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Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study



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Moral Rubio, Carlos and Balugo, Paloma and Fraile Pereda, Adela and Pytel, Vanesa and Fernández Romero, Lucía and Delgado Alonso, Cristina and Delgado Álvarez, Alfonso and Matias Guiu, Jorge and Matias Guiu, Jordi A. and Ayala Rodrigo, José Luis (2021) Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study. Brain Sciences, 11 (10). p. 1262. ISSN 2076-3425

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Official URL: https://doi.org/10.3390/brainsci11101262


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.

Item Type:Article
Uncontrolled Keywords:electroencephalography; resting-state; primary progressive aphasia; biomarkers machine learning; K-Nearest Neighbors; frontotemporal dementia; Alzheimer’s disease; graph theory
Subjects:Sciences > Computer science > Bioinformatics
Medical sciences > Medicine > Neurosciences
ID Code:70878
Deposited On:01 Mar 2022 16:00
Last Modified:02 Mar 2022 08:09

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