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Entropy 2012, 14(6), 978-992; doi:10.3390/e14060978

Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery

1
School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China
2
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan
3
Department of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao, 220, Taiwan
4
School of Engineering and Design, Brunel University, London, UB8 3PH, UK
5
Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, 32003, Taiwan
*
Author to whom correspondence should be addressed.
Received: 29 March 2012 / Revised: 9 May 2012 / Accepted: 21 May 2012 / Published: 25 May 2012
(This article belongs to the Special Issue Concepts of Entropy and Their Applications)
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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.
Keywords: multiscale entropy; electroencephalography; depth of anesthesia; adaptive resampling procedure multiscale entropy; electroencephalography; depth of anesthesia; adaptive resampling procedure
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Liu, Q.; Wei, Q.; Fan, S.-Z.; Lu, C.-W.; Lin, T.-Y.; Abbod, M.F.; Shieh, J.-S. Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery. Entropy 2012, 14, 978-992.

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