This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

39/100

✓ Peer-reviewed alzhub usefulness score 39 out of 100. Prevention trials

Topic: Brain imaging, connectivity and assessment

In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD). AD is preceded by Mild Cognitive Impairment (MCI), typically refers to early-stage AD disease. The purpose of this study is to classify MCI patients from the multi-domain features of their electroencephalography (EEG). Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls. Then, principal component analysis (PCA) was used to perform feature selection. After that, we compared the performance between SVM and KNN on our EEG dataset. The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features. Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.

Key findings

Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls.
Then, principal component analysis (PCA) was used to perform feature selection.
After that, we compared the performance between SVM and KNN on our EEG dataset.
The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features.
Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.

New to this subject? Start with our plain-English guide: Prevention →

Study details

Evidence strengthLow

How this study scored 39/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 22 of the 56 points we could judge it on, which is 39 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference)
Journal's citation record	— / 4	No journal record from OpenAlex — left out of this score
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	2 / 5	2020 — within 10 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	— / 7	No citation count (not found in OpenAlex) — left out of this score
Citations per year	— / 5	No citation count or no year — left out of this score
Impact for its field and year	— / 6	No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here	— / 6	Not yet checked against this library's citation graph — left out of this score
Still being cited	— / 4	No yearly citation counts — left out of this score
Senior author's track record	— / 4	Senior author's record not available — left out of this score
Institution's track record	— / 3	No institution listed in OpenAlex — left out of this score
Multi-centre collaboration	— / 3	Institutions not listed — left out of this score
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 33017977)
Free to read	— / 2	Access not recorded — left out of this score
Points	22 / 56	Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 22 of the 56 points we could judge it on is 39 out of 100.
Total	39 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Li, Y., Xiao, S., Li, Y., Li, Y., & Yang, B. (2020). Classification of Mild Cognitive Impairment from multi-domain features of resting-state EEG. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. https://doi.org/10.1109/embc44109.2020.9176053

Vancouver

Li Y, Xiao S, Li Y, Li Y, Yang B. Classification of Mild Cognitive Impairment from multi-domain features of resting-state EEG. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2020. doi:10.1109/embc44109.2020.9176053. PMID: 33017977.

BibTeX

@article{li2020-33017977,
  author  = {Li, Yuling and Xiao, Shasha and Li, Yingjie and Li, Yunxia and Yang, Banghua},
  title   = {{Classification of Mild Cognitive Impairment from multi-domain features of resting-state EEG}},
  journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
  year    = {2020},
  doi     = {10.1109/embc44109.2020.9176053},
  pmid    = {33017977},
  url     = {https://doi.org/10.1109/embc44109.2020.9176053}
}

Related studies in Prevention trials

Also tagged “convolutional”

205 studies in the library carry the topic tag “convolutional”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 205 tagged studies.

Similar studies

More on Prevention trials

See all studies in Prevention trials →