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International Journal of Alzheimer’s Disease
Volume 2011, Article ID 539621, 10 pages
Research Article

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

1School of Electrical & Electronic Engineering (EEE), Nanyang Technological University (NTU), 50 Nanyang Avenue, Singapore 639798
2Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600 036, India
3Brain Functions Laboratory, Inc., Yokohama 226-8510, Japan
4Laboratoire SIGMA 75231 Paris Cedex 05, ESPCI ParisTech, France
5Center for Neural Science, Korea Institute of Science and Technology (KIST), 39-1 Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791, Republic of Korea
6Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea
7Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-Shi, Saitama 351-0106, Japan

Received 15 December 2010; Revised 10 February 2011; Accepted 15 February 2011

Academic Editor: Florinda Ferreri

Copyright © 2011 Justin Dauwels 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.


Medical studies have shown that EEG of Alzheimer's disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.