Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

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Publication Details

Author list: Fernández A
Publisher: Elsevier: 12 months
Publication year: 2008
Volume number: 43
Issue number: 1
Start page: 75
End page: 85
Number of pages: 11
ISSN: 0933-3657
Languages: English-Great Britain (EN-GB)


Abstract

Objectives: In this pilot study, we intended to assess whether a
procedure based on blind source separation (BSS) and subsequent partial
reconstruction of magnetoencephalogram (MEG) recordings might enhance
the differences between MEGs from Alzheimer's disease (AD) patients and
elderly control subjects. Materials and methods: We analysed MEG
background activity recordings acquired with a 148-channel whole-head
magnetometer from 21 AD patients and 21 control subjects. Artefact-free
epochs of 20 s were blindly decomposed using the algorithm for multiple
unknown signals extraction (AMUSE), which arranges the extracted
components by decreasing linear predictability. Thus, the components of
diverse epochs and subjects could be easily compared. Every component
was characterised with its median frequency and spectral entropy
(denoted by fmedian and SpecEn, respectively). The
differences between subject groups in these variables were statistically
evaluated to find out which components could improve the subject
classification. Then, these significant components were used to
partially reconstruct the MEG recordings. Results: The statistical
analysis showed that the AMUSE components which provided the largest
differences between demented patients and control subjects were ordered
together. Considering this analysis, we defined two subsets, denoted by
BSS-{15,35} and BSS-{20,30}, which included 21 components (15-35) and 11
components (20-30), respectively. We partially reconstructed the MEGs
with these subsets. Then, the classification performance was computed
with a leave-one-out cross-validation procedure for the case where no
BSS was applied and for the partial reconstructions BSS-{15,35} and
BSS-{20,30}. The BSS and component selection procedure improved the
classification accuracy from 69.05% to 83.33% using fmedian
with BSS-{15,35} and from 61.91% to 73.81% using SpecEn with
BSS-{20,30}. Conclusion: These preliminary results lead us to think that
the proposed procedure based on BSS and selection of significant
components may improve the classification of AD patients using
straightforward features from MEG recordings. © 2008 Elsevier B.V. All
rights reserved.


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