Electroencephalographic complexity markers explain neuropsychological test scores in Alzheimer´s disease

Author

Garn, H. ; Waser, M. ; Deistler, M. ; Benke, T. ; Dal-Bianco, P. ; Ransmayr, G. ; Schmidt, Heidemarie ; Sanin, G. ; Santer, P. ; Caravias, G. ; Seiler, S. ; Grossegger, D. ; Fruehwirt, W. ; Schmidt, R.

Author_Institution

AIT Austrian Inst. of Technol. GmbH, Vienna, Austria

fYear

2014

fDate

1-4 June 2014

Firstpage

496

Lastpage

499

Abstract

We investigated the correlation of Alzheimer´s disease (AD) severity as measured by the Mini-Mental State Examination (MMSE) to the signal complexity measures auto-mutual 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.

Keywords

biomedical electrodes; diseases; electroencephalography; entropy; medical signal processing; neurophysiology; regression analysis; Alzheimer´s disease severity; MMSE scores; Mini-Mental State Examination; PRODEM; Shannon entropy; Tsallis entropy; automutual information; electroencephalographic complexity markers; left temporal electrode site; linear regression; multicentric Prospective Dementia Database Austria; neuropsychological test scores; quadratic regressions; signal complexity; traditional slowing measures; Alzheimer´s disease; Complexity theory; Electrodes; Electroencephalography; Electronic mail; Entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on

Conference_Location

Valencia

Type

conf

DOI

10.1109/BHI.2014.6864411

Filename

6864411