论文标题
分析自动检测轻度认知临界的分析：一种深度学习方法
Analysis of Disfluencies for automatic detection of Mild Cognitive Impartment: a deep learning approach
论文作者
论文摘要
所谓的轻度认知障碍（MCI）或认知损失出现在阿尔茨海默氏病（AD）之前的前阶段，但似乎并不足够严重，无法干扰日常生活的独立能力，因此通常不会接受适当的诊断。它的检测是医疗专家要解决的一个挑战性问题。这项工作为旨在支持MCI诊断的言语和反射的自动分析提供了一项新的建议。该方法包括通过卷积神经网络（CNN）和非线性多相模型的深入学习。此外，使用最相关的功能，使用了非参数Mann-Whitney U-Testt和支持向量机属性（SVM）评估。
The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a challenging issue to be addressed by medical specialists. This work presents a novel proposal based on automatic analysis of speech and disfluencies aimed at supporting MCI diagnosis. The approach includes deep learning by means of Convolutional Neural Networks (CNN) and non-linear multifeature modelling. Moreover, to select the most relevant features non-parametric Mann-Whitney U-testt and Support Vector Machine Attribute (SVM) evaluation are used.
