A Comparison of Connected Speech Tasks for Detecting Early Alzheimer's Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning
A Comparison of Connected Speech Tasks for Detecting Early Alzheimer's Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning
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DOI：
10.3389/fcomp.2021.634360
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发表时间：
2021-05-31
影响因子：
2.6
通讯作者：
Garrard, Peter
中科院分区：
文献类型：
--
作者：
Clarke, Natasha;Barrick, Thomas R.;Garrard, Peter
阿尔茨海默病(AD)具有较长的临床前期，因此有必要及早发现，包括轻度认知障碍(MCI)。使用自然语言处理和机器学习对连接语音进行计算分析已被发现可以指示疾病，并可用作早期诊断的快速、可扩展的测试。然而，Cookie Theft图片描述任务一直是人们关注的焦点，这一任务一直受到批评。研究人员招募了50名参与者--25名健康对照组(HC)，25名轻度AD或MCI(AD+MCI)--这些参与者完成了五项相关的语音任务：图片描述、对话地图阅读任务、回忆过学的叙事、程序性回忆和对一本无单词图画书的叙述。从每个成绩单中自动提取高维语言特征集，并用于训练支持向量机对组进行分类。表现各不相同，HC与AD+MCI分类的准确率从使用图画书叙事的62%到使用过度学习的叙事特征的78%不等。这项研究表明，重要的是，言语任务的条件对生成的语篇有影响，这会影响样本长度以外的AD检测的准确性。此外，我们报告了使用不同任务进行分类的重要特征，表明关注Cookie Theft图片描述任务可能会缩小对早期AD病理如何影响语音的理解。
Alzheimer's disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited - 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) - and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.