Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study
In: Al-Khawarizmi Engineering Journal, Jg. 19 (2023-12-01), Heft 4
Online
academicJournal
Zugriff:
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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Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study
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Autor/in / Beteiligte Person: | Noor Kamal Al-Qazzaz ; Sawal Hamid Bin Mohd Ali ; Siti Anom Ahmad |
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Zeitschrift: | Al-Khawarizmi Engineering Journal, Jg. 19 (2023-12-01), Heft 4 |
Veröffentlichung: | Al-Khwarizmi College of Engineering – University of Baghdad, 2023 |
Medientyp: | academicJournal |
ISSN: | 1818-1171 (print) ; 2312-0789 (print) |
DOI: | 10.22153/kej.2023.09.002 |
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