Next Article in Journal
Deformed Exponentials and Applications to Finance
Previous Article in Journal
Implication of Negative Entropy Flow for Local Rainfall
Article Menu

Export Article

Open AccessArticle
Entropy 2013, 15(9), 3458-3470; doi:10.3390/e15093458

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

1
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
3
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan
4
Department of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao 220, Taiwan
5
School of Engineering and Design, Brunel University, London UB8 3PH, UK
6
Department of Mechanical Engineering, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li 32003, Taiwan
7
Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan
*
Author to whom correspondence should be addressed.
Received: 15 July 2013 / Revised: 6 August 2013 / Accepted: 27 August 2013 / Published: 30 August 2013
View Full-Text   |   Download PDF [1070 KB, uploaded 24 February 2015]   |  

Abstract

In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.
Keywords: electroencephalograph; sample entropy; multivariate empirical mode decomposition; depth of anesthesia electroencephalograph; sample entropy; multivariate empirical mode decomposition; depth of anesthesia
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

Wei, Q.; Liu, Q.; Fan, S.-Z.; Lu, C.-W.; Lin, T.-Y.; Abbod, M.F.; Shieh, J.-S. Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy. Entropy 2013, 15, 3458-3470.

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