Evidence map›Paper›PMID 37265653›Full record

ArticleCognitive neurodynamics2023

Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters.

Yuxia Hu, Yufei Wang, Rui Zhang, Yubo Hu, Mingzhu Fang, Zhe Li, Li Shi, Yankun Zhang, Zhong Zhang, Jinfeng Gao and 1 more

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

Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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 4 institutions in 1 country.

Yuxia HuSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Yufei WangSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Rui ZhangSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Yubo HuShenqiu County People's Hospital, Henan Province, China.
Mingzhu FangThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhe LiThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Li ShiDepartment of Automation, Tsinghua University, Beijing, China.
Yankun ZhangZhengzhou Boone Technology Company, Zhengzhou, China.
Zhong ZhangSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Jinfeng GaoSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Lipeng ZhangSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Guangxi Zhuang Autonomous Region Brain Hospital · CNZhengzhou University · CNFifth Affiliated Hospital of Zhengzhou University · CNTsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (

Indexed as

EEGNonlinear parametersQuantitative EEG indexesRehabilitationStroke

Identifiers

PMID37265653
PMCPMC10229519
OpenAlexW4290033593

What OpenQuestion holds

Textmetadata
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.