LVCG is the first self-supervised framework for learning view-invariant latent VCG representations that claims to outperform ECG-space baselines with better robustness and generalization in domain shift settings.
Reading Your Heart: Learning ECG words and sentences via pre-training ECG language model.arXiv preprint arXiv:2502.10707
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Empirical scaling study of ECG models finds SSL scales robustly while ResNets show 1.3-2.5x better parameter efficiency and SSL up to 16x better data efficiency than supervised baselines on out-of-distribution tasks.
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
AEMG learns reusable neuromuscular representations from eight standardized EMG datasets by modeling contraction events as tokens and using self-supervised pre-training to improve robustness to new users and reduce calibration data needs.
citing papers explorer
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Learning Cardiac Latent Representations in Vectorcardiogram Space
LVCG is the first self-supervised framework for learning view-invariant latent VCG representations that claims to outperform ECG-space baselines with better robustness and generalization in domain shift settings.
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HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
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From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
AEMG learns reusable neuromuscular representations from eight standardized EMG datasets by modeling contraction events as tokens and using self-supervised pre-training to improve robustness to new users and reduce calibration data needs.