Application of machine learning to electroencephalography for the diagnosis of primary progressive aphasia: A pilot study
Open accessAbstract
Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.
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Biomarkers have been shown to be less sensitive to multiclass differentiation across neurodegenerative diseases.
“Furthermore, biomarkers have been shown to be less sensitive to multiclass differentiation (i.e., classifying one condition against multiple other variants) across neurodegenerative diseases (Moral-Rubio et al., 2021; Tahmasian et al., 2016).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Moral-Rubio et al., 2021
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Moral-Rubio, C., Fernández‐Romero, L., Balugo, P., Fraile-Pereda, A., Suárez‐Coalla, P., Cabrera‐Martín, M. N., Yus, M., Matias-Guiu, J. A., & Ayala, J. L. (2026) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823261420785
Self-citation
- Authors
- Moral-Rubio, C., Balugo, P., Fraile-Pereda, A., Pytel, V., Fernández-Romero, L., Delgado-Alonso, C., Delgado-Álvarez, A., Matias-Guiu, J., Matias-Guiu, J. A., & Ayala, J. L.
- Affiliations
- Universidad Complutense de Madrid (ES)
- Department of Neurology, Institute of Neuroscience, Hospital Clínico San Carlos (IdISCC), Universidad Complutense de Madrid, 28040 Madrid, Spain
- Venue
- Brain Sciences
- Publisher
- MDPI AG
- Published
- 2021-09-24
- Volume
- 11
- Issue
- 10
- Pages
- 1262
- ISSN
- 2076-3425 (ISSN-L)
- PMC
- PMC8534262
- OpenAlex
- W3201769198
- Cited by
- 25
- References
- 54
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 6 · 2023: 6 · 2022: 5
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Library Open Repository (Universidad Complutense Madrid) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Instituto de Salud Carlos III (INT20/00079)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- primary progressive aphasia, EEG, resting-state EEG, EEG biomarkers, machine learning classification, graph theory, network analysis, semantic variant, logopenic variant, neurodegenerative disease diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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