发表机构
Imperial College London; Chonnam National University College of Medicine; St. George’s University Hospital; Cardiovascular & Genomics Research Institute; City St George’s University of London; National Heart & Lung Institute（帝国理工学院; 全南大学医学院; 圣乔治大学医院; 心血管与基因组学研究所; 伦敦城市圣乔治大学; 国家心肺研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发基于图神经网络的框架，通过合成电图信号训练实现心脏组织电生理特性的高效准确表征，在三类心脏异常检测中平均精度达0.95-0.97，经少样本微调可应用于曲面，具备临床应用潜力。
AI 中文摘要
从空间稀疏的心内测量数据中高效且准确地表征心脏组织的电生理特性，对定位消融靶点、改善心律失常治疗具有重要临床意义。我们开发了一种基于图神经网络的框架，该框架在二维平面上的合成电图信号上进行训练，用于识别室性早搏（PVC）消融场景中的感兴趣区域。我们的方法对单斑块纤维化、快速去极化和高兴奋性的检测平均精度分别达到0.96、0.97和0.95。训练后的模型可通过少样本微调应用于二维曲面，展现出其泛化能力。未来工作将进一步开发该框架，用于PVC消融的临床应用。
英文摘要
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.
CommentsAccepted at The Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop 2026
