Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis
Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mil...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/72309/ http://psasir.upm.edu.my/id/eprint/72309/1/Discrimination%20of%20stroke-related%20mild%20cognitive%20impairment%20.pdf |
| _version_ | 1848857087350669312 |
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| author | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Rodriguez, Javier Escudero |
| author_facet | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Rodriguez, Javier Escudero |
| author_sort | Al-Qazzaz, Noor Kamal |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA−WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects. |
| first_indexed | 2025-11-15T11:51:58Z |
| format | Article |
| id | upm-72309 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:51:58Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-723092020-05-04T00:41:58Z http://psasir.upm.edu.my/id/eprint/72309/ Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Rodriguez, Javier Escudero Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA−WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects. Springer 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72309/1/Discrimination%20of%20stroke-related%20mild%20cognitive%20impairment%20.pdf Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Ahmad, Siti Anom and Islam, Mohd Shabiul and Rodriguez, Javier Escudero (2018) Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis. Medical & Biological Engineering & Computing volume, 56 (1). 137 - 157. ISSN 0140-0118; ESSN: 1741-0444 https://link.springer.com/article/10.1007/s11517-017-1734-7#citeas 10.1007/s11517-017-1734-7 |
| spellingShingle | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Islam, Mohd Shabiul Rodriguez, Javier Escudero Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title | Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title_full | Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title_fullStr | Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title_full_unstemmed | Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title_short | Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis |
| title_sort | discrimination of stroke-related mild cognitive impairment and vascular dementia using eeg signal analysis |
| url | http://psasir.upm.edu.my/id/eprint/72309/ http://psasir.upm.edu.my/id/eprint/72309/ http://psasir.upm.edu.my/id/eprint/72309/ http://psasir.upm.edu.my/id/eprint/72309/1/Discrimination%20of%20stroke-related%20mild%20cognitive%20impairment%20.pdf |