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

Biology

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