Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9623
- Type
- D - Journal article
- DOI
-
10.1093/brain/awx163
- Title of journal
- Brain
- Article number
- -
- First page
- 2120
- Volume
- 140
- Issue
- 8
- ISSN
- 0006-8950
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- URL
-
https://kar.kent.ac.uk/61849/
- Supplementary information
-
-
- Request cross-referral to
- 1 - Clinical Medicine
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
10
- Research group(s)
-
-
- Citation count
- 78
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research combines advanced signal processing, network analysis and machine learning to power a multivariate classification framework for detecting level of consciousness using electrical brain activity. This paper is significant because it allows clinical application to assess consciousness after severe brain injury, complementing systematic behavioural assessment and helping reduce the high misdiagnosis rate reported in patients.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -