Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease
Article
Siuly, Siuly, Alcin, Ömer Faruk, Wang, Hua, Li, Yan and Wen, Peng. 2024. "Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease
." IEEE Transactions on Emerging Topics in Computational Intelligence. 8 (2), pp. 1609-1623. https://doi.org/10.1109/TETCI.2024.3353610
Article Title | Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease |
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ERA Journal ID | 212763 |
Article Category | Article |
Authors | Siuly, Siuly, Alcin, Ömer Faruk, Wang, Hua, Li, Yan and Wen, Peng |
Journal Title | IEEE Transactions on Emerging Topics in Computational Intelligence |
Journal Citation | 8 (2), pp. 1609-1623 |
Number of Pages | 15 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2471-285X |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TETCI.2024.3353610 |
Web Address (URL) | https://ieeexplore.ieee.org/document/10414994 |
Abstract | There is no treatment that permanently cures Alzheimer's disease (AD); however, early detection can alleviate the severe effects of the disease. To support early detection of the different stages of AD (e.g., mild, moderate), the key aim of this study is to develop a computer aided diagnostic (CAD) framework that include a long short-term memory (LSTM) network using massive multi-channel electroencephalogram (EEG) data. Although EEG rhythms and EEG channels jointly possess important biomarkers that may be used for diagnosis of AD, but the traditional methods did not explore this issue in any research. To address this problem, this study introduces a new framework to identify the optimal EEG rhythms and channels required for the diagnosis of AD. The proposed framework was tested on a real-time AD EEG dataset. The results reveal that together, the gamma and beta rhythms in the channels, Cz, F4, P4, T6, Pz were the most reliable biomarker for identifying AD and the proposed LSTM based model yielded the best performance. Additionally, another mild cognitive impairment (MCI) EEG dataset was used to test the proposed approach, and the results were excellent (accuracy>99%). The proposed framework will be useful for creating a CAD system to perform automatic AD diagnosis. |
Keywords | Alzheimer's disease (AD); electroencephalography (EEG); biomarkers of EEG; long short-term memory (LSTM); mild cognitive impairment (MCI); feature extraction; classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Victoria University |
Centre for Health Research | |
Malatya Turgut Ozal University, Turkiye | |
School of Mathematics, Physics and Computing | |
School of Engineering |
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