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Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation
Vecchio F, Miraglia F, Alù F, Menna M, Judica E, Cotelli M, Rossini PM.. Journal of Alzheimer's disease : JAD, 2020-01-01
Topic: Amyloid, tau and blood-test diagnosis
Background Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests. Objective To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter. Methods A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). Results The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification. Conclusion EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Vecchio, F., Miraglia, F., Alù, F., Menna, M., Judica, E., Cotelli, M., et al. (2020). Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation. Journal of Alzheimer's disease : JAD. https://doi.org/10.3233/jad-200171
Vancouver
Vecchio F, Miraglia F, Alù F, Menna M, Judica E, Cotelli M, et al. Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation. Journal of Alzheimer's disease : JAD. 2020. doi:10.3233/jad-200171. PMID: 32417784.
BibTeX
@article{vecchio2020-32417784,
author = {Vecchio, F. and Miraglia, F. and Alù, F. and Menna, M. and Judica, E. and Cotelli, M. and Rossini, P. M.},
title = {{Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation}},
journal = {Journal of Alzheimer's disease : JAD},
year = {2020},
doi = {10.3233/jad-200171},
pmid = {32417784},
url = {https://doi.org/10.3233/jad-200171}
}
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 names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Objective To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter.
- Results The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy).
Why it matters
This study used a machine learning classifier on EEG connectivity parameters to distinguish healthy volunteers from people with Alzheimer's disease, reporting very high classification accuracy. The authors suggest this could add a low-cost, easily accessible, non-invasive technique alongside neuropsychological tests.
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Study details
- Participants295
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 : JAD) |
| 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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 295 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 4 / 7 | Cited 73 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.1 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 4.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 2 / 4 | Cited 39 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Paolo Maria Rossini: h-index 121 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele: institution h-index 393 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32417784) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 48 / 100 | — |
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