EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures

Walter S. Pritchard, Dennis W. Duke, Kerry L. Coburn, Norman C. Moore, Karen A. Tucker, Michael W. Jann, Russell M. Hostetler

Research output: Contribution to journalArticle

139 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)118-130
Number of pages13
JournalElectroencephalography and Clinical Neurophysiology
Volume91
Issue number2
DOIs
StatePublished - Aug 1994

Keywords

  • Alzheimer disease
  • Classification of Alzheimer patients
  • Neural-net modeling
  • Non-linear EEG measures
  • Spectral-band EEG change

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