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arXiv:2608. 12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences.
arXiv:2608.30367v1 Announce Type: new Abstract: Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split h...
arXiv:2606. 12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment.
TransECG-SSL is a self‑supervised framework that learns transferable heartbeat representations from unlabelled ECG data using a Transformer encoder pretrained with a hybrid objective of masked signal reconstruction and contrastive learning. The pretrained model is frozen and evaluated via linear probing on patient‑disjoint splits across three public ECG databases, showing that Hybrid SSL outperforms randomly initialized Transformers and source‑supervised pretraining. While cross‑dataset transfer improves performance over random baselines, it still falls short of target‑specific self‑supervised pretraining, and minority arrhythmia classes remain difficult under severe class imbalance.
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
ECGWmark is a fully convolutional, quantization‑free deep watermarking framework designed for electrocardiogram (ECG) signals. It embeds patient metadata into ECGs while preserving high‑frequency diagnostic details by operating in a continuous latent space and using a localized frame‑level mechanism to resist severe temporal desynchronization. A differentiable Physiological Loss guided by a clinical classifier helps maintain diagnostic semantics, achieving over 90% channel‑bit extraction accuracy under circular phase shifts and demonstrating mixed effects on class‑level diagnostic metrics in the MIT‑BIH benchmark.