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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Vydáno v:	Clinical neurophysiology Ročník 132; číslo 1; s. 232 - 245
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.01.2021
Témata:	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
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