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October 16, 2025Open Access

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

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Analysis shows improved prediction of acute coronary syndrome using ECG foundation models, indicating enhanced risk assessment.

The fusion approach achieved the highest prediction performance for acute coronary syndrome detection.
Results showed that both ECG foundation models significantly outperform the baseline ResNet-50 model.
ST-MEM and ECG-FM leverage different self-supervised learning techniques for enhancing ECG feature capture.
Early and accurate diagnosis of acute coronary syndrome is crucial for improving patient outcomes.

Meng et al. (2025) studied this question.

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October 16, 2025Open Access

View Full Paper

Ask AI

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Authors

Analysis shows improved prediction of acute coronary syndrome using ECG foundation models, indicating enhanced risk assessment.

The fusion approach achieved the highest prediction performance for acute coronary syndrome detection.
Results showed that both ECG foundation models significantly outperform the baseline ResNet-50 model.
ST-MEM and ECG-FM leverage different self-supervised learning techniques for enhancing ECG feature capture.
Early and accurate diagnosis of acute coronary syndrome is crucial for improving patient outcomes.

Meng et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark