HeartBERT applies self-supervised pretraining on a RoBERTa architecture to ECG signals, producing embeddings that enable strong performance on sleep staging and heartbeat classification with smaller labeled datasets and fewer parameters than baselines.
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HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
HeartBERT applies self-supervised pretraining on a RoBERTa architecture to ECG signals, producing embeddings that enable strong performance on sleep staging and heartbeat classification with smaller labeled datasets and fewer parameters than baselines.