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 ambulances. Both models leverage self-supervised learning (SSL), with ST-MEM using a reconstruction-based approach and ECG-FM employing contrastive learning, capturing 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 performance (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.
View on arXiv@article{meng2025_2502.17476,
title={ Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes },
author={ Zeyuan Meng and Lovely Yeswanth Panchumarthi and Saurabh Kataria and Alex Fedorov and Jessica Zègre-Hemsey and Xiao Hu and Ran Xiao },
journal={arXiv preprint arXiv:2502.17476},
year={ 2025 }
}