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

Overview of attention for article published in Sensors, June 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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1 news outlet

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103 Mendeley
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Title
Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks
Published in
Sensors, June 2017
DOI 10.3390/s17061326
Pubmed ID
Authors

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

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 19%
Researcher 13 13%
Student > Master 12 12%
Student > Doctoral Student 6 6%
Lecturer 5 5%
Other 13 13%
Unknown 34 33%
Readers by discipline Count As %
Engineering 18 17%
Neuroscience 10 10%
Computer Science 9 9%
Psychology 7 7%
Medicine and Dentistry 5 5%
Other 10 10%
Unknown 44 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 June 2017.
All research outputs
#4,837,286
of 25,382,440 outputs
Outputs from Sensors
#1,892
of 24,312 outputs
Outputs of similar age
#79,384
of 331,454 outputs
Outputs of similar age from Sensors
#36
of 400 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 24,312 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,454 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 400 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.