{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:12:19Z","timestamp":1773943939759,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2012,5,25]],"date-time":"2012-05-25T00:00:00Z","timestamp":1337904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e14060978","type":"journal-article","created":{"date-parts":[[2012,5,25]],"date-time":"2012-05-25T11:15:22Z","timestamp":1337944522000},"page":"978-992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery"],"prefix":"10.3390","volume":"14","author":[{"given":"Quan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China"}]},{"given":"Qin","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6849-8453","authenticated-orcid":false,"given":"Shou-Zen","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2217-3231","authenticated-orcid":false,"given":"Cheng-Wei","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao, 220, Taiwan"},{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, 32003, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1374-5189","authenticated-orcid":false,"given":"Tzu-Yu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao, 220, Taiwan"},{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, 32003, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8515-7933","authenticated-orcid":false,"given":"Maysam F.","family":"Abbod","sequence":"additional","affiliation":[{"name":"School of Engineering and Design, Brunel University, London, UB8 3PH, UK"}]},{"given":"Jiann-Shing","family":"Shieh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, 32003, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2012,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1037\/0033-2909.132.2.180","article-title":"Meditation states and traits: EEG, ERP, and neuro-imaging studies","volume":"132","author":"Cahn","year":"2006","journal-title":"Psychol. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.brainres.2006.03.010","article-title":"Comparative analysis of event-related potentials during Go\/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention","volume":"1104","author":"Kirmizialsan","year":"2006","journal-title":"Brain Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/bja\/aeh182","article-title":"Comparability of Narcotrend TM index and bispectral index during propofol anaesthesia","volume":"93","author":"Kreuer","year":"2004","journal-title":"Br. J. Anaesth."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1053\/bean.2000.0137","article-title":"Monitoring the depth of anaesthesia using bispectral analysis and closed-loop controlled administration of propofol","volume":"15","author":"Mortier","year":"2001","journal-title":"Best Practice Res. Clin. Anaesth."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1213\/01.ane.0000183668.53139.fc","article-title":"A comparison of state and response entropy versus bispectral index values during the perioperative period","volume":"102","author":"White","year":"2006","journal-title":"Anesth. Analg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1097\/00000542-200411000-00009","article-title":"Narcotrend(R) does not adequately detect the transition between awareness and unconsciousness in surgical patients","volume":"101","author":"Schneider","year":"2004","journal-title":"Anesthesiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1097\/00000542-200011000-00029","article-title":"Development and clinical application of electroencephalographic bispectrum monitoring","volume":"93","author":"Johansen","year":"2000","journal-title":"Anesthesiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1109\/TBME.2006.870230","article-title":"Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthetic induction","volume":"53","author":"Koskinen","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","first-page":"H2039","article-title":"Physiological time series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1097\/ALN.0b013e31818d6c55","article-title":"Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: An analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots","volume":"109","author":"Jordan","year":"2008","journal-title":"Anesthesiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"068102","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale entropy analysis of complex physiologic time series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"046010","DOI":"10.1088\/1741-2560\/7\/4\/046010","article-title":"Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia","volume":"7","author":"Li","year":"2010","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"169","DOI":"10.4015\/S1016237209001222","article-title":"Multiscale entropy analysis of EEG recordings in epileptic rats","volume":"21","author":"Ouyang","year":"2009","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"089803\/1","DOI":"10.1103\/PhysRevLett.92.089803","article-title":"Comment on multiscale entropy analysis of complex physiologic time series","volume":"92","author":"Nikulin","year":"2004","journal-title":"Phys. Rev. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1152\/ajpheart.1994.266.4.H1643","article-title":"Physiological time-series analysis: What does regularity quantify?","volume":"266","author":"Pincus","year":"1994","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_19","unstructured":"Oppenheim, A., Schafer, R., and Buck, J. (1989). Discrete Time Signal Processing, Prentice-Hall, Inc.. [2nd ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1109\/TSP.2011.2106779","article-title":"Filter bank property of multivariate empirical mode decomposition","volume":"59","author":"Rehman","year":"2010","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wei, Q., Huang, D.W., Lu, J.W., Liu, Q., and Shieh, J.S. (2011, January 11\u201313). Intelligent real time data mining of depth of aneasthesia. Proceedings of the Thirteenth International Conference on Intelligent System and Control (IASTED 13th), Cambridge, UK.","DOI":"10.2316\/P.2011.744-063"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adap. Data Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1098\/rspa.2009.0502","article-title":"Multivariate empirical mode decomposition","volume":"466","author":"Rehman","year":"2010","journal-title":"Proc. Roy. Soc. A"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/14\/6\/978\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:50:29Z","timestamp":1760219429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/14\/6\/978"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,5,25]]},"references-count":24,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2012,6]]}},"alternative-id":["e14060978"],"URL":"https:\/\/doi.org\/10.3390\/e14060978","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,5,25]]}}}