Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study
The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity....
| Үндсэн зохиолчид: | , , |
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| Формат: | Өгүүллэг |
| Хэл сонгох: | English |
| Хэвлэсэн: |
Al-Khwarizmi College of Engineering – University of Baghdad
2023-12-01
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| Цуврал: | Al-Khawarizmi Engineering Journal |
| Онлайн хандалт: | https://www.alkej.uobaghdad.edu.iq/index.php/alkej/article/view/849 |
| Тойм: | The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique and the improved binary gravitation search ( ) optimization algorithm as a channel selection method has been conducted. The NN classification accuracy was increased from 86.67% to 88.09% and 90.52% using the dimensionality reduction technique and the channel selection algorithm, respectively. According to the findings, reliably enhances discrimination of , , and participants. Therefore, WT, entropy features, IBGSA and NN classifiers provide a valid dementia index for looking at EEG background activity in patients with and .
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| ISSN: | 1818-1171 2312-0789 |