RT Journal Article
T1 Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study
A1 Moral Rubio, Carlos
A1 Balugo, Paloma
A1 Fraile Pereda, Adela
A1 Pytel, Vanesa
A1 Fernández Romero, Lucía
A1 Delgado Alonso, Cristina
A1 Delgado Álvarez, Alfonso
A1 Matías-Guiu Guía, Jorge
A1 Matias Guiu, Jordi A.
A1 Ayala Rodrigo, José Luis
AB 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.
PB MPDI
SN 2076-3425
YR 2021
FD 2021-09-23
LK https://hdl.handle.net/20.500.14352/4750
UL https://hdl.handle.net/20.500.14352/4750
LA eng
NO Moral Rubio, C., Balugo, P., Fraile Pereda, A. et al. «Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study». Brain Sciences, vol. 11, n.o 10, septiembre de 2021, p. 1262. DOI.org (Crossref), https://doi.org/10.3390/brainsci11101262.
NO Instituto de Salud Carlos III/Fondo Europeo de Desarrollo Regional
DS Docta Complutense
RD 10 abr 2025