Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes
This work addresses a critical medical problem for cardiovascular patients by improving early diagnosis, though it is incremental as it builds on existing foundation models.
The study tackled early detection of Acute Coronary Syndromes using ECG foundation models, finding that a fusion approach achieved the highest performance with an AUROC of 0.843 and AUCPR of 0.674.
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.