Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909509267488768 |
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| author | Meng, Zeyuan Panchumarthi, Lovely Yeswanth Kataria, Saurabh Fedorov, Alex Zègre-Hemsey, Jessica Hu, Xiao Xiao, Ran |
| author_facet | Meng, Zeyuan Panchumarthi, Lovely Yeswanth Kataria, Saurabh Fedorov, Alex Zègre-Hemsey, Jessica Hu, Xiao Xiao, Ran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes Meng, Zeyuan Panchumarthi, Lovely Yeswanth Kataria, Saurabh Fedorov, Alex Zègre-Hemsey, Jessica Hu, Xiao Xiao, Ran Signal Processing Machine Learning 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. |
| title | Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2502.17476 |