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M. Waser, M. Deistler, H. Garn, T. Benke, P. Dal-Bianco, G. Ransmayr, D. Grossegger, R. Schmidt:
"EEG in the diagnostics of Alzheimer´s disease";
Statistical Papers, 54 (2013), 2.

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. Thesemeasures 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.

Alzheimer´s Disease · Electroencephalogram · Spectral density estimation · Coherence · Partial coherence · Granger causality · Canonical correlation

http://dx.doi.org/10.1007/s00362-013-0538-6

http://publik.tuwien.ac.at/files/PubDat_219126.pdf