Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements

•Artificial neural networks with stacked autoencoders detected Alzheimer’s dementia patients based on EEG and structural MRI variables.•Classification accuracies over control participants reached 80% (EEG), 85% (MRI), and 89% (both).•These results motivate future multi-centric, harmonized prospectiv...

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Veröffentlicht in:	Clinical neurophysiology Jg. 132; H. 1; S. 232 - 245
Hauptverfasser:	Ferri, Raffaele, Babiloni, Claudio, Karami, Vania, Triggiani, Antonio Ivano, Carducci, Filippo, Noce, Giuseppe, Lizio, Roberta, Pascarelli, Maria T., Soricelli, Andrea, Amenta, Francesco, Bozzao, Alessandro, Romano, Andrea, Giubilei, Franco, Del Percio, Claudio, Stocchi, Fabrizio, Frisoni, Giovanni B., Nobili, Flavio, Patanè, Luca, Arena, Paolo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.01.2021
Schlagworte:	Alzheimer’s Disease (AD) Low-resolution brain electromagnetic tomography (LORETA) Neurology Resting State Electroencephalography (rsEEG) Stacked Artificial Neural Networks (ANNs) with Autoencoders Alzheimer’s Disease (AD) Resting State Electroencephalography (rsEEG) Low-resolution brain electromagnetic tomography (LORETA) Stacked Artificial Neural Networks (ANNs) with Autoencoders
ISSN:	1388-2457, 1872-8952, 1872-8952
Online-Zugang:	Volltext
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