Monitoring the depth of anesthesia using entropy features and an artificial neural network

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Show simple item record Shalbaf, R en Behnam, H en Sleigh, James en Steyn-Ross, A en Voss, LJ en
dc.coverage.spatial Netherlands en 2017-02-27T03:33:43Z en 2013-03-12 en 2013-08-15 en
dc.identifier.citation Journal of Neuroscience Methods, 15 August 2013, 218 (1), 17 - 24 en
dc.identifier.issn 0165-0270 en
dc.identifier.uri en
dc.description.abstract Monitoring the depth of anesthesia using an electroencephalogram (EEG) is a major ongoing challenge for anesthetists. The EEG is a recording of brain electrical activity, and it contains valuable information related to the different physiological states of the brain. This study proposes a novel automated method consisting of two steps for assessing anesthesia depth. Initially, the sample entropy and permutation entropy features were extracted from the EEG signal. Because EEG-derived parameters represent different aspects of the EEG features, it would be reasonable to use multiple parameters to assess the effect of the anesthetic. The sample entropy and permutation entropy features quantified the amount of complexity or irregularity in the EEG data and were conceptually simple, computationally efficient and artifact-resistant. Next, the extracted features were used as input for an artificial neural network, which was a data processing system based on the structure of a biological nervous system. The experimental results indicated that an overall accuracy of 88% could be obtained during sevoflurane anesthesia in 17 patients to classify the EEG data into awake, light, general and deep anesthetized states. In addition, this method yielded a classification accuracy of 92.4% to distinguish between awake and general anesthesia in an independent database of propofol and desflurane anesthesia in 129 patients. Considering the high accuracy of this method, a new EEG monitoring system could be developed to assist the anesthesiologist in estimating the depth of anesthesia in a rapid and accurate manner. en
dc.description.uri en
dc.language English en
dc.publisher Elsevier en
dc.relation.ispartofseries Journal of Neuroscience Methods en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from en
dc.rights.uri en
dc.subject Artificial neural network en
dc.subject Electroencephalogram (EEG) en
dc.subject Monitoring the depth of anesthesia en
dc.subject Permutation entropy en
dc.subject Sample Entropy en
dc.subject Adolescent en
dc.subject Adult en
dc.subject Anesthetics, Inhalation en
dc.subject Brain en
dc.subject Electroencephalography en
dc.subject Entropy en
dc.subject Female en
dc.subject Humans en
dc.subject Intraoperative Neurophysiological Monitoring en
dc.subject Male en
dc.subject Methyl Ethers en
dc.subject Middle Aged en
dc.subject Neural Networks (Computer) en
dc.subject Young Adult en
dc.title Monitoring the depth of anesthesia using entropy features and an artificial neural network en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.jneumeth.2013.03.008 en
pubs.issue 1 en
pubs.begin-page 17 en
pubs.volume 218 en
dc.description.version VoR - Version of Record en
dc.identifier.pmid 23567809 en en
pubs.end-page 24 en
pubs.publication-status Published en
dc.rights.accessrights en
pubs.subtype Article en
pubs.elements-id 389012 en Medical and Health Sciences en School of Medicine en Anaesthesiology en
dc.identifier.eissn 1872-678X en
dc.identifier.pii S0165-0270(13)00113-1 en
pubs.record-created-at-source-date 2017-02-27 en 2013-04-06 en
pubs.dimensions-id 23567809 en

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