Lattice 123 pattern for automated Alzheimer's detection using EEG signal
Article
Dogan, Sengul, Barua, Prabal Datta, Baygin, Mehmet, Tuncer, Turker, Tan, Ru-San, Ciaccio, Edward J., Fujita, Hamido, Devi, Aruna and Acharya, U. Rajendra. 2024. "Lattice 123 pattern for automated Alzheimer's detection using EEG signal." Cognitive Neurodynamics. 18, pp. 2503-2519. https://doi.org/10.1007/s11571-024-10104-1
| Article Title | Lattice 123 pattern for automated Alzheimer's detection using EEG signal |
|---|---|
| ERA Journal ID | 3179 |
| Article Category | Article |
| Authors | Dogan, Sengul, Barua, Prabal Datta, Baygin, Mehmet, Tuncer, Turker, Tan, Ru-San, Ciaccio, Edward J., Fujita, Hamido, Devi, Aruna and Acharya, U. Rajendra |
| Journal Title | Cognitive Neurodynamics |
| Journal Citation | 18, pp. 2503-2519 |
| Number of Pages | 17 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | Netherlands |
| ISSN | 1871-4080 |
| 1871-4099 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-024-10104-1 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11571-024-10104-1 |
| Abstract | This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database. |
| Keywords | Lattice123 pattern; AD detection; EEG signal classification; Feature engineering; Self-organized classification model |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Byline Affiliations | Firat University, Turkey |
| School of Business | |
| Erzurum Technical University, Turkey | |
| National Heart Centre, Singapore | |
| Columbia University Irving Medical Center, United States | |
| University of Technology Malaysia, Malaysia | |
| University of Granada, Spain | |
| Iwate Prefectural University, Japan | |
| University of the Sunshine Coast | |
| School of Mathematics, Physics and Computing | |
| Centre for Health Research |
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https://research.usq.edu.au/item/z8649/lattice-123-pattern-for-automated-alzheimer-s-detection-using-eeg-signal
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