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Phase-based brain consciousness analysis

Li, Ling, Looney, David, Park, Cheolsoo, Tanaka, Toshiko, Cao, Jianting, Mandic, Danilo P. (2012) Phase-based brain consciousness analysis. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. . pp. 1032-1035. IEEE ISBN 978-1-4244-4119-8. E-ISBN 978-1-4577-1787-1. (doi:10.1109/EMBC.2012.6346110) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

This work provides a novel framework for identifying coma and brain death consciousness states by analysing frequency power and phase synchrony features from electroencephalogram (EEG). The proposed analysis of pairs of EEG electrodes using complex extensions of Empirical Mode Decomposition (EMD) permits the extraction of information related to the state of the brain function. Analysis on 34 subjects in the coma and quasi-brain-death states suggests that phase synchrony constitutes a feasible feature to discriminate quasi-brain-death from coma state. Thus, illustrate the effectiveness of the proposed methods for brain consciousness identification. The predictive power of the features extracted is evaluated by building classification models using support vector machine (SVM) and evaluation the models through receiver operating characteristic (ROC) analysis.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EMBC.2012.6346110
Uncontrolled keywords: biomedical electrodes;electroencephalography;feature extraction;medical signal processing;sensitivity analysis;signal classification;support vector machines;EEG electrodes;ROC analysis;SVM;brain function;classification models;coma;electroencephalogram;empirical mode decomposition;frequency power;information feature extraction;phase synchrony features;phase-based brain consciousness analysis;receiver operating characteristic analysis;support vector machine;Electrodes;Electroencephalography;Feature extraction;Frequency synchronization;Support vector machines;Automatic Data Processing;Brain Death;Consciousness;Electroencephalography;Female;Humans;Male;Predictive Value of Tests;ROC Curve
Subjects: Q Science
R Medicine
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 17 Jul 2015 16:27 UTC
Last Modified: 29 May 2019 14:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49596 (The current URI for this page, for reference purposes)
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