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Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis
Yixuan Huang, Zhenyu Li, Fangzhou Liu, Bo Li, Chenhui Mao, Liling Dong, Shanshan Chu, Wei Jin et al.. Journal of Alzheimer s Disease, 2025-07-28
Topic: Risk, prevention and care in population studies
BackgroundElectroencephalogram (EEG) is a promising, non-invasive method for identifying the presence of Alzheimer's disease (AD) by recognizing specific brain activity patterns associated with the disease. However, research on the correlation between EEG and the degree of cognitive impairment is still lacking.ObjectiveIn this study, we employ machine learning models to explore the potential of EEG in distinguishing different levels of cognitive impairment and various types of dementia.MethodsA total of 431 participants, including 77 cognitively unimpaired (CU), 167 patients with mild dementia, 110 patients with moderate dementia, and 77 patients with severe dementia were enrolled. Among them, 91 patients have detailed biomarker results to support differential diagnosis, with 77 AD and 14 frontotemporal dementia. After feature extraction, the rule-based representation learning was used to train models for EEG-based classification tasks.ResultsOur model can effectively differentiate between CU and moderate-to-severe dementia (AUC 0.8475), as well as between CU and AD patients in individuals under 65 (AUC 0.8170). However, our preliminary analysis was not able to effectively distinguish between different types of dementia. It is also challenging to differentiate between CU and mild dementia groups, as well as between the moderate and severe dementia.ConclusionsOur study suggests that EEG might be used not only in the early identification of AD, but also in the diagnosis and monitoring of the entire dementia spectrum, encompassing various stages and types of cognitive decline.
Key findings
- BackgroundElectroencephalogram (EEG) is a promising, non-invasive method for identifying the presence of Alzheimer's disease (AD) by recognizing specific brain activity patterns associated with the disease.
- Among them, 91 patients have detailed biomarker results to support differential diagnosis, with 77 AD and 14 frontotemporal dementia.
- It is also challenging to differentiate between CU and mild dementia groups, as well as between the moderate and severe dementia.ConclusionsOur study suggests that EEG might be used not only in the early identification of AD, but also in the diagnosis and monitoring of the entire dementia spectrum, encompassing various stages and types of cognitive decline.
Why it matters
An EEG-based machine learning model could tell apart cognitively unimpaired people from those with moderate-to-severe dementia, and from Alzheimer's patients under 65, but it could not reliably distinguish different dementia types or separate unimpaired people from mild dementia. This is a diagnostic study only and says nothing about treating, curing, or preventing Alzheimer's disease.
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Study details
- Participants431
- Effect sizeAUC 0.8475; AUC 0.8170
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Alzheimer s Disease) |
| Journal's citation record | 2 / 4 | Journal of Alzheimer s Disease: journal h-index 218 (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 | 2 / 4 | 431 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jing Gao: h-index 22 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Tsinghua University: institution h-index 1,218 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40719017) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 43 / 100 | — |
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Read the original
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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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Huang, Y., Li, Z., Liu, F., Li, B., Mao, C., Dong, L., et al. (2025). Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis. Journal of Alzheimer s Disease. https://doi.org/10.1177/13872877251360331
Vancouver
Huang Y, Li Z, Liu F, Li B, Mao C, Dong L, et al. Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis. Journal of Alzheimer s Disease. 2025. doi:10.1177/13872877251360331. PMID: 40719017.
BibTeX
@article{huang2025-40719017,
author = {Huang, Yixuan and Li, Zhenyu and Liu, Fangzhou and Li, Bo and Mao, Chenhui and Dong, Liling and Chu, Shanshan and Jin, Wei and others},
title = {{Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis}},
journal = {Journal of Alzheimer s Disease},
year = {2025},
doi = {10.1177/13872877251360331},
pmid = {40719017},
url = {https://doi.org/10.1177/13872877251360331}
}
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