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

Noor Kamal Al-Qazzaz, Sawal Hamid Md 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 publication2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2017-October
ISBN (Electronic)9781538616420
DOIs
Publication statusPublished - 5 Dec 2017
Event4th International Conference on Advances in Biomedical Engineering, ICABME 2017 - Beirut, Lebanon
Duration: 19 Oct 201721 Oct 2017

Other

Other4th International Conference on Advances in Biomedical Engineering, ICABME 2017
CountryLebanon
CityBeirut
Period19/10/1721/10/17

Fingerprint

Electroencephalography
Signal processing
Entropy
Data storage equipment

Keywords

  • Approximation entropy
  • Dementia
  • Electroencephalography
  • ICA-WT
  • Mild cognitive impairment
  • Permutation entropy
  • Working memory

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Al-Qazzaz, N. K., Md Ali, S. H., Ahmad, S. A., & Escudero, J. (2017). Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. In 2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017 (Vol. 2017-October). [8167557] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICABME.2017.8167557

Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. / Al-Qazzaz, Noor Kamal; Md Ali, Sawal Hamid; Ahmad, Siti Anom; Escudero, Javier.

2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017. 8167557.

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

Al-Qazzaz, NK, Md Ali, SH, Ahmad, SA & Escudero, J 2017, Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. in 2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017. vol. 2017-October, 8167557, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Advances in Biomedical Engineering, ICABME 2017, Beirut, Lebanon, 19/10/17. https://doi.org/10.1109/ICABME.2017.8167557
Al-Qazzaz NK, Md Ali SH, Ahmad SA, Escudero J. Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. In 2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017. Vol. 2017-October. Institute of Electrical and Electronics Engineers Inc. 2017. 8167557 https://doi.org/10.1109/ICABME.2017.8167557
Al-Qazzaz, Noor Kamal ; Md Ali, Sawal Hamid ; Ahmad, Siti Anom ; Escudero, Javier. / Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing. 2017 4th International Conference on Advances in Biomedical Engineering, ICABME 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017.
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