CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains
This addresses the problem of robust ECG diagnostics for clinical applications, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the challenge of limited labeled ECG data and subtle clinical variations by introducing CREMA, a self-supervised foundation model for 12-lead ECGs that combines generative learning and contrastive regularization. It outperforms supervised baselines and existing self-supervised models across diverse clinical domains, maintaining superior performance in real-world deployment scenarios with distribution shifts.
Electrocardiogram (ECG) diagnosis remains challenging due to limited labeled data and the need to capture subtle yet clinically meaningful variations in rhythm and morphology. We present CREMA (Contrastive Regularized Masked Autoencoder), a foundation model for 12-lead ECGs designed to learn generalizable representations through self-supervised pretraining. CREMA combines generative learning and contrastive regularization via a Contrastive Regularized MAE loss, and employs a Signal Transformer (SiT) architecture to capture both local waveform details and global temporal dependencies. We evaluate CREMA on benchmark datasets and real-world clinical environments, including deployment scenarios with significant distribution shifts. CREMA outperforms supervised baselines and existing self-supervised models in both linear probing and fine-tuning evaluations. Notably, it maintains superior performance across diverse clinical domains, such as emergency care, highlighting its robustness under real-world conditions. These results demonstrate that CREMA serves as a scalable and reliable foundation model for ECG diagnostics, supporting downstream applications across heterogeneous and high-risk clinical settings.