← Back to all news
arXiv Machine Learning By Mengyu Wang, Kozo Okada, Takafumi Goto, Natsuko Jinba, Hiroki Yamaya, Kiyoshi Hibi, Tomoki Hamagami

Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
More like this →
arXiv Machine Learning
Aug 20

Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework

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
efficiencybenchmarks
More like this →
arXiv Machine Learning
Aug 5

LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics

arXiv:2608. 03690v1 Announce Type: new Abstract: Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2 leads, yet existing ECG foundation models are architecturally constrained to fixed 12-lead inputs, degrading or failing under these reduced configurations.

By Edoardo Coppola, Stefano Fiorini, Pietro Li\`o, Mattia Savardi, Alberto Signoroni
More like this →
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
llmsbenchmarks
More like this →