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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 3521261, 14 pages
Research Article

Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs

1Department of Electronic Engineering, Soongsil University, Seoul, Republic of Korea
2Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, Seoul, Republic of Korea

Correspondence should be addressed to Hyun-Chool Shin

Received 7 October 2016; Revised 2 December 2016; Accepted 28 December 2016; Published 16 March 2017

Academic Editor: Saeid Sanei

Copyright © 2017 Kab-Mun Cha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.