用图神经网络从稀疏心电数据中精准识别心律失常靶点。
Characterising cardiac tissue properties with graph neural networks
- 基于合成心电图信号，在2D平面训练图神经网络识别病灶区域。
- 对单片纤维化、快速去极化、高兴奋性检测平均精度达0.95以上。
- 可少量微调用于2D曲面，适合临床心律失常消融导航场景。
从空间稀疏的心内测量中高效准确地表征心肌组织的电生理特性，对定位消融靶点、改善心律失常治疗具有临床意义。我们开发了一种基于图神经网络的框架，利用二维平面的合成心电图信号训练，以识别早搏（PVCs）消融中的关注区域。该方法对单片纤维化、快速去极化和高兴奋性的检测平均精度分别为0.96、0.97和0.95。训练好的模型可通过少样本微调应用于二维曲面，展现出良好的泛化能力。未来工作将推进该框架在PVC消融中的临床应用。
原文摘要 · Abstract (English)
Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment. We developed a graph neural network-based framework trained on synthetic electrogram signals on 2D flat surfaces to identify areas of interest in the context of cardiac ablation for premature ventricular complexes (PVCs). Our method achieved an average precision of 0.96, 0.97, and 0.95 for the detection of single-patch fibrosis, rapid depolarisation and high excitability, respectively. The trained model can then be applied to 2D curved surfaces with few-shot fine-tuning, demonstrating its generalisation capability. Future work will develop this framework further for clinical use in PVC ablation.
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