Please use this identifier to cite or link to this item:
Title: Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery
Authors: Liu, Q
Wei, Q
Fan, SZ
Lu, CW
Lin, TY
Abbod, MF
Shieh, JS
Keywords: Multiscale entropy;Electroencephalography;Depth of anesthesia;Adaptive resampling procedure
Issue Date: 2012
Publisher: MDPI AG
Citation: Entropy, 14(6): 978-992, (May 2012)
Abstract: Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.
ISSN: 1099-4300
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf457.2 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.