arXiv:2607. 09680v1 Announce Type: cross Abstract: Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware.
arXiv:2607. 14747v1 Announce Type: cross Abstract: Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis.
arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.
arXiv:2606. 02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems.
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.
arXiv:2607. 07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease.
arXiv:2605. 29977v2 Announce Type: replace-cross Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands.
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.
ECGQuest is a new benchmark that evaluates language models on the contextual knowledge required for electrocardiogram interpretation, featuring 10,904 True/False questions derived from 23 ECG references and 2003‑2025 Computing in Cardiology proceedings. The study tested 23 commercial and open‑source models, finding that zero‑shot accuracy ranged from 49.5% to 74.4% and that fine‑tuning with Low‑Rank Adaptation improved all open‑source models by 6.5–14.1%, with the best fine‑tuned model achieving 76.3% accuracy and a five‑model ensemble reaching 78.5%. ECGQuest demonstrates that parameter‑efficient fine‑tuning can enable smaller models to compete with larger commercial ones on ECG‑specific tasks.
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.
arXiv:2509. 11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.
The paper introduces R‑U‑Net, an ECG delineation model that combines a ResNet‑18 encoder with a U‑Net decoder. It demonstrates that this decoder design outperforms a ResNet‑18 + fully convolutional network baseline across 16 in‑domain settings and improves cross‑domain performance by 8.1 mIoU. Ablation studies reveal that the decoder contributes more to performance gains than the evaluated semi‑supervised learning methods.