Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks

Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Md. Shabiul Islam, Javier Escudero

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA-WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA-WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA-WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p ˂ 0.05). The AICA-WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA-WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.

Original languageEnglish
JournalSensors (Basel, Switzerland)
Volume17
Issue number6
DOIs
Publication statusPublished - 8 Jun 2017
Externally publishedYes

Fingerprint

Wavelet Analysis
electroencephalography
Independent component analysis
Electroencephalography
Short-Term Memory
wavelet analysis
Artifacts
Wavelet transforms
artifacts
Data storage equipment
Vascular Dementia
Stroke
strokes
Dementia
Brain
impairment
Analysis of variance (ANOVA)
brain
Analysis of Variance
Deceleration

Keywords

  • electroencephalography
  • independent component analysis
  • mild cognitive impairment
  • spectral analysis
  • vascular dementia
  • wavelet

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks. / Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Md. Shabiul; Escudero, Javier.

In: Sensors (Basel, Switzerland), Vol. 17, No. 6, 08.06.2017.

Research output: Contribution to journalArticle

Al-Qazzaz, Noor Kamal ; Hamid Bin Mohd Ali, Sawal ; Ahmad, Siti Anom ; Islam, Md. Shabiul ; Escudero, Javier. / Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks. In: Sensors (Basel, Switzerland). 2017 ; Vol. 17, No. 6.
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