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Med Biol Eng Comput. 2018 Jan;56(1):137-157. doi: 10.1007/s11517-017-1734-7. Epub 2017 Nov 8.

Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis.

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Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad, 47146, Iraq.
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia.
Malaysian Research Institute of Ageing (MyAgeing), Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
Faculty of Engineering, Multimedia Universiti, MMU, 63100, Cyberjaya, Selangor, Malaysia.
Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3FB, UK.


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.


Electroencephalography; Fractal dimension; ICA−WT; Mild cognitive impairment; Permutation entropy; Relative power; Vascular dementia

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