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

• URL: http://arxiv.org/abs/2502.17476v1
• Date: Mon, 17 Feb 2025 04:50:56 GMT
• Title: Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes
• Authors: Zeyuan Meng, Lovely Yeswanth Panchumarthi, Saurabh Kataria, Alex Fedorov, Jessica Zègre-Hemsey, Xiao Hu, Ran Xiao,
• Abstract summary: This study explores the use of ECG foundation models, specifically ST-MEM and ECG-FM, to enhance ACS risk assessment.<n>We evaluate the performance of these models individually and through a fusion approach, where their embeddings are combined for enhanced prediction.
• Score: 5.723893680574976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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