Li, Ling, Witon, Adrien, Marcora, Samuele Maria, Bowman, Howard, Mandic, Danilo P. (2014) EEG-based brain connectivity analysis of states of unawareness. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. . pp. 1002-1005. IEEE (doi:10.1109/EMBC.2014.6943762) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:48649)
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Language: English Restricted to Repository staff only |
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Official URL: http://doi.org/10.1109/EMBC.2014.6943762 |
Abstract
This work investigates phase synchrony as a neuro-marker for the identification of two brain states: coma and quasi-brain-death. Scalp electroencephalography (EEG) data of 34 patients were recorded in an intensive care unit (ICU), with 17 recordings for patients in a coma state, and 17 recordings for patients in a quasi-brain-death state. Phase synchrony was used for feature extraction from EEG recording by comparing the phase value between pairs of electrodes using an entropy based measure. In particular, we performed phase synchrony analysis in five standard frequency bands and provide visualization of the phase synchronies in matrices. The effectiveness of the phase synchrony features in each of the frequency bands are evaluated with statistical analysis. Results suggest phase synchrony for coma patients has a significant increase in the theta / alpha band compared to quasi-brain-death patients. Hence, we propose phase synchrony as a candidate for the identification of consciousness states between coma and quasi-brain-death.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.1109/EMBC.2014.6943762 |
Subjects: | Q Science |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Caroline Li |
Date Deposited: | 17 Jul 2015 15:34 UTC |
Last Modified: | 05 Nov 2024 10:32 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/48649 (The current URI for this page, for reference purposes) |
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