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

被引:59
作者
Al-Qazzaz, Noor Kamal [1 ,2 ]
Ali, Sawal Hamid Bin Mohd [1 ]
Ahmad, Siti Anom [3 ,4 ]
Islam, Mohd Shabiul [5 ]
Escudero, Javier [6 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Elect Elect & Syst Engn, Bangi 43600, Selangor, Malaysia
[2] Baghdad Univ, Dept Biomed Engn, Al Khwarizmi Coll Engn, Baghdad 47146, Iraq
[3] Univ Putra Malaysia, Dept Elect & Elect Engn, Fac Engn, Serdang 43400, Selangor, Malaysia
[4] Univ Putra Malaysia, Malaysian Res Inst Ageing MyAgeing, Serdang 43400, Selangor, Malaysia
[5] Multimedia Univ, Fac Engn, Cyberjaya 63100, Selangor, Malaysia
[6] Univ Edinburgh, Inst Digital Commun, Sch Engn, Edinburgh EH9 3FB, Midlothian, Scotland
关键词
Electroencephalography; ICA-WT; Relative power; Permutation entropy; Fractal dimension; Vascular dementia; Mild cognitive impairment; MULTISCALE PERMUTATION ENTROPY; ALZHEIMERS-DISEASE PATIENTS; FRACTAL DIMENSION; COMPLEXITY; RECOGNITION; ALGORITHMS; SELECTION; DYNAMICS; THETA; ICA;
D O I
10.1007/s11517-017-1734-7
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:137 / 157
页数:21
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