Ws. Pritchard et al., EEG-BASED, NEURAL-NET PREDICTIVE CLASSIFICATION OF ALZHEIMERS-DISEASEVERSUS CONTROL SUBJECTS IS AUGMENTED BY NONLINEAR EEG MEASURES, Electroencephalography and clinical neurophysiology, 91(2), 1994, pp. 118-130
Attempts to classify Alzheimer's disease (AD) subjects versus controls
using spectral-band measures of electroencephalographic (EEG) data ty
pically 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 measur
es were estimated correlation dimension (''dimensional complexity,'' o
r DCx) and saturation (degree of leveling-off of DCx with increasing e
mbedding dimension). In a sample of 39 subjects (14 ADs, 25 controls),
it was found that (a) the addition of non-linear EEG measures improve
d the classification accuracy of the AD/control status of subjects, an
d (b) a back-percolation neural net predictively classified the subjec
ts much better than the standard linear techniques of multivariate dis
criminant analysis or nearest-neighbor discriminant analysis.