Magnetoencephalogram Blind Source Separation and Component Selection Procedure to Improve the Diagnosis of Alzheimer´s Disease Patients

Author

Escudero, J. ; Hornero, R. ; Abasolo, D. ; Fernandez, A. ; Poza, Jesus

Author_Institution

Univ. of Valladolid, Valladolid

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

5437

Lastpage

5440

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

blind source separation; diseases; entropy; magnetoencephalography; medical signal processing; patient diagnosis; signal classification; signal reconstruction; spectral analysis; statistical testing; Alzheimer´s disease patient diagnosis; Lempel-Ziv complexity; MEG recordings; Student´s t-test; approximate entropy; blind source separation; component selection procedure; linear discriminant analysis; magnetoencephalogram; multiple unknown signal extraction; partial reconstruction; signal classification; spectral entropy; Alzheimer´s disease; Blind source separation; Degenerative diseases; Electroencephalography; Entropy; Frequency; Magnetic separation; Senior citizens; Signal resolution; Source separation; Aged; Algorithms; Alzheimer Disease; Artificial Intelligence; Brain; Diagnosis, Computer-Assisted; Female; Humans; Magnetoencephalography; Male; Pattern Recognition, Automated; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4353575

Filename

4353575