DICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals

DICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals

Objective: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects a significant percentage of the elderly. EEG has emerged as a promising tool for the timely diagnosis and classification of AD or other dementia types. This paper proposes a novel approach to AD E...

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Bibliographic Details
Main Authors: Andreas Miltiadous, Emmanouil Gionanidis, Katerina D. Tzimourta, Nikolaos Giannakeas, Alexandros T. Tzallas
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Alzheimer’s disease
deep learning
detection
EEG
Frontotemporal dementia
transformers
Online Access:https://ieeexplore.ieee.org/document/10179900/

Internet

https://ieeexplore.ieee.org/document/10179900/

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