EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures.
Attempts to classify Alzheimer's disease (AD) subjects versus controls using spectral-band measures of electroencephalographic (EEG) data typically achieve around 80% success. This study assessed the ability of adding non-linear EEG measures and using a neural-net classification procedure to improve this performance level. The non-linear EEG measures were estimated correlation dimension ("dimensional complexity," or DCx) and saturation (degree of leveling-off of DCx with increasing embedding dimension). In a sample of 39 subjects (14 ADs, 25 controls), it was found that (a) the addition of non-linear EEG measures improved the classification accuracy of the AD/control status of subjects, and (b) a back-percolation neural net predictively classified the subjects much better than the standard linear techniques of multivariate discriminant analysis or nearest-neighbor discriminant analysis.
Pritchard
W S
WS
Biological Research Division, R&D, Bowman Gray Technical Center 611-12, R.J. Reynolds Tobacco Company, Winston-Salem, NC 27102.
Duke
D W
DW
Coburn
K L
KL
Moore
N C
NC
Tucker
K A
KA
Jann
M W
MW
Hostetler
R M
RM
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000503:physiopathology*; D000704:Analysis of Variance; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D003198:Computer Simulation; D016002:Discriminant Analysis; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer