N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals
Article
Barua, Prabal Datta, Tuncer, Turker, Baygin, Mehmet, Dogan, Sengul and Acharya, U. Rajendra. 2024. "N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals." Knowledge-Based Systems. 304. https://doi.org/10.1016/j.knosys.2024.112510
| Article Title | N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals |
|---|---|
| ERA Journal ID | 18062 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Tuncer, Turker, Baygin, Mehmet, Dogan, Sengul and Acharya, U. Rajendra |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 304 |
| Article Number | 112510 |
| Number of Pages | 16 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0950-7051 |
| 1872-7409 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2024.112510 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0950705124011444 |
| Abstract | The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 × 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)-based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %. |
| Keywords | AD detection; Attention pooling; N-body pattern; SOFE; Dementia detection |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Business |
| Firat University, Turkey | |
| Erzurum Technical University, Turkey | |
| School of Mathematics, Physics and Computing | |
| Centre for Health Research |
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