University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Tensor based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG.

Kouchaki, S, Sanei, S, Arbon, E and Dijk, DJ (2014) Tensor based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG. IEEE Trans Neural Syst Rehabil Eng.

[img] Text
Tensor_Based_Singular_Spectrum.pdf - ["content_typename_UNSPECIFIED" not defined]
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (732kB)
[img] Text (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (33kB)

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: Article
Authors :
NameEmailORCID
Kouchaki, SUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Arbon, EUNSPECIFIEDUNSPECIFIED
Dijk, DJUNSPECIFIEDUNSPECIFIED
Date : 13 June 2014
Identification Number : 10.1109/TNSRE.2014.2329557
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:12
Last Modified : 31 Oct 2017 17:00
URI: http://epubs.surrey.ac.uk/id/eprint/806233

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800