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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 3521261, 14 pages
https://doi.org/10.1155/2017/3521261
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; rk.ca.uss@chnihs

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.

How to Cite this Article

Kab-Mun Cha, Byung-Moon Choi, Gyu-Jeong Noh, and Hyun-Chool Shin, “Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs,” Computational Intelligence and Neuroscience, vol. 2017, Article ID 3521261, 14 pages, 2017. doi:10.1155/2017/3521261