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International Journal of Alzheimer’s Disease
Volume 2011, Article ID 259069, 7 pages
Research Article

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

1Laboratoire SIGMA, ESPCI ParisTech, 75231 Paris, France
2Laboratory for Advanced Brain Signal Processing, Riken BSI, Wako Saitama 351-0198, Japan
3School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), b39798, Singapore
4Brain Functions Laboratory Inc., Takatsu Kawasaki-shi, Yokohama 226-8510, Japan

Received 11 January 2011; Revised 18 March 2011; Accepted 28 March 2011

Academic Editor: Florinda Ferreri

Copyright © 2011 François-B. Vialatte et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were 𝜃 (3.5–7.5 Hz), 𝛼1 (7.5–9.5 Hz), 𝛼2 (9.5–12.5 Hz), and 𝛽 (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the 𝜃 range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.