arXiv AI

SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification

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 Machine Learning

Jun 30

Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning

arXiv:2512. 11095v2 Announce Type: replace Abstract: Label ambiguity is an inherent and largely unaddressed challenge in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreements.

By Sana Rahmani, Javad Hashemi, Ali Etemad

arXiv Machine Learning

Jul 2

A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data

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.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jul 16

Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis

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.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jul 28

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

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

benchmarks

arXiv AI

Jun 2

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

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

benchmarks

arXiv AI

Jun 11

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

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.

By Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng, Shreyank N Gowda

arXiv AI

Aug 5

FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection

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

rag benchmarks

arXiv Machine Learning

5d ago

The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning

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.

By Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan Varatharajah

llms rag efficiency

arXiv AI

Aug 7

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

arXiv:2608. 05893v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging.

By Akanta Das, Tasinul Islam Ahon, Ahmed Mahir Sultan Rumi, Md Mahbubur Rahman, Tausif Amim Shadly, Tanzima Hashem

llms benchmarks

arXiv AI

Jul 14

LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification

arXiv:2607. 10784v1 Announce Type: cross Abstract: Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs.

By Yi Zhao, Jiajun Gao, Chenyang Xu, Yuxi Zhou, Hao Wang

llms efficiency benchmarks

arXiv Machine Learning

Aug 4

Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet

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

computer-vision safety

arXiv AI

Aug 12

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

arXiv:2605. 31249v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clinical report generation.

By Bosong Huang, Panzhen Zhao, Zengxiang Li, Patricia Lee, Wei Jin, Alan Wee-Chung Liew, Ming Jin, Shirui Pan

diffusion

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