Predictive Diagnostic Approach to Dementia and Dementia Subtypes Using Wireless and Mobile Electroencephalography: A Pilot Study

Abstract

Background:

Developing a screening method for mild cognitive impairment in the aging population and intervening early in the progression of dementia based on such a method, remains challenging. Electroencephalography (EEG) is a noninvasive and sensitive tool to assess the functional activity of the brain, and wireless and mobile EEG (wmEEG) could serve as an alternative screening technique that is widely tolerable in patients with dementia from the preclinical to severe stage.

Materials and Methods:

Using wmEEG, we recorded bioelectrical activity (BA) from the forehead in 101 individuals with dementia and nondementia controls (NCs) during 4 tasks and investigated which task could differentiate dementia from NC.

Results:

We found significant differences in three power spectra of the time–frequency analysis (3–4, 5–7, and 17–23 Hz) between dementia and NC under an eyes-open condition and a significant consistent difference in a specific slow alpha power spectrum (6–8 Hz) between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) under an eyes-closed condition. These results were confirmed by classification analysis using a deep learning method based on the whole wmEEG data sets, in which the accuracy of discriminating dementia from NC under the eyes-open condition was higher than that under the eyes-closed condition (0.71 vs. 0.52, respectively). Moreover, the accuracy of discriminating AD from DLB under the eyes-closed condition was higher than that under the eyes-open condition (0.77 vs. 0.64, respectively).

Conclusion:

The result of this pilot study suggests that wmEEG can be a useful tool for recording BA, and that analyzing BA may help to detect early dementia and discriminate dementia subtypes effectively and objectively.