Leveraging Pretrained Language Models to Interpret Intracardiac Electrograms for Atrial Fibrillation Detection

This study demonstrates the effectiveness of using pretrained masked language models with textual representations for interpreting intracardiac electrograms, achieving impressive results in atrial fibrillation classification and signal interpolation.