arXiv (All)
open access
BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment
Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats in each patch shifts with heart rate. This matters most for infants, whose heart rates are higher and whose ECG di
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data#329075
- Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning#662312
- Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning#661262
- Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning#480055
- X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals#159232
Record · ID 1106245
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive