Identification of Partial Regression Coefficient of Alpha Wave for Estimation of MMSE Score

Abstract

The number of elderly people with dementia increases year by year, and about 20% of people in Japan over 65 years old are estimated to be dementia patients in 2030. Because dementia is difficult to be treated after the symptoms advance, early detection of dementia is important. The peak latency of P300 is one of the EEG (Electroencephalogram) features. It has been reported that the peak latency of P300 is dependent on MMSE (Mini-Mental State Examination) score. If MMSE score can be estimated by measuring the latency of P300, it is expected to be used for the early detection of dementia. However, no regression equation between MMSE score and the latency of P300 had been identified yet. The authors measured the peak latency of P300 of dementia patients who were attending the Geriatrics of Nagoya University hospital and identified a multiple regression equation with MMSE score as the objective variable, and the latency of P300, age, educational background as the explanatory variables. The 95% confidence interval of estimated MMSE score was ±3.15. This paper incorporates alpha wave as a new explanatory variable for the regression equation. The result shows that the alpha wave is statistically significant for the estimation of the MMSE score, and the 95% confidence interval of estimated MMSE score is reduced to ±3.09.