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arXiv · 2502.17476

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

Zeyuan Meng·Lovely Yeswanth Panchumarthi·Saurabh Kataria·Alex Fedorov·Jessica Zègre-Hemsey·Xiao Hu·Ran Xiao
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Abstract

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.

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Zeyuan Meng, Lovely Yeswanth Panchumarthi, Saurabh Kataria, Alex Fedorov, Jessica Zègre-Hemsey, Xiao Hu, Ran Xiao. 2025-02-17. Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes. https://arxiv.org/abs/2502.17476

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