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Improving Alzheimer's disease diagnosis with machine learning techniques
Trambaiolli LR, Lorena AC, Fraga FJ, Kanda PA, Anghinah R, Nitrini R.. Clinical EEG and neuroscience, 2011-07-01
Topic: Amyloid, tau and blood-test diagnosis
There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.
Key findings
- Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results.
- As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia.
- The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%.
Why it matters
This study used a machine learning method on EEG recordings to distinguish people with Alzheimer's disease from normal individuals, reaching about 80% accuracy on epochs and 87% accuracy per patient. It is a diagnostic aid, not a treatment, cure or prevention.
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Study details
- Effect size79.9%; 83.2%; 87.0%
- Evidence strengthLow
How this study scored 42/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Clinical EEG and neuroscience) |
| Journal's citation record | 0 / 4 | Clinical EEG and Neuroscience: journal h-index 68 (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 | 1 / 5 | 2011 — within 20 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 | 5 / 7 | Cited 146 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.6 citations a year over 17 year(s) since 2011 |
| Impact for its field and year | 2 / 6 | Cited 2.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 41 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Ricardo Nitríni: h-index 69 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Universidade de São Paulo: institution h-index 936 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 21870467) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 42 / 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. Check the citation against the original before you use it.
APA 7th
Trambaiolli, L. R., Lorena, A. C., Fraga, F. J., Kanda, P. A., Anghinah, R., & Nitrini, R. (2011). Improving Alzheimer's disease diagnosis with machine learning techniques. Clinical EEG and neuroscience. https://doi.org/10.1177/155005941104200304
Vancouver
Trambaiolli LR, Lorena AC, Fraga FJ, Kanda PA, Anghinah R, Nitrini R. Improving Alzheimer's disease diagnosis with machine learning techniques. Clinical EEG and neuroscience. 2011. doi:10.1177/155005941104200304. PMID: 21870467.
BibTeX
@article{trambaiolli2011-21870467,
author = {Trambaiolli, L. R. and Lorena, A. C. and Fraga, F. J. and Kanda, P. A. and Anghinah, R. and Nitrini, R.},
title = {{Improving Alzheimer's disease diagnosis with machine learning techniques}},
journal = {Clinical EEG and neuroscience},
year = {2011},
doi = {10.1177/155005941104200304},
pmid = {21870467},
url = {https://doi.org/10.1177/155005941104200304}
}
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