πŸ“„
An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention paper by Independent / Community
⭐ 68.7
πŸ“„
Paper

An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention

by Independent / Community 00aeb0ed9f96946c158b453e2bc773b6a8a5c75b
Free2AITools Nexus Index
68.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 84
P: Popularity 59
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting ...

Source β†’
Semantic Scholar 46 Citations
Paper Information Summary
Entity Passport
Registry ID 00aeb0ed9f96946c158b453e2bc773b6a8a5c75b
License ArXiv
Provider semantic_scholar
πŸ“œ

Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00aeb0ed9f96946c158b453e2bc773b6a8a5c75b,
  author = {Unknown},
  title = {An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00aeb0ed9f96946c158b453e2bc773b6a8a5c75b}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention [Paper]. Free2AITools. https://api.semanticscholar.org/00aeb0ed9f96946c158b453e2bc773b6a8a5c75b

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 84
Popularity (P) 59
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention: Authority (A:84), Popularity (P:59), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting ..."

❝ Cite Node 

@article{Unknown2026An,
  title={An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ46CitationsSemantic Scholar
πŸ›οΈ84AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈvision multimediaField

🏷️ Research Topics

transformer architecture
πŸ“¦Data Source: semantic_scholar
πŸ”„ Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
75
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
0
stars
null
forks
null
citations
46

Data indexed from public sources. Updated daily.