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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 232381, 16 pages
http://dx.doi.org/10.1155/2015/232381
Research Article

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

1Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan, Hubei 430070, China
2School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China
3Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan
4College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
5Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan

Received 15 June 2015; Revised 23 August 2015; Accepted 7 September 2015

Academic Editor: Dong Song

Copyright © 2015 Quan Liu 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

Quan Liu, Yi-Feng Chen, Shou-Zen Fan, Maysam F. Abbod, and Jiann-Shing Shieh, “EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks,” Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 232381, 16 pages, 2015. https://doi.org/10.1155/2015/232381.