Tensor based singular spectrum analysis for automatic scoring of sleep EEG
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33 - 702260
- Type
- D - Journal article
- DOI
-
10.1109/TNSRE.2014.2329557
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Article number
- -
- First page
- 1
- Volume
- 23
- Issue
- 1
- ISSN
- 1534-4320
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- n collaboration with Surrey Sleep Centre (SSC), for the first time a tensor-based singular spectrum analysis (SSA) for recovering the underlying sources from single-channel electroencephalograms was developed. It outperforms traditional SSA because it can detect transients such as spindles and exploit data frequency, subject and segment diversities to highlight the desired subspace. This enables accurate estimation of sleep stages for subjects in normal condition, with sleep restriction and sleep extension. This, currently being used by Prof. Dijk (SSC), effectively assesses mental and sleep abnormalities for shift-based workers and became a platform for a new PhD research (by Sara Mahvash)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
