# Electrocardiography

Deep learning models for electrocardiograms are mostly trained with supervision tied to specific diagnostic labels, which makes them fragile when patient populations or acquisition settings change. This self-supervised pretraining framework learns features from 918,915 unlabeled 12-lead ECG recordings, with one path explicitly measuring each lead's deviation from normal waveform morphology and another reconstructing cross-lead associations under those deviation cues. With the feature extractor frozen and no downstream fine-tuning, it improves AUROC by up to 6.5% and F1 by up to 12.8% over prior methods on four public datasets.

Deep learning models for electrocardiograms are mostly trained with supervision tied to specific diagnostic labels, which makes them fragile when patient populations or acquisition settings change. This self-supervised pretraining framework learns features from 918,915 unlabeled 12-lead ECG recordings, with one path explicitly measuring each lead's deviation from normal waveform morphology and another reconstructing cross-lead associations under those deviation cues. With the feature extractor frozen and no downstream fine-tuning, it improves AUROC by up to 6.5% and F1 by up to 12.8% over prior methods on four public datasets.

Weight-space programming lets a language model read and edit a trained network's weights directly, and had been shown on game-playing networks and language models. Applied to EEG and ECG models, Claude Opus 5.5 wrote all 2,730 weights of TinyEEGNet with no gradient step and only five practice subjects, reaching 0.834 balanced accuracy on 65 unseen subjects, on par with 0.806 for the same architecture trained on 30 subjects and above 0.704 trained on five; it could not infer a network's task from its weights, nor improve an already well-trained network.

Weight-space programming lets a language model read and edit a trained network's weights directly, and had been shown on game-playing networks and language models. Applied to EEG and ECG models, Claude Opus 5.5 wrote all 2,730 weights of TinyEEGNet with no gradient step and only five practice subjects, reaching 0.834 balanced accuracy on 65 unseen subjects, on par with 0.806 for the same architecture trained on 30 subjects and above 0.704 trained on five; it could not infer a network's task from its weights, nor improve an already well-trained network.