NDLI: Tensor Based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG

Content Provider	IEEE Xplore Digital Library
Author	Kouchaki, S. Sanei, S. Arbon, E.L. Derk-Jan Dijk
Copyright Year	2001
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 algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition. 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 electroencephalogram has been analyzed 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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Starting Page	1
Ending Page	9
Page Count	9
File Size	1465258
File Format	PDF
ISSN	15344320
Volume Number	23
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sleep Tensile stress Noise Source separation Electroencephalography Time series analysis Narrowband tensor factorization Electroencephalogram (EEG) empirical mode decomposition (EMD) single channel source separation singular spectrum analysis (SSA) sleep
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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