Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs
The aim of the present study was to select the optimal denoising technique that helps in discriminating dementia in the early stages and illustrating its degree of severity. In this paper, a comparative analysis of three denoising techniques, which are wavelet (WT), automatic independent component a...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/36770/ http://psasir.upm.edu.my/id/eprint/36770/1/Comparison%20of%20the%20effectiveness%20of%20AICA-WT%20technique%20in%20discriminating%20vascular%20dementia%20EEGs.pdf |
| _version_ | 1848848425225814016 |
|---|---|
| author | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom |
| author_facet | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom |
| author_sort | Al-Qazzaz, Noor Kamal |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The aim of the present study was to select the optimal denoising technique that helps in discriminating dementia in the early stages and illustrating its degree of severity. In this paper, a comparative analysis of three denoising techniques, which are wavelet (WT), automatic independent component analysis (AICA) rejection, and automatic hybrid technique using independent component analysis and wavelet (AICA-WT), has been conducted to select the optimal denoising technique. Two approaches have been used to extract meaningful features these are Permutation entropy (PEn) and Higuchi's fractal dimension (FD) from Electroencephalography (EEG) dataset of 5 vascular dementia (VD) patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 healthy subjects during working memory task (WMT). k-nearest neighbors (kNN) classifier has been used. The results show that the AICA-WT denoising technique improved the kNN classification accuracy from 88.15% for WT and 89.26% for AICA rejection to 90.37%for AICA-WT denoising technique. These results suggest AICA-WT consistently improves the discrimination of VD, MCI patients and control normal subjects which are useful for dementia early detection. |
| first_indexed | 2025-11-15T09:34:18Z |
| format | Conference or Workshop Item |
| id | upm-36770 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:34:18Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-367702020-06-16T02:17:15Z http://psasir.upm.edu.my/id/eprint/36770/ Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom The aim of the present study was to select the optimal denoising technique that helps in discriminating dementia in the early stages and illustrating its degree of severity. In this paper, a comparative analysis of three denoising techniques, which are wavelet (WT), automatic independent component analysis (AICA) rejection, and automatic hybrid technique using independent component analysis and wavelet (AICA-WT), has been conducted to select the optimal denoising technique. Two approaches have been used to extract meaningful features these are Permutation entropy (PEn) and Higuchi's fractal dimension (FD) from Electroencephalography (EEG) dataset of 5 vascular dementia (VD) patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 healthy subjects during working memory task (WMT). k-nearest neighbors (kNN) classifier has been used. The results show that the AICA-WT denoising technique improved the kNN classification accuracy from 88.15% for WT and 89.26% for AICA rejection to 90.37%for AICA-WT denoising technique. These results suggest AICA-WT consistently improves the discrimination of VD, MCI patients and control normal subjects which are useful for dementia early detection. IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/36770/1/Comparison%20of%20the%20effectiveness%20of%20AICA-WT%20technique%20in%20discriminating%20vascular%20dementia%20EEGs.pdf Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Ahmad, Siti Anom (2018) Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs. In: 2nd International Conference on BioSignal Analysis, Processing and System (ICBAPS 2018), 24-26 July 2018, Kuching, Sarawak, Malaysia. (pp. 109-112). 10.1109/ICBAPS.2018.8527412 |
| spellingShingle | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title | Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title_full | Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title_fullStr | Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title_full_unstemmed | Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title_short | Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs |
| title_sort | comparison of the effectiveness of aica-wt technique in discriminating vascular dementia eegs |
| url | http://psasir.upm.edu.my/id/eprint/36770/ http://psasir.upm.edu.my/id/eprint/36770/ http://psasir.upm.edu.my/id/eprint/36770/1/Comparison%20of%20the%20effectiveness%20of%20AICA-WT%20technique%20in%20discriminating%20vascular%20dementia%20EEGs.pdf |