Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals
Article
Dogan, Sengul, Baygin, Mehmet, Tasci, Burak, Loh, Hui Wen, Barua, Prabal D., Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2023. "Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals." Cognitive Neurodynamics. 17 (3), pp. 647-659. https://doi.org/10.1007/s11571-022-09859-2
| Article Title | Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals |
|---|---|
| ERA Journal ID | 3179 |
| Article Category | Article |
| Authors | Dogan, Sengul, Baygin, Mehmet, Tasci, Burak, Loh, Hui Wen, Barua, Prabal D., Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
| Journal Title | Cognitive Neurodynamics |
| Journal Citation | 17 (3), pp. 647-659 |
| Number of Pages | 13 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | Netherlands |
| ISSN | 1871-4080 |
| 1871-4099 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-022-09859-2 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11571-022-09859-2 |
| Abstract | Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome. |
| Keywords | AD detection; Primate brain modelling; Feature engineering; Feature extraction; EEG signal classification |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Firat University, Turkey |
| Ardahan University, Turkiye | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| School of Business | |
| University of Technology Sydney | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan |
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