Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing

Área de publicación Neurociencia Cognitiva
Tipo de publicación Articles
Lugar de publicación Journal of Neural Engineering
Fecha de publicación 2021
Autores Mario A. Parra - Huggins, C.J., Escudero, J., Scally, B., Anghinah, R., Vitória Lacerda De Araújo, A,, Basile, L.F., Abasolo, D.

The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

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