Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach.
Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach.
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DOI：
10.3389/fphys.2021.682446
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发表时间：
2021
影响因子：
4
通讯作者：
Bishop M
中科院分区：
文献类型：
--
作者：
Monaci S;Gillette K;Puyol-Antón E;Rajani R;Plank G;King A;Bishop M
背景：局灶性室性心动过速（VT）是一种危及生命的心律失常，是导致高发病率和心源性猝死（SCD）的原因。射频消融术是治疗持续性室性心动过速的唯一有效方法，但其成功与否取决于对室性心动过速源的准确定位，这是一种高侵入性且耗时的方法。目的：作为概念验证，我们研究的目标是证明利用心脏植入式电子设备（CIED）的电描记图（EGM）记录的可能性。为了实现这一目标，我们利用快速准确的整个躯干电生理（EP）模拟结合卷积神经网络（CNN），使用模拟EGM自动定位局灶性VT。材料和方法：使用高度详细的3D躯干模型来模拟4000个局灶性室性心动过速，这些室性心动过速均匀分布在左心室（LV）上，并利用快速反应-程函环境。随后将解决方案与躯干上的导联场计算相结合，以获得准确的心电图（ECG）和EGM轨迹，这些轨迹用作CNN的输入，以定位局灶源。我们比较了先前开发的CNN架构（基于笛卡尔概率）与我们利用通用心室坐标（UVC）的新型CNN算法的定位性能。结果：植入器械EGM成功定位VT源，定位误差（8.74 mm）与基于ECG的定位（6.69 mm）相当。我们的新型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 mm和8.07 mm）。总体而言，定位对噪声和身体成分的变化相对不敏感;然而，ECG电极和CIED导联的位移导致性能下降（误差16-25 mm）。结论：植入器械的EGM记录可用于成功、稳健地定位局灶性室性心动过速源，并辅助消融计划。
Background: 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. Objective: 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. Materials and Methods: 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). Results: 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). Conclusion: EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.
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影响因子：
3.8
作者：
Bayer, J. D.;Blake, R. C.;Plank, G.;Trayanova, N. A.
通讯作者：
Trayanova, N. A.
DOI：
10.1093/europace/euw346
发表时间：
2016-12
期刊：
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
影响因子：
--
作者：
Cardone-Noott L;Bueno-Orovio A;Mincholé A;Zemzemi N;Rodriguez B
通讯作者：
Rodriguez B
DOI：
10.1046/j.1540-8167.2003.03211.x
发表时间：
2003-12-01
影响因子：
2.7
作者：
Ito, S;Tada, H;Nogami, A
通讯作者：
Nogami, A
影响因子：
10.9
作者：
Gillette, Karli;Gsell, Matthias A. F.;Plank, Gernot
通讯作者：
Plank, Gernot
影响因子：
6.1
作者：
Raatikainen, M. J. Pekka;Arnar, David O.;Kuck, Karl-Heinz
通讯作者：
Kuck, Karl-Heinz