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A Comparison of Connected Speech Tasks for Detecting Early Alzheimer's Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning

Clarke, N; Barrick, TR; Garrard, P (2021) A Comparison of Connected Speech Tasks for Detecting Early Alzheimer's Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning. FRONTIERS IN COMPUTER SCIENCE, 3. p. 634360. ISSN 2624-9898 https://doi.org/10.3389/fcomp.2021.634360
SGUL Authors: Barrick, Thomas Richard

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Abstract

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.

Item Type: Article
Additional Information: Copyright © 2021 Clarke, Barrick and Garrard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: machine learning, natural language processing, dementia, connected speech, alzheimer's disease, mild cognitive impairment, discourse, spontaneous speech
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: FRONTIERS IN COMPUTER SCIENCE
ISSN: 2624-9898
Dates:
DateEvent
31 May 2021Published
4 May 2021Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
MR/N013638/1Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
Web of Science ID: WOS:000663557300001
URI: https://openaccess.sgul.ac.uk/id/eprint/113409
Publisher's version: https://doi.org/10.3389/fcomp.2021.634360

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