Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

Citation

Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Ahmad, Siti Anom and Rodriguez, Javier Escudero (2017) Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. In: Fourth International Conference on Advances in Biomedical Engineering (ICABME 2017), 19-21 Oct. 2017, Beirut, Lebanon. .

Abstract

The aim of the present study was to reveal markers from the electroencephalography (EEG) using approximation entropy (ApEn) and permutation entropy (PerEn). EEGs' of 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task have EEG artifacts were removed using a wavelet (WT) based method. A t-test (p <; 0.05) was used to test the hypothesis that the irregularity (ApEn and PerEn) in MCIs was reduced in comparison with control subjects. ApEn and PerEn showed reduced irregularity in the EEGs of MCI patients. Therefore, ApEn and PerEn could be used as markers associated with MCI detection and identification and the EEG could be a valuable tool for inspecting the background activity in the identification of patients with MCI.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1109/ICABME.2017.8167557
Publisher:	IEEE
Keywords:	Electroencephalography; ICA-WT; Approximation entropy; Permutation entropy; Dementia; Mild cognitive impairment; Working memory
Depositing User:	Nabilah Mustapa
Date Deposited:	07 Mar 2018 01:09
Last Modified:	07 Mar 2018 01:09
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICABME.2017.8167557
URI:	http://psasir.upm.edu.my/id/eprint/59476
Statistic Details:	View Download Statistic

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