Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs
Joint Authors
Noh, Gyu-Jeong
Shin, Hyun-Chool
Cha, Kab-Mun
Choi, Byung-Moon
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-16
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In this paper, we propose novel methods for measuring depth of anesthesia (DOA) by quantifying dominant information flow in multichannel EEGs.
Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain.
Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement.
Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition.
Three bipartitioning methods are used to detect the dominant information flow in entire EEG channels and the dominant information flow is quantified by calculating information entropy.
High correlation between the proposed measures and the plasma concentration of propofol is confirmed from the experimental results of clinical data in 39 subjects.
To illustrate the performance of the proposed methods more easily we present the results for multichannel EEG on a two-dimensional (2D) brain map.
American Psychological Association (APA)
Cha, Kab-Mun& Choi, Byung-Moon& Noh, Gyu-Jeong& Shin, Hyun-Chool. 2017. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140907
Modern Language Association (MLA)
Cha, Kab-Mun…[et al.]. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1140907
American Medical Association (AMA)
Cha, Kab-Mun& Choi, Byung-Moon& Noh, Gyu-Jeong& Shin, Hyun-Chool. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140907
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140907