Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography. For serious cases of AFib, catheter ablations are performed to collect intracardiac electrograms (EGMs). EGMs offer intricately detailed and localized electrical activity of the heart and are an ideal modality for interpretable cardiac studies. Recent advancements in artificial intelligence (AI) has allowed some works to utilize deep learning frameworks to interpret EGMs during AFib. Additionally, language models (LMs) have shown exceptional performance in being able to generalize to unseen domains, especially in healthcare. In this study, we are the first to leverage pretrained LMs for finetuning of EGM interpolation and AFib classification via masked language modeling. We formulate the EGM as a textual sequence and present competitive performances on AFib classification compared against other representations. Lastly, we provide a comprehensive interpretability study to provide a multi-perspective intuition of the model's behavior, which could greatly benefit the clinical use.

翻译：理解心房颤动（AFib）的不规则电活动一直是心电图学的关键挑战。对于严重的AFib病例，临床通过导管消融术采集心内电图（EGM）。EGM能提供心脏高度精细且局部化的电活动信息，是可解释性心脏研究的理想模态。人工智能（AI）的最新进展使得部分研究能够利用深度学习框架解读AFib期间的EGM。此外，语言模型（LM）在泛化至未见领域方面展现出卓越性能，尤其在医疗健康领域。本研究首次利用预训练语言模型，通过掩码语言建模实现EGM插值与AFib分类的微调。我们将EGM表述为文本序列，并在AFib分类任务上取得了优于其他表示方法的性能表现。最后，我们提供了全面的可解释性研究，从多角度直观揭示模型行为机制，这对临床实践具有重要价值。