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

Noor Kamal Al-qazzaz, Sawal Ali, Siti Anom Ahmad, Javier Escudero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationAdvances in Biomedical Engineering (ICABME), 2017 Fourth International Conference on
Number of pages4
ISBN (Electronic)2377-5696
DOIs
Publication statusPublished - 7 Dec 2017

Fingerprint

Dive into the research topics of 'Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing'. Together they form a unique fingerprint.

Cite this