Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach
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.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.date.accessioned | 2017-06-19T11:43:20Z | |
dc.date.available | 2017-06-19T11:43:20Z | |
dc.date.issued | 2017-04-18 | |
dc.date.updated | 2017-06-19T11:35:14Z | |
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.description.status | Peer reviewed | en |
dc.description.version | Published Version | en |
dc.format.mimetype | application/pdf | en |
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.doi | 10.1016/j.medengphy.2017.04.003 | |
dc.identifier.endpage | 50 | en |
dc.identifier.issn | 1350-4533 | |
dc.identifier.journaltitle | Medical Engineering & Physics | en |
dc.identifier.startpage | 42 | en |
dc.identifier.uri | https://hdl.handle.net/10468/4090 | |
dc.identifier.volume | 45 | 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 |