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Unconsciousness state identification using phase information extracted by wavelet and Hilbert transform
File(s)
root.pdf (212.89 KB)
Accepted version
Author(s)
Berthelot, ME
Witon, A
Li, L
Type
Conference Paper
Abstract
This work aims to determine features for the distinction of coma and quasi brain death (QBD) consciousness states by implementing an algorithm for extracting phase information from EEG data using Wavelet/ Hilbert Transform. The relationship between the EEG data recorded from pairs of different electrodes is then quantified by calculating phase synchrony using Shannon entropy, as a phase synchrony index (PSI). Statistical analysis was used to evaluate the significant pairs of electrodes for the features extracted in the different frequency bands. The findings suggest confirm that both Wavelet and Hilbert Transform based phase synchrony analysis provide similar results. In particular, Hilbert Transform might be a more suitable method for phase synchrony analysis to characterize coma or QBD brain states for lower frequency bands. Using non-parametric statistical tools is reliable and does not require strong assumption on the dataset distribution. The algorithm is not designed to be a diagnosis tool; it rather serves as a secondary test to confirm diagnosis. It also has a potential contribution for the systematic distinction of brain states in other areas of EEG-based research.
Date Issued
2017-11-07
Date Acceptance
2017-03-10
Citation
Digital Signal Processing (DSP), 2017 22nd International Conference on, 2017
URI
DOI
ISBN
9781538618950
ISSN
2165-3577
Publisher
IEEE
Journal / Book Title
Digital Signal Processing (DSP), 2017 22nd International Conference on
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
Digital Signal Processing (DSP) 2017
Publication Status
Published
Start Date
2017-08-23
Finish Date
2017-08-25
Coverage Spatial
London, UK
Date Publish Online
2017-11-07