Multiway array decomposition analysis of EEGs in Alzheimer's disease

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Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different: hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied two state of the art multiway array decomposition (MAD) methods to extract unique features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE), and singular value decomposition (SVD) coupled to tensor unfolding. We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease. (C) 2012 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2012-05
Language
English
Article Type
Article
Keywords

MILD COGNITIVE IMPAIRMENT; HIPPOCAMPAL ATROPHY; QUANTITATIVE EEG; SENILE DEMENTIA; CLASSIFICATION; FREQUENCY; COHERENCE; STATE; DISCRIMINATION; PROGRESSION

Citation

JOURNAL OF NEUROSCIENCE METHODS, v.207, no.1, pp.41 - 50

ISSN
0165-0270
DOI
10.1016/j.jneumeth.2012.03.005
URI
http://hdl.handle.net/10203/102671
Appears in Collection
BiS-Journal Papers(저널논문)
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