Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic

The Mini-Mental State Examination (MMSE) is widely used in clinics to screen for low cognitive status. However, it is limited in that it requires examiners to be present; and has fixed questions that constrain its repeated use. Thus, the MMSE cannot be used as a daily assessment to facilitate early...

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Autores principales:	Shuqiong Wu, Taku Matsuura, Fumio Okura, Yasushi Makihara, Chengju Zhou, Kota Aoki, Ikuhisa Mitsugami, Yasushi Yagi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Cognitive impairment dementia dual-task machine learning MCI MMSE Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1368ba115414449d8dac4c3797d24c69
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id	oai:doaj.org-article:1368ba115414449d8dac4c3797d24c69
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spelling	oai:doaj.org-article:1368ba115414449d8dac4c3797d24c692021-11-18T00:09:25ZDetecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic2169-353610.1109/ACCESS.2021.3126067https://doaj.org/article/1368ba115414449d8dac4c3797d24c692021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605691/https://doaj.org/toc/2169-3536The Mini-Mental State Examination (MMSE) is widely used in clinics to screen for low cognitive status. However, it is limited in that it requires examiners to be present; and has fixed questions that constrain its repeated use. Thus, the MMSE cannot be used as a daily assessment to facilitate early detection of cognitive impairment. To address this issue, we developed an automated system to detect older adults with lower MMSE scores by analyzing performance during a dual task involving stepping and calculation, which can be used repeatedly because its questions were randomly created. Leveraging this advantage, this paper proposes a learning-based method to detect subjects with lower MMSE scores using multiple trials with the dual-task system. We investigated various patterns for effectively combining the features acquired during multiple continuous trials, and analyzed the sensitivity of the number <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> of trials on detection performance to find the optimal <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> via experiments. We compared our approach with previous methods and demonstrated the superiority of our strategy. Using the cross-trial feature, our approach achieved an overall performance (sensitivity + specificity) as high as 1.79 for detecting older adults whose MMSE score is equal to or less than 23 (indicate a relatively high probability of dementia), and 1.75 for detecting older adults whose MMSE score is equal to or less than 27 (indicative of a relatively high probability of mild cognitive impairment (MCI)).Shuqiong WuTaku MatsuuraFumio OkuraYasushi MakiharaChengju ZhouKota AokiIkuhisa MitsugamiYasushi YagiIEEEarticleCognitive impairmentdementiadual-taskmachine learningMCIMMSEElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150268-150282 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cognitive impairment dementia dual-task machine learning MCI MMSE Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Cognitive impairment dementia dual-task machine learning MCI MMSE Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shuqiong Wu Taku Matsuura Fumio Okura Yasushi Makihara Chengju Zhou Kota Aoki Ikuhisa Mitsugami Yasushi Yagi Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
description	The Mini-Mental State Examination (MMSE) is widely used in clinics to screen for low cognitive status. However, it is limited in that it requires examiners to be present; and has fixed questions that constrain its repeated use. Thus, the MMSE cannot be used as a daily assessment to facilitate early detection of cognitive impairment. To address this issue, we developed an automated system to detect older adults with lower MMSE scores by analyzing performance during a dual task involving stepping and calculation, which can be used repeatedly because its questions were randomly created. Leveraging this advantage, this paper proposes a learning-based method to detect subjects with lower MMSE scores using multiple trials with the dual-task system. We investigated various patterns for effectively combining the features acquired during multiple continuous trials, and analyzed the sensitivity of the number <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> of trials on detection performance to find the optimal <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> via experiments. We compared our approach with previous methods and demonstrated the superiority of our strategy. Using the cross-trial feature, our approach achieved an overall performance (sensitivity + specificity) as high as 1.79 for detecting older adults whose MMSE score is equal to or less than 23 (indicate a relatively high probability of dementia), and 1.75 for detecting older adults whose MMSE score is equal to or less than 27 (indicative of a relatively high probability of mild cognitive impairment (MCI)).
format	article
author	Shuqiong Wu Taku Matsuura Fumio Okura Yasushi Makihara Chengju Zhou Kota Aoki Ikuhisa Mitsugami Yasushi Yagi
author_facet	Shuqiong Wu Taku Matsuura Fumio Okura Yasushi Makihara Chengju Zhou Kota Aoki Ikuhisa Mitsugami Yasushi Yagi
author_sort	Shuqiong Wu
title	Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
title_short	Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
title_full	Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
title_fullStr	Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
title_full_unstemmed	Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic
title_sort	detecting lower mmse scores in older adults using cross-trial features from a dual-task with gait and arithmetic
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/1368ba115414449d8dac4c3797d24c69
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Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic

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