Tensor based singular spectrum analysis for automatic scoring of sleep EEG

Kouchaki, S., Sanei, S. ORCID: 0000-0002-3437-2801, Arbon, E.L. and Dijk, D.-J., 2015. Tensor based singular spectrum analysis for automatic scoring of sleep EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (1), pp. 1-9. ISSN 1534-4320

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Abstract

A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis (SSA) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition (SVD). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep EEG has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts.

Item Type: Journal article
Publication Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Creators: Kouchaki, S., Sanei, S., Arbon, E.L. and Dijk, D.-J.
Publisher: IEEE
Date: January 2015
Volume: 23
Number: 1
ISSN: 1534-4320
Identifiers:
NumberType
10.1109/TNSRE.2014.2329557DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jill Tomkinson
Date Added: 15 Feb 2018 10:24
Last Modified: 13 Apr 2018 13:51
Related URLs:
URI: http://irep.ntu.ac.uk/id/eprint/32719

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