Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
Benchecg and xecg: a benchmark and baseline for ecg foundation models
4 Pith papers cite this work. Polarity classification is still indexing.
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StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.
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.
DeepArrhythmia introduces a segment-contextualized multimodal framework for beat-level ECG arrhythmia classification that uses tool-grounded evidence extraction and selective acquisition routed by segment-level confidence.
citing papers explorer
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Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
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Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.
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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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DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
DeepArrhythmia introduces a segment-contextualized multimodal framework for beat-level ECG arrhythmia classification that uses tool-grounded evidence extraction and selective acquisition routed by segment-level confidence.