Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks
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...
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/60978/1/Automatic%20artifact%20removal%20in%20EEG%20of%20normal%20and%20demented%20individuals%20using%20ICA-WT%20during%20working%20memory%20tasks.pdf |
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| author | Al-Qazzaz, Noor Kamal Mohd Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Escudero, Javier |
| author_facet | Al-Qazzaz, Noor Kamal Mohd Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Escudero, Javier |
| author_sort | Al-Qazzaz, Noor Kamal |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T09:39:24Z |
| format | Article |
| id | upm.eprints-60978 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:39:24Z |
| publishDate | 2017 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | upm.eprints-609782019-05-14T03:25:42Z http://psasir.upm.edu.my/id/eprint/60978/ Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks Al-Qazzaz, Noor Kamal Mohd Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Escudero, Javier 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. Multidisciplinary Digital Publishing Institute 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60978/1/Automatic%20artifact%20removal%20in%20EEG%20of%20normal%20and%20demented%20individuals%20using%20ICA-WT%20during%20working%20memory%20tasks.pdf Al-Qazzaz, Noor Kamal and Mohd Ali, Sawal Hamid and Ahmad, Siti Anom and Islam, Mohd Shabiul and Escudero, Javier (2017) Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks. Sensors, 17 (6). pp. 1-25. ISSN 1424-8220 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492863/pdf/sensors-17-01326.pdf 10.3390/s17061326 |
| spellingShingle | Al-Qazzaz, Noor Kamal Mohd Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Escudero, Javier Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title | Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title_full | Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title_fullStr | Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title_full_unstemmed | Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title_short | Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks |
| title_sort | automatic artifact removal in eeg of normal and demented individuals using ica wt during working memory tasks |
| url | http://psasir.upm.edu.my/id/eprint/60978/1/Automatic%20artifact%20removal%20in%20EEG%20of%20normal%20and%20demented%20individuals%20using%20ICA-WT%20during%20working%20memory%20tasks.pdf |
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