Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11555
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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.accessioned2015-11-06T12:53:11Z-
dc.date.available2015-01-01-
dc.date.available2015-11-06T12:53:11Z-
dc.date.issued2015-
dc.identifier.citationComputational and Mathematical Methods in Medicine, 2015: 232381, (2015)en_US
dc.identifier.issn1748-670X-
dc.identifier.issn1748-6718-
dc.identifier.urihttp://www.hindawi.com/journals/cmmm/2015/232381/-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11555-
dc.description.abstractIn order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.en_US
dc.description.sponsorshipThis research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342).en_US
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.subjectEEG signalsen_US
dc.subjectMultiscale entropyen_US
dc.subjectArtificial neural networksen_US
dc.titleEEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networksen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1155/2015/232381-
dc.relation.isPartOfComputational and Mathematical Methods in Medicine-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.volume2015-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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