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

Understand

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.