A Novel EEG-Based Deep Approach for Diagnosing Alzheimer's Disease Using Attention-Time-Aware LSTM-Reference-Cited by-同舟云学术

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A Novel EEG-Based Deep Approach for Diagnosing Alzheimer's Disease Using Attention-Time-Aware LSTM

Published:2023-12-27 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)
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Author:

Khosravi M.¹,Parsaei Hossein¹,Alghanim Mohammed²,Rezaee Khosro³

Affiliation:

1. School of Medicine, Shiraz University of Medical Sciences,Department of Medical Physics and Engineering,Iran

2. School of Information Technology, Zarqa University,Cyber Security Department,Zarqa,Jordan

3. School of Engineering Meybod University,Deprtement of Biomedical Enginering,Iran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10590066/10590003/10590201.pdf?arnumber=10590201

Reference33 articles.

1. Automatic detection of Alzheimer’s disease from EEG signals using low-complexity orthogonal wavelet filter banks

2. Behavioural symptoms in patients with Alzheimer's disease and their association with cognitive impairment

3. The Alzheimer’s Disease Clinical Spectrum

4. Fully automated discrimination of Alzheimer’s disease using resting-state electroencephalography signals

5. Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings

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