Interest in dementia has increased over the past few decades. Stroke is an important cause of cognitive problems. The term vascular cognitive impairment is now used to describe dementia attributed to stroke or deep white matter lesions detected on imaging. Although vascular cognitive impairment is increasingly diagnosed, Alzheimer's disease remains the most common dementia worldwide. The relationship between Alzheimer's disease and vascular cognitive impairment is unclear, although there exists significant overlap, which prompts physicians to consider them opposite ends of a disease spectrum, rather than separate entities. There is also substantial evidence that stroke risk factors such as hypertension, diabetes; lipid disorders, etc. are independently associated with an increased risk of Alzheimer's disease and vascular cognitive impairment. Evidence suggests that these risk factors have a cumulative effect on Alzheimer's disease development but not on vascular cognitive impairment. This is more marked in Alzheimer's disease patients in the presence of the ε4 allelic variant of apolipoprotein E. How these risk factors increase the risk of dementia is largely unknown. Physicians must be aware that stroke causes dementia; that vascular risk factors appear to be independent risk factors in developing dementia, and that poststroke care must include cognitive assessment.
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.