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Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)
Chaturvedi M, Hatz F, Gschwandtner U, Bogaarts JG, Meyer A, Fuhr P, Roth V.. Frontiers in aging neuroscience, 2017-01-23
Topic: Risk, prevention and care in population studies
Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region. The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.
Key findings
- Random Forest and LASSO were found to be optimal methods for feature selection.
- A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients.
- The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region.
- The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.
Why it matters
This study used quantitative EEG measures and machine learning to distinguish Parkinson's disease patients from healthy controls, finding theta power in the temporal left region and the alpha1/theta ratio in the central left region most important. It concerns Parkinson's disease, not Alzheimer's disease, and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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 (Frontiers in aging neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Aging Neuroscience: journal h-index 174 (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 | 2017 — 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 4 / 7 | Cited 96 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.7 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 3 / 6 | Cited 3.9× 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 | 2 / 4 | Cited 33 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Volker Röth: h-index 46 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Basel: institution h-index 928 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28167911) |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnagi.2017.00003
- PubMed: 28167911
- Original source →
- Free full text: Frontiers in Aging Neuroscience →
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
Chaturvedi, M., Hatz, F., Gschwandtner, U., Bogaarts, J. G., Meyer, A., Fuhr, P., et al. (2017). Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC). Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2017.00003
Vancouver
Chaturvedi M, Hatz F, Gschwandtner U, Bogaarts JG, Meyer A, Fuhr P, et al. Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC). Frontiers in aging neuroscience. 2017. doi:10.3389/fnagi.2017.00003. PMID: 28167911.
BibTeX
@article{chaturvedi2017-28167911,
author = {Chaturvedi, M. and Hatz, F. and Gschwandtner, U. and Bogaarts, J. G. and Meyer, A. and Fuhr, P. and Roth, V.},
title = {{Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)}},
journal = {Frontiers in aging neuroscience},
year = {2017},
doi = {10.3389/fnagi.2017.00003},
pmid = {28167911},
url = {https://doi.org/10.3389/fnagi.2017.00003}
}
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