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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.

Author information

1
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia. noorbmemsc@gmail.com.
2
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad, 47146, Iraq. noorbmemsc@gmail.com.
3
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
4
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia.
5
Malaysian Research Institute of Ageing (MyAgeing), Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
6
Faculty of Engineering, Multimedia Universiti, MMU, 63100, Cyberjaya, Selangor, Malaysia.
7
Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3FB, UK.

Abstract

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.

KEYWORDS:

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

PMID:
29119540
DOI:
10.1007/s11517-017-1734-7
[Indexed for MEDLINE]

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