Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition
The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA)...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59464/ http://psasir.upm.edu.my/id/eprint/59464/1/Classification%20enhancement%20for%20post-stroke%20dementia%20using%20fuzzy%20neighborhood%20preserving%20analysis%20with%20QR-decomposition.pdf |
| _version_ | 1848853928262762496 |
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| author | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Rodriguez, Javier Escudero |
| author_facet | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Rodriguez, Javier Escudero |
| author_sort | Al-Qazzaz, Noor Kamal |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI. |
| first_indexed | 2025-11-15T11:01:46Z |
| format | Conference or Workshop Item |
| id | upm-59464 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:01:46Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-594642018-03-06T04:09:02Z http://psasir.upm.edu.my/id/eprint/59464/ Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Rodriguez, Javier Escudero The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI. IEEE 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59464/1/Classification%20enhancement%20for%20post-stroke%20dementia%20using%20fuzzy%20neighborhood%20preserving%20analysis%20with%20QR-decomposition.pdf Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Ahmad, Siti Anom and Rodriguez, Javier Escudero (2017) Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17), 11-15 July 2017, Jeju Island, Korea. (pp. 3174-3177). 10.1109/EMBC.2017.8037531 |
| spellingShingle | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Rodriguez, Javier Escudero Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title | Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title_full | Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title_fullStr | Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title_full_unstemmed | Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title_short | Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition |
| title_sort | classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with qr-decomposition |
| url | http://psasir.upm.edu.my/id/eprint/59464/ http://psasir.upm.edu.my/id/eprint/59464/ http://psasir.upm.edu.my/id/eprint/59464/1/Classification%20enhancement%20for%20post-stroke%20dementia%20using%20fuzzy%20neighborhood%20preserving%20analysis%20with%20QR-decomposition.pdf |