課題実行時fNIRS脳機能計測データのベイジアンマイニングに基づく認知機能障害の３群判別

抄録

This paper presents a new trial approach to early detection of dementia in the elderly with the use of functional brain imaging during cognitive tests. We have developed a non-invasive screening system of the elderly with cognitive impairment. In addition of our previous research of speech-prosody based data-mining approach, we had started the measurement of functional brain imaging for patient having a cognitive test by using functional near-infrared spectroscopy (fNIRS). We had collected 42 CHs fNIRS signals on frontal and right and left temporal areas from 50 elderly participants (18 males and 32 females between ages of 64 to 92) during cognitive tests in a specialized medical institute. We propose a Bayesian classifier, which can discriminate among elderly individuals with three clinical groups: normal cognitive abilities (NC), patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD). The Bayesian classifier has two phases on the assumption of screening process, that firstly checks whether a suspicion of the cognitive impairment (CI) or not (NC) from given fNIRS signals; if any, and then secondly judges the degree of the impairment: cognitive impairment (MCI) or Alzheimer's disease (AD). This paper also reports the examination of the detection performance by cross-validation, and discusses the effectiveness of this study for early detection of cognitive impairment in elderly subjects. Consequently, empirical results that both the accuracy rate of AD and the predictive value of NC are equal to or more than 90\%. This suggests that proposed approach is adequate practical to screen the elderly with cognitive impairment.