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A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals
Siuly S, Alcin OF, Kabir E, Sengur A, Wang H, Zhang Y, Whittaker F.. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020-07-31
Topic: Risk, prevention and care in population studies
Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.
Key findings
- The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods.
- The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.
Why it matters
This study developed a machine learning framework using resting-state EEG signals that distinguished patients with mild cognitive impairment from healthy controls with high classification accuracy. It is about detection of mild cognitive impairment, not about treating, curing, or preventing Alzheimer's disease.
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Study details
- Effect size98.78%
- Evidence strengthLow
How this study scored 46/100
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 (IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society) |
| Journal's citation record | 2 / 4 | IEEE Transactions on Neural Systems and Rehabilitation Engineering: journal h-index 190 (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 | 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 153 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 19.1 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 10.2× 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 | 3 / 4 | Cited 70 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Frank Whittaker: h-index 15 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Victoria University: institution h-index 330 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32746328) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1109/tnsre.2020.3013429
- PubMed: 32746328
- Original source →
- Free full text: Malatya Turgut Özal University Institutional Repository →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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 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.
APA 7th
Siuly, S., Alcin, O. F., Kabir, E., Sengur, A., Wang, H., Zhang, Y., et al. (2020). A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/tnsre.2020.3013429
Vancouver
Siuly S, Alcin OF, Kabir E, Sengur A, Wang H, Zhang Y, et al. A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 2020. doi:10.1109/tnsre.2020.3013429. PMID: 32746328.
BibTeX
@article{siuly2020-32746328,
author = {Siuly, S. and Alcin, O. F. and Kabir, E. and Sengur, A. and Wang, H. and Zhang, Y. and Whittaker, F.},
title = {{A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals}},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
year = {2020},
doi = {10.1109/tnsre.2020.3013429},
pmid = {32746328},
url = {https://doi.org/10.1109/tnsre.2020.3013429}
}
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