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BioMed Research International
Volume 2015, Article ID 536863, 13 pages
http://dx.doi.org/10.1155/2015/536863
Research Article

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

1Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
2Department of Anestheology, College of Medicine, National Taiwan University, Taipei 100, Taiwan
3Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK
4Missile & Rocket System Research Division, National Chung-Shan Institute of Science and Technology, Taoyuan, Longtan 32500, Taiwan
5Center of Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan

Received 21 May 2015; Revised 18 September 2015; Accepted 21 September 2015

Academic Editor: Stefan Rampp

Copyright © 2015 Muammar Sadrawi 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

Muammar Sadrawi, Shou-Zen Fan, Maysam F. Abbod, Kuo-Kuang Jen, and Jiann-Shing Shieh, “Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks,” BioMed Research International, vol. 2015, Article ID 536863, 13 pages, 2015. https://doi.org/10.1155/2015/536863.