The paper introduces DCGCNet, a dual-codebook graph collaborative network that jointly reconstructs ECG signals and classifies atrial fibrillation. It incorporates a local‑global contrastive module for noise‑invariant feature learning and an adaptive codebook vector quantizer to prevent codebook collapse. The model achieves state‑of‑the‑art intra‑dataset performance and consistently attains AUC > 0.98 across seven cross‑dataset settings, even under realistic noisy conditions.
arXiv:2608. 03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.
AF-Mamba is a deep learning model that predicts atrial fibrillation (AF) onset one hour in advance using long‑term RR intervals. It combines temporal convolutional networks for local feature extraction with Mamba, a state‑space model for long‑range sequence modeling, achieving high sensitivity (0.889) and specificity (0.943) in subject‑wise testing. The model maintains strong performance across unseen datasets, offering a favorable trade‑off between predictive accuracy and computational efficiency for real‑time ambulatory monitoring.
The paper presents a new labelled ICU dataset and benchmarks for detecting atrial fibrillation (AF) from electrocardiograms (ECGs). It compares three AI approaches—feature‑based classifiers, deep learning, and ECG foundation models—across Canadian ICU data and the 2021 PhysioNet challenge, finding that ECG foundation models with transfer learning achieve the highest F1 score (0.89). The study demonstrates the feasibility of automated AF monitoring in ICU settings and provides resources for further research.
BEAT-Net is a supervised biomimetic framework for ECG diagnosis that incorporates QRS-centered tokenization and a hierarchical architecture mirroring a cardiologist’s workflow. It processes heartbeat sequences through morphological, spatial, temporal, and transformer-based stages, achieving an AUC of 0.924 on large benchmarks while using only 0.7 million parameters. The model outperforms the 39.5‑million‑parameter HeartLang foundation model on morphological form classification and demonstrates superior cross‑dataset generalization with only 35% of the training data.
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...
The paper introduces Graph-CMMC, a graph-based pseudo‑multimodal contrastive learning framework for 12‑lead ECG representations. It transforms ECG waveforms into Gramian Angular Difference Field (GADF) images to create complementary views, then aligns these views while modeling inter‑lead dependencies with a graph module. Experiments on coronary artery occlusion classification show that Graph-CMMC performs competitively with supervised methods, highlighting the value of GADF representations and explicit graph modeling for robust ECG analysis.
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: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.
arXiv:2609.22631v1 Announce Type: new Abstract: Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction...
arXiv:2606. 10410v1 Announce Type: new Abstract: Objective: Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and deployment data.
arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.
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.