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Hemodynamics Analysis of Patients With Mild Cognitive Impairment During Working Memory Tasks

机译:工作记忆任务期间轻度认知障碍患者的血流动力学分析

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Diagnosis of dementia in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of Alzheimer disease (AD). In this paper, we aim to classify MCI patients from healthy controls (HC) during working memory tasks using functional near-infrared spectroscopy (fNIRS). To achieve this objective, t-values and correlation coefficients are calculated to find the region of interest (ROI) channels and brain connectivity. From the ROI channels averaged over subjects, features (mean and slope) of hemodynamic responses were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracies were 73.08 % with LDA and 71.15 % with SVM. The results show that there are significant differences in the hemodynamic responses (HR) between MCI patients and healthy controls. Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.
机译:早期诊断痴呆对于预防老龄化社会中痴呆的进展很重要。轻度认知障碍(MCI)表示阿尔茨海默病(AD)的早期阶段。在本文中,我们旨在使用功能性近红外光谱(fNIRS)对在工作记忆任务期间健康对照(HC)中的MCI患者进行分类。为了实现此目标,计算t值和相关系数以找到感兴趣区域(ROI)通道和大脑连接性。从受试者平均的ROI通道中,提取血流动力学反应的特征(均值和斜率)以进行分类。提取的特征被标记为两个类别,并通过两个分类器(线性判别分析(LDA)和支持向量机(SVM))进行分类。 LDA的分类精度为73.08%,SVM的分类精度为71.15%。结果表明,MCI患者和健康对照组之间的血液动力学反应(HR)有显着差异。因此,这些结果表明有可能将fNIRS用作MCI患者的诊断工具。

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