Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes

Acute Coronary Syndrome (ACS) is a life - threatening cardiovascular condition where early and accurate diagnosis is critical for effective treatment and improved patient outcomes. This study explores the use of ECG foundation models, specifically ST - MEM and ECG - FM, to enhance ACS risk assessment using prehospital ECG data collected in the ambulances . Both models leverage self - supervised learning (SSL), with ST - MEM using a reconstruction - based approach and ECG - FM employing contrastive learning, capt uring unique spatial and temporal ECG features. We evaluate the performance of these models individually and through a fusion approach, where their embeddings are combined for enhanced prediction. Results demonstrate that both foundation models outperform a baseline ResNet - 50 model, with the fusion - based approach achieving the highest perf ormance (AUROC: 0.843 0.006, AUCPR: 0.674 0.012). These findings highlight the potential of ECG foundation models for early ACS detection and motivate further exploration of advanced fusion strategies to maximize complementary feature utilization.