Early detection of alzheimer’s disease by blind source separation, time frequency representation, and bump modeling of EEG signals

Authors:
François Vialatte;Andrzej Cichocki;Gérard Dreyfus;Toshimitsu Musha;Sergei L. Shishkin;Rémi Gervais
Affiliations:
ESPCI (ParisTech), Laboratoire d’Electronique (CNRS UMR 7084), Paris, France;BSI RIKEN ABSP Lab, Saitama, Japan;ESPCI (ParisTech), Laboratoire d’Electronique (CNRS UMR 7084), Paris, France;Brain Functions Laboratory Inc., Kanagawa, Japan;BSI RIKEN ABSP Lab, Saitama, Japan;Equipe Neurobiologie de la Mémoire Olfactive, Institut des Sciences Cognitives, (UMR 5015 CNRS UCB), Bron Cedex, France
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

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Abstract

The early detection Alzheimer’s disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using electroencephalographic (EEG) recordings: first a blind source separation algorithm is applied to extract the most significant spatio-temporal components; these components are subsequently wavelet transformed; the resulting time-frequency representation is approximated by sparse “bump modeling”; finally, reliable and discriminant features are selected by orthogonal forward regression and the random probe method. These features are fed to a simple neural network classifier. The method was applied to EEG recorded in patients with Mild Cognitive Impairment (MCI) who later developed AD, and in age-matched controls. This method leads to a substantially improved performance (93% correctly classified, with improved sensitivity and specificity) over classification results previously published on the same set of data. The method is expected to be applicable to a wide variety of EEG classification problems.