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.
Zero-Shot ECG classification with multimodal learning and test-time clinical knowledge enhancement.arXiv preprint arXiv:2403.06659
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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cs.LG 4years
2026 4representative citing papers
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.
SPOTR is a single-token reconstruction self-supervised pretraining method for EEG, iEEG, ECG, and PPG that reports AUC gains of 4.64-21.71% over baselines under linear probing while cutting latency and memory.
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
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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How Do Electrocardiogram Models Scale?
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.
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SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning
SPOTR is a single-token reconstruction self-supervised pretraining method for EEG, iEEG, ECG, and PPG that reports AUC gains of 4.64-21.71% over baselines under linear probing while cutting latency and memory.
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Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.