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BioMed Research International
Volume 2015 (2015), Article ID 536863, 13 pages
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.


This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.