用心电图基础模型融合提升急性冠脉综合征早期诊断准确率
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes
- 通过自监督学习提取心电图特征，融合两个基础模型嵌入
- 融合方法达到0.843的AUROC和0.674的AUCPR，优于单模型
- 适合急症早期筛查场景，对临床快速决策有参考价值
急性冠脉综合征（ACS）是危及生命的心血管疾病，早期准确诊断对治疗效果和患者预后至关重要。本研究探索使用心电图基础模型（ST-MEM 和 ECG-FM）来提升基于院前救护车采集心电图数据的 ACS 风险评估能力。两个模型均采用自监督学习（SSL）：ST-MEM 基于重构，ECG-FM 采用对比学习，分别捕捉心电图独特的时空特征。我们分别评估了模型性能，并采用融合策略，将两者嵌入向量结合进行预测。结果表明，两个基础模型均优于基准的 ResNet-50 模型，其中融合方法表现最优，获得 AUROC: 0.843 ± 0.006，AUCPR: 0.674 ± 0.012。研究证实了心电图基础模型在早期 ACS 识别中的潜力，并推动进一步探索先进融合策略以最大化互补特征利用。
原文摘要 · Abstract (English)
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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