The diagnosis of Alzheimer's disease (AD) at the present time remains dependent upon clinical symptomatology. Lifetime accuracy in the best clinics remains 86-89 percent, and mean diagnostic delay in the clinical course of the disease remains 3.6 years after symptomatic onset. Although EEG is an obvious quantitative parameter related to the illness, its limitation is the absence of an identified set of features that discriminates AD EEG abnormalities form those due to confounding conditions. As a consequence, no computerized method exists up to date that can reliably detect those abnormalities. The objective of this study is to develop a robust computerized method for early detection of AD in EEG. We explore the ability of specifically designed and trained recurrent neural networks (RNN), combined with wavelet preprocessing, to discriminate between EEGs of early onset AD patients and their age-matched control subjects. We have used a similar approach previously for predicting the onset of epileptic seizure in EEG. The RNNs are chosen because they can implement extremely nonlinear decision boundaries and possess memory of the state, which is crucial for the considered task. The result on eyes-closed resting EEG reveals particularly favorable network behavior when applied to wavelet-filtered subbands as opposed to original signal data.

Date Published: 4 December 2000
PDF: 8 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408570

Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)