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Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review
Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG.. International journal of neural systems, 2021-02-16
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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APA 7th
Tzimourta, K. D., Christou, V., Tzallas, A. T., Giannakeas, N., Astrakas, L. G., Angelidis, P., et al. (2021). Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. International journal of neural systems. https://doi.org/10.1142/s0129065721300023
Vancouver
Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, et al. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. International journal of neural systems. 2021. doi:10.1142/s0129065721300023. PMID: 33588710.
BibTeX
@article{tzimourta2021-33588710,
author = {Tzimourta, K. D. and Christou, V. and Tzallas, A. T. and Giannakeas, N. and Astrakas, L. G. and Angelidis, P. and Tsalikakis, D. and Tsipouras, M. G.},
title = {{Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review}},
journal = {International journal of neural systems},
year = {2021},
doi = {10.1142/s0129065721300023},
pmid = {33588710},
url = {https://doi.org/10.1142/s0129065721300023}
}
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Key findings
- Results of each experimental study are presented and compared.
Why it matters
This systematic review of 49 experimental studies from 2009 to 2020 examined machine learning algorithms applied to resting-state EEG recordings for Alzheimer's disease detection and correlation with MMSE score. Most studies focused on Alzheimer's detection using Support Vector Machines, and deep learning techniques had not yet been applied to large EEG datasets.
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Study details
- Participants49
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International journal of neural systems) |
| Journal's citation record | 1 / 4 | International Journal of Neural Systems: journal h-index 95 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question (from PubMed type “Systematic Review”) |
| 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 | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 1 / 4 | 49 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 | A review of other studies — randomisation is not credited here |
| Times cited | 4 / 7 | Cited 78 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.1 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 2 / 6 | Cited 2.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 25 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Markos G. Tsipouras: h-index 37 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Ioannina: institution h-index 513 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33588710) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 55 / 100 | — |
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