Authors:
Shohei Kato
1
;
Hidetoshi Endo
2
;
Risako Nagata
2
;
Takuto Sakuma
1
and
Keita Watanabe
1
Affiliations:
1
Nagoya Institute of Technology, Japan
;
2
National Center for Geriatrics and Gerontology, Japan
Keyword(s):
Verbally-based Cognitive Training to Prevent Cognitive Impairment from Elderlies, Early Detection of Dementia, Cerebral Blood Flow Activation during a Cognitive Task, Functional near Infrared Spectroscopy (fNIRS).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Clinical Problems and Applications
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
ICT, Ageing and Disability
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
With the goal of promoting a fruitful and healthy longevity society, this paper presents a verbally-based cognitive task and an early detection method of dementia and mild cognitive impairment for elderly. As designed with conscious of daily conversation, the task is done by verbally responding to questionnaire. An elderly firstly talks about the topics of favorite season, travel, gourmet, and daily life, and then he/she does three cognitive tasks of reminiscence, category recall, and working memory. With the use of the functional near-infrared spectroscopy (fNIRS), which can measure cerebral blood flow activation non-invasively, we had collected 42 CHs fNIRS signals on frontal and right and left temporal areas from 22 elderly participants (7 males and 15 females between ages of 64 to 89) during cognitive tests in a specialized medical institute. All participates are classified into three clinical groups: elderly individuals with cognitively normal controls (CN), patients with mild c
ognitive impairment (MCI), and mild Alzheimer’s disease (AD). In this paper, we report a task effect measurement of the verbally-based cognitive task by the statistical tests of fNIRS signals, and then report the examination of the detection performance by cross-validation using proposed Bayesian classifier, which can discriminate among elderly individuals with three clinical groups: CN, MCI, and AD. Consequently, empirical result indicated that total accuracy rate is more than 95% and the result suggests that proposed approach is adequate practical to screen the elderly with cognitive impairment.
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