Brief Screening Tools for Cognitive Impairment Using Motor Evoked Potentials in a Non-Active Manner

Abstract

The number of patients with dementia is increasing, and early detection of dementia is important to reduce the burden on medical institutions. Because Alzheimer's disease accounts for the largest proportion of causative diseases of dementia, this study focused on Alzheimer's disease and its preliminary stage, amnesic mild cognitive impairment. In this paper, we developed a machine learning model to detect nonactive for detecting cognitive impairment, focusing on motor evoked potentials before and after the application of transcranial magnetic stimulation to the brain. In this experiment, 24 experimental participants were measured for the motor evoked potentials. As a result of the experiment, the support vector machine was selected as the best weak learner, recording a sensitivity of 0.88 and a specificity of 0.63 in a measurement time of 15 minutes. Therefore, it is suggested that this model may be able to detect cognitive impairment.