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41/100

✓ Peer-reviewed alzhub usefulness score 41 out of 100. Lewy body dementia

Topic: Amyloid, tau and blood-test diagnosis

Accurate identification of neurodegenerative disorders, including Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), is crucial for prompt medical intervention. This research introduces an EEG-based ensemble classification framework aimed at differentiating between AD, FTD, and Cognitively Normal (CN) people. The system uses ElectroEncephaloGram (EEG) signals derived from the publicly accessible dataset from Kaggle. Independent Component Analysis (ICA) is used for preprocessing to remove artefacts and noise. The important key characteristics are identified and chosen by Recursive Feature Elimination (RFE) to provide optimum input for classification from the features of Discrete Wavelet Transform (DWT) sub-bands. Several classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), are trained, and their predictions are aggregated using Logistic Regression (LR) as the meta-classifier. The resulting model has exceptional diagnostic performance, with the LR ensemble achieving an accuracy of 98.98% in differentiating the three subject groups. Results proved the capability of combining EEG signal analysis with ensemble learning techniques to enhance clinical decision making for the early identification of dementia-related disorders.

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

APA 7th

Sudha, G., Mohankumar, N., Thahniyath, G., & Thimmarayan, R. (2025). EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS. International Journal of Advances in Signal and Image Sciences. https://doi.org/10.29284/ijasis.11.1.2025.44-54

Vancouver

Sudha G, Mohankumar N, Thahniyath G, Thimmarayan R. EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS. International Journal of Advances in Signal and Image Sciences. 2025. doi:10.29284/ijasis.11.1.2025.44-54.

BibTeX

@article{sudha2025-833da773,
  author  = {Sudha, G. and Mohankumar, N. and Thahniyath, Gousia and Thimmarayan, Raja},
  title   = {{EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS}},
  journal = {International Journal of Advances in Signal and Image Sciences},
  year    = {2025},
  doi     = {10.29284/ijasis.11.1.2025.44-54},
  url     = {https://doi.org/10.29284/ijasis.11.1.2025.44-54}
}

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Key findings

Results proved the capability of combining EEG signal analysis with ensemble learning techniques to enhance clinical decision making for the early identification of dementia-related disorders.

Why it matters

An EEG-based ensemble classification framework achieved high accuracy in differentiating Alzheimer's disease, frontotemporal dementia, and cognitively normal individuals. Results suggest combining EEG signal analysis with ensemble learning techniques could enhance clinical decision making for early identification of dementia-related disorders.

Study details

Effect size98.98%
Evidence strengthLow

How this study scored 41/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 39 of the 96 points we could judge it on, which is 41 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal of Advances in Signal and Image Sciences)
Journal's citation record	0 / 4	International Journal of Advances in Signal and Image Sciences: journal h-index 18 (OpenAlex, 2026-10-29)
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	4 / 5	2025 — within 3 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	2 / 3	Effect reported as a number, without an interval or test
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	3 / 7	Cited 15 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	5.0 citations a year over 3 year(s) since 2025
Impact for its field and year	4 / 6	Cited 8.6× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	2 / 4	Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Raja Thimmarayan holds only 7 work(s), too few to judge a track record — left out of this score
Institution's track record	0 / 3	Symbiosis International University: institution h-index 190 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 1 country
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	2 / 2	Free to read
Points	39 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 39 of the 96 points we could judge it on is 41 out of 100.
Total	41 / 100	—

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

Read the original

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