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Published VersionCC BY V4.0, Open Access
Journal article
Lattice 123 pattern for automated Alzheimer's detection using EEG signal
Cognitive Neurodynamics, Vol.18, pp.2503-2519
2024
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.
Details
- Title
- Lattice 123 pattern for automated Alzheimer's detection using EEG signal
- Authors
- Sengul Dogan (Corresponding Author) - Fırat UniversityPrabal Datta Barua - University of Southern QueenslandMehmet Baygin - Erzurum Technical UniversityTurker Tuncer - Fırat UniversityRu-San Tan - National Heart Centre SingaporeEdward J. Ciaccio - Columbia University Irving Medical CenterHamido Fujita - University of Technology MalaysiaAruna Devi - University of the Sunshine Coast, Queensland, School of Education and Tertiary AccessU. Rajendra Acharya - University of Southern Queensland
- Publication details
- Cognitive Neurodynamics, Vol.18, pp.2503-2519
- Publisher
- Springer Dordrecht
- Date published
- 2024
- DOI
- 10.1007/s11571-024-10104-1
- ISSN
- 1871-4099; 1871-4080
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Data Availability
- Not applicable.
- Organisation Unit
- Indigenous and Transcultural Research Centre; School of Education and Tertiary Access
- Language
- English
- Record Identifier
- 991016196602621
- Output Type
- Journal article
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- Domestic collaboration
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- Neurosciences
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