Interpretation of Intracardiac Electrograms Through Textual Representations
This addresses the challenge of understanding irregular electrical activity in AFib for clinical applications, representing an incremental advance by applying existing language model techniques to a new domain.
The paper tackles the problem of interpreting intracardiac electrograms (EGMs) for atrial fibrillation (AFib) by formulating EGMs as textual sequences and using pretrained language models for finetuning, achieving competitive performance on AFib classification and providing interpretability insights for clinical use.
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.