Linguistic Features Identify Alzheimer's Disease in Narrative Speech
Linguistic Features Identify Alzheimer's Disease in Narrative Speech
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DOI：
10.3233/jad-150520
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发表时间：
2016-01-01
影响因子：
4
通讯作者：
Rudzicz, Frank
中科院分区：
文献类型：
--
作者：
Fraser, Kathleen C.;Meltzer, Jed A.;Rudzicz, Frank
背景：虽然记忆障碍是阿尔茨海默病（AD）的主要症状，但语言障碍可能是一个重要的标志。相对较少的语言研究在AD量化的损害连接speech.Objective：我们的目标是展示国家的最先进的准确性，自动识别阿尔茨海默氏病从短叙事样本引发的图片描述task，并揭示显着的语言因素与统计因素分析。数据来自DementiaBank语料库，其中167名诊断为“可能”或“可能”AD的患者提供了240个叙述样本，97名对照提供了另外的233个。我们从成绩单中计算了一些语言变量，并从相关的音频文件中计算了声学变量，并使用这些变量来训练机器学习分类器，以区分AD参与者和健康对照者。要检查AD的语言障碍的异质性的程度，我们遵循探索性因素分析这些措施的语音和语言与斜promax旋转，并提供解释所产生的factors.Results：我们获得国家的最先进的分类准确率超过81%，区分个人与AD从那些没有基于他们的语言的图片描述任务的短样本。出现了四个明显的因素：结论：现代机器学习和语言学分析在疑似AD的评估和聚类中将越来越有用。
Background: Although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.Objective: We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.Methods: Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.Results: We obtain state-of-the-art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.Conclusion: Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.