Entropy 2013, 15(9), 3325-3339; doi:10.3390/e15093325
Article

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

1 Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, 32003, Taiwan 2 Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan 3 School of Engineering and Design, Brunel University, London, UB8 3PH, UK 4 Missile & Rocket Systems Research Division, Chung-Shan Institute of Science and Technology, Taoyuan, Longtan, 32500, Taiwan 5 Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li, 32001, Taiwan
* Author to whom correspondence should be addressed.
Received: 21 May 2013; in revised form: 4 August 2013 / Accepted: 16 August 2013 / Published: 23 August 2013
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Abstract: EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.
Keywords: sample entropy; electroencephalography; depth of anesthesia; multivariate empirical mode decomposition; artificial neural networks; receiver operating characteristic curve

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MDPI and ACS Style

Huang, J.-R.; Fan, S.-Z.; Abbod, M.F.; Jen, K.-K.; Wu, J.-F.; Shieh, J.-S. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia. Entropy 2013, 15, 3325-3339.

AMA Style

Huang J-R, Fan S-Z, Abbod MF, Jen K-K, Wu J-F, Shieh J-S. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia. Entropy. 2013; 15(9):3325-3339.

Chicago/Turabian Style

Huang, Jeng-Rung; Fan, Shou-Zen; Abbod, Maysam F.; Jen, Kuo-Kuang; Wu, Jeng-Fu; Shieh, Jiann-Shing. 2013. "Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia." Entropy 15, no. 9: 3325-3339.

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