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

François Vialatte, Andrzej Cichocki, Gérard Dreyfus, Toshimitsu Musha, Sergei L. Shishkin, Rémi Gervais

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

46 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages683-692
Number of pages10
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw, Poland
Duration: 11 Sep 200515 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3696 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
Country/TerritoryPoland
CityWarsaw
Period11/09/0515/09/05

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