Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach

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dc.contributor.author O'Toole, John M.
dc.contributor.author Boylan, Geraldine B.
dc.contributor.author Lloyd, Rhodri O.
dc.contributor.author Goulding, Robert M.
dc.contributor.author Vanhatalo, Sampsa
dc.contributor.author Stevenson, Nathan J.
dc.date.accessioned 2017-06-19T11:43:20Z
dc.date.available 2017-06-19T11:43:20Z
dc.date.issued 2017-04-18
dc.identifier.citation O’Toole, J. M., Boylan, G. B., Lloyd, R. O., Goulding, R. M., Vanhatalo, S. and Stevenson, N. J. (2017) 'Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach', Medical Engineering & Physics, 45, pp. 42-50. doi:10.1016/j.medengphy.2017.04.003 en
dc.identifier.volume 45 en
dc.identifier.startpage 42 en
dc.identifier.endpage 50 en
dc.identifier.issn 1350-4533
dc.identifier.uri http://hdl.handle.net/10468/4090
dc.identifier.doi 10.1016/j.medengphy.2017.04.003
dc.description.abstract Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen’s kappa (κ) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4–5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973–0.997) and sensitivity–specificity of 95.8–94.4%. Agreement rates between the detector and experts’ annotations, κ=0.72 (0.36–0.83) and κ=0.65 (0.32–0.81), are comparable to inter-rater agreement, κ=0.60 (0.21–0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods. en
dc.description.sponsorship Science Foundation Ireland (SFI 12/IP/1369 and 12/RC/2272); Wellcome Trust UK (085249); Irish Research Council (GOIPD/2014/396); Suomen Akatemia, Academy of Finland (253130); Sigrid Juséliuksen Säätiö, Sigrid Juselius Foundation, Finland en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.rights © 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Burst detection en
dc.subject Electroencephalography en
dc.subject Preterm infant en
dc.subject Feature extraction en
dc.subject Spectral analysis en
dc.subject Support vector machine en
dc.subject Inter-burst interval en
dc.title Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother John O'Toole, Obstetrics & Gynaecology, University College Cork, Cork, Ireland. +353-21-490-3000 Email: jotoole@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-06-19T11:35:14Z
dc.description.version Published Version en
dc.internal.rssid 399705037
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Wellcome Trust en
dc.contributor.funder Irish Research Council en
dc.contributor.funder Suomen Akatemia en
dc.contributor.funder Sigrid Juséliuksen Säätiö
dc.description.status Peer reviewed en
dc.identifier.journaltitle Medical Engineering & Physics en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress jotoole@ucc.ie en


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© 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) Except where otherwise noted, this item's license is described as © 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
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