Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study

Authors

  • Noor Kamal Al-Qazzaz Department of Biomedical Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad/ Baghdad / Iraq
  • Sawal Hamid Bin Mohd Ali Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia/ UKM Bangi/ Selangor 43600/ Malaysia Centre of Advanced Electronic and Communication Engineering, Department of Electrical/ Electronic and Systems Engineering/ Universiti Kebangsaan Malaysia/ Selangor 43600/ Malaysia
  • Siti Anom Ahmad Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang/ Selangor 43400/ Malaysia Malaysian Research Institute on Ageing (MyAgeing™)/ Universiti Putra Malaysia/ Serdang/ Selangor 43400/ Malaysia

DOI:

https://doi.org/10.22153/kej.2023.09.002

Abstract

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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Published

2023-12-01

How to Cite

Working Memory Classification Enhancement of EEG Activity in Dementia: A Comparative Study. (2023). Al-Khwarizmi Engineering Journal, 19(4), 29-41. https://doi.org/10.22153/kej.2023.09.002

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