Preliminary Study of EEG Characterization Using Power Spectral Analysis in Post-stroke Patients with Cognitive Impairment

Abstract

Post-stroke dementia (PSD) or post-stroke cognitive impairment can occur in one-third of stroke sufferers. Therefore, we need a detection protocol so that patients get treatment early. Electroencephalogram (EEG) analysis is one of the methods to study deteriorating brain function where visual observation is commonly used. However, this method requires expert experience and time-consuming. Therefore, in this study, a method for characterizing EEG waves in post-stroke patients with cognitive impairment is proposed by calculating and analyzing quantitative EEG (QEEG) parameters. This study proposes a linear QEEG method through the power spectral analysis approach to characterize post-stroke patients with cognitive impairments and normal subjects. This study used a resting-awake EEG dataset collected from nineteen participants consisting of ten normal subjects, five post-stroke patients with mild cognitive impairment, and four post-stroke patients with dementia. The experiment results showed significant differences in the relative power between the three groups. These include (1) increase in delta activity and simultaneously a decrease in alpha, beta and gamma activity in dementia patients, (2) Significant differences (p-value < 0.05) on these bands are most commonly found on the frontal area electrodes and (3) there is linearity between power spectral density and the severity of dementia. This preliminary study showed that relative power analysis could be a discriminant feature among normal, post-stroke patients with mild cognitive impairment and post-stroke patients with dementia. It is hoped that the proposed method can be used to assist doctors in the early detection of post-stroke dementia and monitor the progress of dementia.