Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11696
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dc.contributor.authorLiu, Q-
dc.contributor.authorChen, YF-
dc.contributor.authorFan, SZ-
dc.contributor.authorAbbod, MF-
dc.contributor.authorShieh, JS-
dc.date.accessioned2015-12-03T15:06:35Z-
dc.date.available2016-03-01-
dc.date.available2015-12-03T15:06:35Z-
dc.date.issued2016-
dc.identifier.citationBiomedical Signal Processing and Control, 25 pp. 24 - 34, (2016)en_US
dc.identifier.issn1746-8094-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1746809415001767-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11696-
dc.description.abstractAll rights reserved. Electroencephalography (EEG) signals have been commonly used for assessing the level of anesthesia during surgery. However, the collected EEG signals are usually corrupted with artifacts which can seriously reduce the accuracy of the depth of anesthesia (DOA) monitors. In this paper, the main purpose is to compare five different EEG based anesthesia indices, namely median frequency (MF), 95% spectral edge frequency (SEF), approximate entropy (ApEn), sample entropy (SampEn) and permutation entropy (PeEn), for their artifacts rejection ability in order to measure the DOA accurately. The current analysis is based on synthesized EEG corrupted with four different types of artificial artifacts and real data collected from patients undergoing general anesthesia during surgery. The experimental results demonstrate that all indices could discriminate awake from anesthesia state (p < 0.05), however PeEn is superior to other indices. Furthermore, a combined index is obtained by applying these five indices as inputs to train, validate and test a feed-forward back-propagation artificial neural network (ANN) model with bispectral index (BIS) as target. The combined index via ANN offers more advantages with higher correlation of 0.80 ± 0.01 for real time DOA monitoring in comparison with single indices.en_US
dc.description.sponsorshipCenter for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by Ministry of Science and Technology (Grant Number: MOST103-2911-I-008-001). National Natural Science Foundation of China (Grant Number: 51475342).en_US
dc.format.extent24 - 34-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.subjectElectroencephalographyen_US
dc.subjectArtifactsen_US
dc.subjectDepth of anesthesiaen_US
dc.subjectNoise considerationen_US
dc.titleA Comparison of Different Algorithms for EEG Signal Analysis for the Purpose of Monitoring Depth of Anesthesiaen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.bspc.2015.10.010-
dc.relation.isPartOfBiomedical Signal Processing and Control-
pubs.publication-statusAccepted-
pubs.publication-statusAccepted-
pubs.volume25-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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