NDLI: EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease

Content Provider	IEEE Xplore Digital Library
Author	Trambaiolli, L.R. Falk, T.H. Fraga, F.J. Anghinah, R. Lorena, A.C.
Copyright Year	2011
Description	Author affiliation: Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, Brazil (Fraga, F.J.) \|\| Mathematics, Computation and Cognition Center, Universidade Federal do ABC, Brazil (Trambaiolli, L.R.; Lorena, A.C.) \|\| Reference Center of Behavioral Disturbances and Dementia, School of Medicine, Universidade de Sao Paulo, Brazil (Anghinah, R.) \|\| Institut National de la Recherche Scientifique (EMT), University of Quebec, Canada (Falk, T.H.)
Abstract	There is recent indication that Alzheimer's disease (AD) can be characterized by atypical modulation of electrophysiological brain activity caused by fibrillar amyloid deposition in specific regions of the brain, such as those related to cognition and memory. In this paper, we propose to objectively characterize EEG sub-band modulation in an attempt to develop an automated noninvasive AD diagnostics tool. First, multi-channel full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma. The temporal amplitude envelope of each sub-band is then computed via a Hilbert transformation. The proposed ‘spectro-temporal modulation energy’ feature measures the rate with which each sub-band is modulated. Modulation energy features are computed for 19 referential EEG signals and seven bipolar signals. Salient features are then selected and used to train four different classifiers, namely, support vector machines, logistic regression, classification and regression trees, and neural networks. Experiments with a database of 34 participants, 22 of which have been clinically diagnosed with probable-AD, show a neural network classifier achieving over 91% accuracy, thus significantly outperforming a classifier trained with conventional spectral-based features.
Starting Page	3828
Ending Page	3831
File Size	306117
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090951
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Modulation Alzheimer's disease Accuracy Sensitivity Feature extraction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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