm utilizing universal ventricular coordinates (UVCs). Implanted device EGMs successfully localized VT sources with localization error (8.74 mm) comparable to ECG-based localization (6.69 mm). Our novel UVC CNN architecture outperformed the existing Cartesian probability-based algorithm (errors = 4.06 mm and 8.07 mm for ECGs and EGMs, respectively). Overall, localization was relatively insensitive to noise and changes in body compositions; however, displacements in ECG electrodes and CIED leads caused performance to decrease (errors 16-25 mm). EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.
局灶性室性心动过速（VT）是一种危及生命的心律失常，可导致高发病率和心源性猝死（SCD）。射频消融是治疗持续性VT的唯一有效疗法；然而，其成功取决于对其起源的准确定位，这具有高度侵入性且耗时。我们研究的目的是，作为概念验证，证明利用心脏植入式电子设备（CIED）的心电图（EGM）记录的可能性。为实现这一目标，我们将快速准确的全躯干电生理（EP）模拟与卷积神经网络（CNN）相结合，使用模拟EGM自动定位局灶性VT。使用高度详细的三维躯干模型，利用快速反应-光锥环境，模拟约4000个局灶性VT，均匀分布在左心室（LV）。随后将解与躯干上的导联场计算相结合，以得出准确的心电图（ECG）和EGM轨迹，这些被用作CNN的输入以定位局灶性起源。我们将先前开发的基于笛卡尔概率的CNN架构的定位性能与我们使用通用心室坐标（UVC）的新型CNN算法进行了比较。植入设备的EGM成功定位了VT起源，定位误差（8.74毫米）与基于ECG的定位（6.69毫米）相当。我们新颖的UVC CNN架构优于现有的基于笛卡尔概率的算法（ECG和EGM的误差分别为4.06毫米和8.07毫米）。总体而言，定位对噪声和身体成分变化相对不敏感；然而，ECG电极和CIED导联的位移会导致性能下降（误差为16 - 25毫米）。植入设备的EGM记录可用于成功且稳健地定位局灶性VT起源，并辅助消融计划。
