Scaling ECG Foundation Models and Identifying a Threshold for Effective Representation Learning
Sriram, R.; Nenadic, I.; Shahrabani, E.; Goonewardena, S.; Yao, S.; Farrell, B.; Loring, Z.; Murthy, V. L.
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We conducted a scaling evaluation of unlabeled pretraining for electrocardiogram foundation model performance. One-dimensional vision transformer masked autoencoders were pretrained across increasing ECG volumes and fine-tuned for rhythm, morphology, diagnostic, and structural heart disease tasks. Models pretrained below 400,000 ECGs failed to consistently exceed controls without self-supervised pre-training, whereas 600,000 to 800,000 ECGs improved AUROC across tasks, suggesting a minimum threshold for effective ECG representation learning.
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