Next Article in Journal
Entropy Measures of Street-Network Dispersion: Analysis of Coastal Cities in Brazil and Britain
Previous Article in Journal
On the Entropy of a Two Step Random Fibonacci Substitution
Article Menu

Export Article

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

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 / Revised: 4 August 2013 / Accepted: 16 August 2013 / Published: 23 August 2013
View Full-Text   |   Download PDF [437 KB, uploaded 24 February 2015]   |  

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 sample entropy; electroencephalography; depth of anesthesia; multivariate empirical mode decomposition; artificial neural networks; receiver operating characteristic curve
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top