论文标题
自动分析轻度认知临界检测的分类言语流利度：一种非线性语言独立方法
Automatic analysis of Categorical Verbal Fluency for Mild Cognitive Impartment detection: a non-linear language independent approach
论文作者
论文摘要
阿尔茨海默氏病（AD）是世界上痴呆症的主要原因之一，患者患有严重的残疾，有时会充分依赖。在先前的阶段，轻度认知障碍（MCI）会产生认知损失，但不足以干扰日常生活。这项工作是从言语中选择AD检测的生物标志物，是一项广泛的跨研究，用于诊断阿尔茨海默氏症。特别是在这项工作中，已经使用了检测MCI的任务。该任务分析了分类的言语流利度。自动分类是通过经典线性特征，Castiglioni分形维度和置换熵进行的。最后，最相关的功能是通过方差分析测试选择的。 MCI的有希望结果超过50％
Alzheimer's disease (AD) is one the main causes of dementia in the world and the patients develop severe disability and sometime full dependence. In previous stages Mild Cognitive Impairment (MCI) produces cognitive loss but not severe enough to interfere with daily life. This work, on selection of biomarkers from speech for the detection of AD, is part of a wide-ranging cross study for the diagnosis of Alzheimer. Specifically in this work a task for detection of MCI has been used. The task analyzes Categorical Verbal Fluency. The automatic classification is carried out by SVM over classical linear features, Castiglioni fractal dimension and Permutation Entropy. Finally the most relevant features are selected by ANOVA test. The promising results are over 50% for MCI
