Bionic Wearable ECG with Multimodal Large Language Models: Coherent Temporal Modeling for Early Ischemia Warning and Reperfusion Risk Stratification.

Myocardial ischemia remains one of the principal causes of mortality and morbidity worldwide, necessitating novel approaches to facilitate early diagnosis and subsequent risk evaluation following reperfusion. Although advancements in wearables capable of ECG (electrocardiogram) monitoring have been initiated, these devices have encountered barriers due to limited capacities to encapsulate the temporally complex nature of ischemic events, notably in risk stratifying reperfusion injury. In this paper, we describe a framework that leverages bionic, wearable ECG sensor technologies along with multimodal large language models using a coherent temporal modeling effort to address the intertwining of fine-grained temporal dependencies, heterogeneous biomedical modalities, and interpretable risk stratification. Our temporally hierarchical fusion transformer utilizes a cross-granularity attention mechanism to model intrabeat, interbeat, and long-term dependencies all simultaneously. The validation of our system was carried out using 4 datasets across