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. 01145v2 Announce Type: replace Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.
arXiv:2607. 01145v1 Announce Type: new Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
arXiv:2606. 08037v1 Announce Type: cross Abstract: Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs.
arXiv:2606. 19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data.
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.
The paper presents a hybrid CNN–state‑space–attention backbone designed for 12‑lead ECG classification, combining early waveform tokenization, mixed temporal dynamics modeling, and late global attention. It introduces an ECG‑oriented Joint‑Embedding Predictive Pretraining (JEPA) that samples span masks at latent resolution and predicts clean latent targets via a momentum encoder, avoiding waveform reconstruction. Experiments on CPSC2018, Chapman‑Shaoxing, and PTB‑XL, with pretraining on ~350K unlabeled CODE‑15 recordings, demonstrate strong supervised baselines and improved transfer, especially in low‑label scenarios and with LoRA adaptation.
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:2610.33964v1 Announce Type: cross Abstract: AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where...
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...
arXiv:2610.07885v1 Announce Type: cross Abstract: Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinicall...
arXiv:2608. 14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability.