[Zurück]

H. Garn, M. Waser, M. Deistler, T. Benke, P. Dal-Bianco, G. Ransmayr, H. Schmidt, G. Sanin, P. Santer, G. Caravias, S. Seiler, D. Grossegger, W. Frühwirth, R. Schmidt:
"Electroencephalographic Complexity Markers Explain Neuropsychological Test Scores in Alzheimer´s Disease";
in: "Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on", herausgegeben von: IEEE; IEEE Xplore, 2014, S. 496 - 499.

We investigated the correlation of Alzheimer´s
disease (AD) severity as measured by the Mini-Mental State
Examination (MMSE) to the signal complexity measures automutual
information, Shannon entropy and Tsallis entropy in 79
patients with probable AD from the multi-centric Prospective
Dementia Database Austria (PRODEM). Using quadratic (linear)
regressions, auto-mutual information explained up to 48%
(43%), Shannon entropy up to 48% (37%) and Tsallis entropy
up to 49% (35%) of the variations in MMSE scores, all at left
temporal (T7) electrode site. The steepest slope of the linear
regression was found for auto-mutual information (Δy/Δx =
36). For Shannon and Tsallis entropy, slopes were less steep.
Comparing to traditional slowing measures, complexity
measures yielded higher coefficients of determination. We conclude
that auto-mutual information is well suited to characterize
disease severity in mild to moderate AD.

biomedical electrodes diseases electroencephalography entropy medical signal processing neurophysiology regression analysis

http://dx.doi.org/10.1109/BHI.2014.6864411