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.
Med Biol Eng Comput. 2018 Jan;56(1):137-157. doi: 10.1007/s11517-017-1734-7. Epub 2017 Nov 8.
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.
中风幸存者更容易出现认知障碍和痴呆。痴呆症检测是支持个性化医疗保健的一个挑战。本研究分析了 5 名血管性痴呆（VaD）患者、15 名中风相关轻度认知障碍（MCI）患者和 15 名对照健康受试者在工作记忆（WM）任务期间的脑电图（EEG）背景活动。本研究的目的有两个。首先，使用 QR 分解的模糊邻域保持分析（FNPAQR）增强 VaD、中风相关 MCI 患者和对照受试者的区分；其次，提取并研究与对照受试者相比特征化中风后痴呆患者的频谱特征。使用独立成分分析和小波分析（ICA-WT）去噪技术记录和分析了 19 个通道。使用 ANOVA 计算包括相对功率（RP）和功率比在内的线性光谱功率，以测试 VaD 和中风相关 MCI 患者的 EEG 主导频率是否减慢。使用排列熵（PerEn）和分形维数（FD）等非线性特征测试不规则性和复杂性的程度，结果表明 VaD 和中风相关 MCI 患者的不规则性和复杂性明显低于对照组（ANOVA；p ˂ 0.05）。本研究首次使用 QR 分解的模糊邻域保持分析（FNPAQR）降维技术分析痴呆症患者的脑电图背景活动。使用支持向量机（SVM）和 k-最近邻（kNN）分类器检测中风后患者的损伤。已经进行了比较研究，以检查使用 FNPAQR 降维技术与 SVM 和 kNN 分类器的有效性。FNPAQR 与 SVM 和 kNN 的分类准确率分别为 91.48%和 89.63%，而不使用 FNPAQR 的 SVM 和 kNN 的分类准确率分别为 70%和 67.78%，分别用于 VaD、中风相关 MCI 和对照患者的分类。因此，与对照健康受试者相比，EEG 可能是一种可靠的指标，可以检查对 VaD 和中风相关 MCI 患者敏感的简明标志物。
