Paper Title :Artificial Alzheimer’s EEG Dataset Generation Using Generative Adversarial Networks
Author :Husam Al-Hammadi, Mohammed Nasser, Ebubekir Koc
Article Citation :Husam Al-Hammadi ,Mohammed Nasser ,Ebubekir Koc ,
(2023 ) " Artificial Alzheimer’s EEG Dataset Generation Using Generative Adversarial Networks " ,
International Journal of Advances in Science, Engineering and Technology(IJASEAT) ,
pp. 136-141,
Volume-11,Issue-2
Abstract : Alzheimer's disease (AD) is a progressive and irreversible brain disorder that affects memory, thinking, and
behavior. It is the leading cause of dementia. However, early diagnosis is critical in increasing the quality and quantity of
patient care. The primary method in early diagnosis is electroencephalography (EEG). It has been proposed to assess
abnormal brain patterns related to Alzheimer's disease at the cortical level for its low cost, noninvasive, and portability.
Furthermore, artificial intelligence tools have been essential in developing models that facilitate disease diagnosis and
detection. Deep learning is a promising approach for such applications; however, it requires a reliable dataset. Due to the
patient's rights, researchers may not be able to access a sufficient dataset to train the network. This study aims to propose a
model to address this issue. A Generative Adversarial Network (GNN) model is presented to generate an artificial EEG
dataset for Alzheimer's disease. It may be employed to understand brain processes better and make more accurate medical
diagnoses for Alzheimer's disease using deep learning tools. The results show that the proposed model can generate reliable
artificial EEG signals for Alzheimer's disease in related channels.
Keywords - Alzheimer's Disease(AD), Electroencephalography (EEG), Alzheimer's EEG modelling, Artificial EEG Dataset,
Generative Adversarial Network (GAN)
Type : Research paper
Published : Volume-11,Issue-2
DOIONLINE NO - IJASEAT-IRAJ-DOIONLINE-20271
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Copyright: © Institute of Research and Journals
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Published on 2023-12-18 |
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