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EEG markers for early detection and characterization of vascular dementia during working memory tasks


Citation

Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Islam, Md. Shabiul and Ahmad, Siti Anom and Rodriguez, Javier Escudero (2016) EEG markers for early detection and characterization of vascular dementia during working memory tasks. In: 2016 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 4-8 Dec. 2016, Kuala Lumpur, Malaysia. (pp. 347-351).

Abstract / Synopsis

The aim of the this study was to reveal markers using spectral entropy (SpecEn), sample entropy (SampEn) and Hurst Exponent (H) from the electroencephalography (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task. EEG artifacts were removed using independent component analysis technique and wavelet technique. With ANOVA (p < 0.05), SpecEn was used to test the hypothesis of slowing the EEG signal down in both VaD and MCI compared to control subjects, whereas the SampEn and H features were used to test the hypothesis that the irregularity and complexity in both VaD and MCI were reduced in comparison with control subjects. SampEn and H results in reducing the complexity in VaD and MCI patients. Therefore, SampEn could be the EEG marker that associated with VaD detection whereas H could be the marker for stroke-related MCI identification. EEG could be as a valuable marker for inspecting the background activity in the identification of patients with VaD and stroke-related 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/IECBES.2016.7843471
Publisher: IEEE
Keywords: Electroencephalography; Hurst exponent; ICA-WT; mild cognitive impairment; Sample entropy; Spectral entropy; Vascular dementia
Depositing User: Nabilah Mustapa
Date Deposited: 07 Jun 2017 16:34
Last Modified: 07 Jun 2017 16:34
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/IECBES.2016.7843471
URI: http://psasir.upm.edu.my/id/eprint/55681
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