Evidence map›Paper›PMID 36866172›Full record

ArticleFrontiers in physiology2023

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network.

Hua Zhang, Chengyu Liu, Fangfang Tang, Mingyan Li, Dongxia Zhang, Ling Xia, Stuart Crozier, Hongping Gan, Nan Zhao, Wenlong Xu and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 7 institutions in 2 countries.

Hua ZhangSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Chengyu LiuSchool of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Fangfang TangSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Mingyan LiSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Dongxia ZhangZhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, Zhejiang, China.
Ling XiaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.
Stuart CrozierSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Hongping GanSchool of Software, Northwestern Polytechnical University, Xi'an, China.
Nan ZhaoSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Wenlong XuDepartment of Biomedical Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
Feng LiuSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
Queensland University of Technology · AUThe University of Queensland · AUChina Jiliang University · CNNorthwestern Polytechnical University · CNSoutheast University · CNZhejiang Center for Disease Control and Prevention · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined

Indexed as

atrial fibrillation identificationelectrocardiogramnon-deep neural networkoptimal subsetrecurrence plot

Identifiers

PMID36866172
PMCPMC9971936
OpenAlexW4320719694

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.