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.
By Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk, Vahid Abolghasemi, Naser Hossein Motlagh, Sadasivan Puthusserypady, Daniel Teichmann, Abdolrahman Peimankar
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.
By Hongtao Li, Jia Wei, Guoyao Li, Yuchen Lei, Guangnian Ma, Jia Xiao, Yuanjun Lai, Shuzhen Lv, Xueqiang Ouyang
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.
By Yongbin Lee, Ki H. Chon
arXiv:2608. 00058v1 Announce Type: cross Abstract: Accurate measurement of ECG intervals, including PR, QRS duration, and QT/QTc, is central to cardiac diagnosis, yet the published ECG delineation literature evaluates performance almost exclusively as fiducial-point timing errors on small curated databases, rather than as clinical interval accuracy on large unselected cohorts.
By Farhan Adam Mukadam, Harshit Mishra, Nachiket Makwana, Pradyot Tiwari, Subramani Kandasamy, KVS Hari
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.
By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner
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.
By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed