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

dc.contributor.authorO'Toole, John M.
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.authorLloyd, Rhodri O.
dc.contributor.authorGoulding, Robert M.
dc.contributor.authorVanhatalo, Sampsa
dc.contributor.authorStevenson, Nathan J.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderWellcome Trusten
dc.contributor.funderIrish Research Councilen
dc.contributor.funderSuomen Akatemiaen
dc.contributor.funderSigrid Juséliuksen Säätiö
dc.date.accessioned2017-06-19T11:43:20Z
dc.date.available2017-06-19T11:43:20Z
dc.date.issued2017-04-18
dc.date.updated2017-06-19T11:35:14Z
dc.description.abstractAim: 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.sponsorshipScience 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, Finlanden
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO’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.2017.04.003en
dc.identifier.doi10.1016/j.medengphy.2017.04.003
dc.identifier.endpage50en
dc.identifier.issn1350-4533
dc.identifier.journaltitleMedical Engineering & Physicsen
dc.identifier.startpage42en
dc.identifier.urihttps://hdl.handle.net/10468/4090
dc.identifier.volume45en
dc.language.isoenen
dc.publisherElsevieren
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.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBurst detectionen
dc.subjectElectroencephalographyen
dc.subjectPreterm infanten
dc.subjectFeature extractionen
dc.subjectSpectral analysisen
dc.subjectSupport vector machineen
dc.subjectInter-burst intervalen
dc.titleDetecting bursts in the EEG of very and extremely premature infants using a multi-feature approachen
dc.typeArticle (peer-reviewed)en
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