EEG in the diagnostics of Alzheimer's disease

摘要

Dementia caused by Alzheimer's disease (AD) is worldwide one of the main medical and social challenges for the next years and decades. An automated analysis of changes in the electroencephalogram (EEG) of patients with AD may contribute to improving the quality of medical diagnoses. In this paper, measures based on uni- and multi-variate spectral densities are studied in order to measure slowing and, in greater detail, reduced synchrony in the EEG signals. Hereby, an EEG segment is interpreted as sample of a (weakly) stationary stochastic process. The spectral density was computed using an indirect estimator. Slowing was considered by calculating the spectral power in predefined frequency bands. As measures for synchrony between single EEG signals, we analyzed coherences, partial coherences, bivariate and conditional Granger causality; for measuring synchrony between groups of EEG signals, we considered coherences, partial coherences, bivariate and conditional Granger causality between the respective first principal components of each group, and dynamic canonic correlations. As measure for local synchrony within a group, the amount of variance explained by the respective first principal component of static and dynamic principal component analysis was investigated. These measures were exemplarily computed for resting state EEG recordings from 83 subjects diagnosed with probable AD. Here, the severity of AD is quantified by the Mini Mental State Examination score.