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学中的关键挑战。对于严重的心房颤动病例，需进行导管消融术以采集心内电图。心内电图能够提供心脏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式。近年来，人工智能的进展使得部分研究能够利用深度学习框架解读心房颤动期间的心内电图。此外，语言模型在泛化至未知领域（尤其是医疗领域）方面展现出卓越性能。本研究中，我们首次利用预训练语言模型，通过掩码语言建模对心内电图插值与心房颤动分类进行微调。我们将心内电图构建为文本序列，并在心房颤动分类任务中与其他表征方法相比展现出竞争性表现。最后，我们提供了全面的可解释性研究，从多视角揭示模型行为的内在机理，这将极大促进其临床应用。