IEEE Access (Jan 2021)

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

  • Shuqiong Wu,
  • Taku Matsuura,
  • Fumio Okura,
  • Yasushi Makihara,
  • Chengju Zhou,
  • Kota Aoki,
  • Ikuhisa Mitsugami,
  • Yasushi Yagi

DOI
https://doi.org/10.1109/ACCESS.2021.3126067
Journal volume & issue
Vol. 9
pp. 150268 – 150282

Abstract

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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 $N$ of trials on detection performance to find the optimal $N$ 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)).

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