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EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer’s disease

Tiago H Falk1*, Francisco J Fraga2, Lucas Trambaiolli3 and Renato Anghinah4

Author Affiliations

1 Institut National de la Recherche Scientifique, Energy, Materials, and Telecommunications, University of Quebec, Montréal, Quebec, Canada

2 Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, Brazil

3 Mathematics, Computing and Cognition Center, Universidade Federal do ABC, Santo André, Brazil

4 Reference Center of Behavioral Disturbances and Dementia, School of Medicine, Universidade de São Paulo, São Paulo, Brazil

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EURASIP Journal on Advances in Signal Processing 2012, 2012:192  doi:10.1186/1687-6180-2012-192

A correction for this article has been published in http://asp.eurasipjournals.com/content/2014/1/49

Published: 30 August 2012


Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer’s disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

Alzheimer’s disease; Modulation spectrum; Electroencephalogram; Spectral peak; Support vector machine