Name
Title | ||
|---|---|---|
Detecting Lower MMSE Scores in Older Adults Using Cross-Trial Features From a Dual-Task With Gait and Arithmetic |
Abstract | ||
|---|---|---|
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)). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3126067 | IEEE ACCESS |
Keywords | DocType | Volume |
Task analysis, Feature extraction, Dementia, Arithmetic, Standards, Sensitivity, Monitoring, Cognitive impairment, dementia, dual-task, machine learning, MCI, MMSE | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuqiong Wu | 1 | 0 | 0.34 |
| Taku Matsuura | 2 | 0 | 0.34 |
| Fumio Okura | 3 | 0 | 0.34 |
| Yasushi Makihara | 4 | 1012 | 70.67 |
| Chengju Zhou | 5 | 0 | 0.34 |
| Kota Aoki | 6 | 0 | 0.34 |
| Ikuhisa Mitsugami | 7 | 42 | 11.97 |
| Yasushi Yagi | 8 | 1752 | 186.22 |