dc.contributor.author |
O'Toole, John M. |
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dc.contributor.author |
Boylan, Geraldine B. |
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dc.contributor.author |
Lloyd, Rhodri O. |
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dc.contributor.author |
Goulding, Robert M. |
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dc.contributor.author |
Vanhatalo, Sampsa |
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dc.contributor.author |
Stevenson, Nathan J. |
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dc.date.accessioned |
2018-01-29T11:31:01Z |
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dc.date.available |
2018-01-29T11:31:01Z |
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dc.date.issued |
2017-07 |
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dc.identifier.citation |
O'Toole , J M , Boylan , G B , Lloyd , R O , Goulding , R M , Vanhatalo , S & Stevenson , N J 2017 , ' Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach ' , Medical Engineering & Physics , vol. 45 , pp. 42-50 . https://doi.org/10.1016/j.medengphy.2017.04.003 |
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dc.identifier.other |
PURE: 97814744 |
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dc.identifier.other |
PURE UUID: 825266db-6130-455b-9696-2193bb3869b0 |
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dc.identifier.other |
WOS: 000403625100005 |
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dc.identifier.other |
Scopus: 85017551785 |
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dc.identifier.uri |
http://hdl.handle.net/10138/231702 |
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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 (K) 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, K = 0.72 (0.36-0.83) and K = 0.65 (0.32-0.81), are comparable to inter-rater agreement, K = 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. (C) 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM. |
en |
dc.format.extent |
9 |
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dc.language.iso |
eng |
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dc.relation.ispartof |
Medical Engineering & Physics |
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dc.rights |
cc_by |
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dc.rights.uri |
info:eu-repo/semantics/openAccess |
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dc.subject |
Burst detection |
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dc.subject |
Electroencephalography |
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dc.subject |
Preterm infant |
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dc.subject |
Feature extraction |
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dc.subject |
Spectral analysis |
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dc.subject |
Support vector machine |
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dc.subject |
Inter-burst interval |
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dc.subject |
PRETERM INFANTS |
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dc.subject |
ACTIVITY TRANSIENTS |
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dc.subject |
RECORDINGS |
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dc.subject |
BORN |
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dc.subject |
ELECTROENCEPHALOGRAM |
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dc.subject |
SUPPRESSION |
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dc.subject |
SELECTION |
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dc.subject |
BRAIN |
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dc.subject |
3111 Biomedicine |
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dc.subject |
3124 Neurology and psychiatry |
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dc.title |
Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach |
en |
dc.type |
Article |
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dc.contributor.organization |
Clinicum |
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dc.contributor.organization |
Department of Neurosciences |
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dc.contributor.organization |
Kliinisen neurofysiologian yksikkö |
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dc.contributor.organization |
HUS Medical Imaging Center |
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dc.description.reviewstatus |
Peer reviewed |
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dc.relation.doi |
https://doi.org/10.1016/j.medengphy.2017.04.003 |
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dc.relation.issn |
1350-4533 |
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dc.rights.accesslevel |
openAccess |
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dc.type.version |
publishedVersion |
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