Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model

Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model

Biological signals have a dynamic and non-linear nature, and hence nonlinear analysis is important for understanding the signals. In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Even...

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Bibliographic Details
Main Authors: Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computer Methods and Programs in Biomedicine Update
Subjects:
Alzheimer's Disease (AD)
Attention Mechanism
Deep Learning
EEG
ERP
Nonlinear features
Online Access:http://www.sciencedirect.com/science/article/pii/S2666990025000163
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