arXiv AI July 14, 2026 By Yi Zhao, Jiajun Gao, Chenyang Xu, Yuxi Zhou, Hao Wang

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation

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.

By Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham

efficiency benchmarks safety

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 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 AI

Jun 11

Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

arXiv:2512. 13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomain or monodomain equations.

By Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota, Jakub Grzelak, Oleg Aslanidi

benchmarks

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 AI

Jun 10

Boosting ECG Classification Performance by Pre-training with Synthesized Data

arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.

By Naoki Nonaka, Jun Seita

safety

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EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation

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

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

Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

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

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