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首页> 外文会议>33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by exploiting the spatial diversity of surface ECG
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Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by exploiting the spatial diversity of surface ECG

机译：利用表面心电图的空间多样性，对持续性心房纤颤导管消融结果进行非侵入性预测

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Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice. Radiofre-quency catheter ablation (CA) is becoming one of the most widely employed therapies. Yet selection of patients who will benefit from this treatment remains a challenging task. Previous works have examined several electrocardiogram (ECG) parameters as potential predictors of CA success, such as fibrillatory wave (f-wave) amplitude. However, they require a manual computation and consider only a subset of electrodes, so inter-lead spatial variability of the 12-lead ECG is not fully exploited. The present study puts forward an automatic procedure for f-wave amplitude computation to non-invasively predict CA outcome. An extension of this quantitative measure to the whole set of leads is also proposed, based on Principal Component Analysis (PCA). We show that exploiting the spatial diversity present in the surface ECG not only improves the robustness to electrode selection but also increases the predictive power of the amplitude parameter.
机译：心房颤动（AF）是临床上最常见的心律不齐。射频导管消融（CA）成为最广泛采用的疗法之一。然而，从中受益的患者选择仍然是一项艰巨的任务。先前的工作已经检查了几个心电图（ECG）参数，作为CA成功的潜在预测指标，例如颤动波（f波）幅度。但是，它们需要人工计算，并且只考虑电极的一个子集，因此未完全利用12导联ECG的导联间空间变异性。本研究提出了一种自动计算f波振幅的程序，以无创地预测CA结局。基于主成分分析（PCA），还提出了将这种定量方法扩展到整个潜在客户的方法。我们表明，利用表面ECG中存在的空间分集不仅可以提高对电极选择的鲁棒性，还可以提高幅度参数的预测能力。

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