@inproceedings{cb8f2150d83e4cb88e8fbdcb320e35b8,
title = "Magnetoencephalogram blind source separation and component selection procedure to improve the diagnosis of Alzheimer's disease patients",
abstract = "The aim of this study was to improve the diagnosis of Alzheimer's disease (AD) patients applying a blind source separation (BSS) and component selection procedure to their magnetoencephalogram (MEG) recordings. MEGs from 18 AD patients and 18 control subjects were decomposed with the algorithm for multiple unknown signals extraction. MEG channels and components were characterized by their mean frequency, spectral entropy, approximate entropy, and Lempel-Ziv complexity. Using Student's t-test, the components which accounted for the most significant differences between groups were selected. Then, these relevant components were used to partially reconstruct the MEG channels. By means of a linear discriminant analysis, we found that the BSS-preprocessed MEGs classified the subjects with an accuracy of 80.6%, whereas 72.2% accuracy was obtained without the BSS and component selection procedure.",
keywords = "APPROXIMATE ENTROPY, BACKGROUND ACTIVITY, COMPLEXITY, DYNAMICS",
author = "Javier Escudero and Roberto Hornero and Daniel Abasolo and Alberto Fernandez and Jesus Poza",
year = "2007",
language = "English",
isbn = "978-1-4244-0787-3",
series = "PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "5437--5440",
booktitle = "2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16",
address = "United States",
note = "29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 22-08-2007 Through 26-08-2007",
}