title: Multiway array decomposition analysis of EEGs in Alzheimer's disease.
abstract: 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.
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology-KAIST, Daejeon 305-701, Republic of Korea.
authors.forename: Charles-Francois V
authors.initials: CF
authors.lastname: Latchoumane
authors.forename: Francois-Benois
authors.initials: FB
authors.lastname: Vialatte
authors.forename: Jordi
authors.initials: J
authors.lastname: Solé-Casals
authors.forename: Monique
authors.initials: M
authors.lastname: Maurice
authors.forename: Sunil R
authors.initials: SR
authors.lastname: Wimalaratna
authors.forename: Nigel
authors.initials: N
authors.lastname: Hudson
authors.forename: Jaeseung
authors.initials: J
authors.lastname: Jeong
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted