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Title:
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Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease
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Author:
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Latchoumane, Charles-François V.; Vialatte, François B.; Solé-Casals, Jordi; Maurice, Monique; Wimalaratna, Sunil R.; Hudson, Niegel; Jaeseung, Jeonga; Cichocki, Andrej
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Other authors:
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Universitat de Vic. Grup de Recerca en Tecnologies Digitals; Universitat de Vic. Escola Politècnica Superior |
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Notes:
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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 three state of the art multiway array
decomposition (MAD) methods to extract 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). 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. |
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Subject(s):
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-Alzheimer, Malaltia d' |
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Rights:
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(c) 2012 Elsevier. Published article is available at: http://dx.doi.org/10.1016/j.jneumeth.2012.03.005
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Document type:
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Article
info:eu-repo/acceptedVersion |
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Published by:
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Elsevier
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