dc.contributor.author |
Abbasi, Seyed |
en |
dc.contributor.author |
Gunn, Alistair |
en |
dc.contributor.author |
Unsworth, CP |
en |
dc.contributor.author |
Bennet, Laura |
en |
dc.coverage.spatial |
Montreal, Canada |
en |
dc.date.accessioned |
2020-06-11T23:08:34Z |
en |
dc.date.available |
2020-04-11 |
en |
dc.date.issued |
2020-07-20 |
en |
dc.identifier.citation |
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20), Montreal, Canada, 20 Jul 2020 - 24 Jul 2020. 20 Jul 2020 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/51502 |
en |
dc.description.abstract |
Neonatal seizures after birth may contribute to brain injury after an hypoxic-ischemic (HI) event, impaired brain development and a later life risk for epilepsy. Despite neural immaturity, seizures can also occur in preterm infants. However, surprisingly little is known about their evolution after an HI insult or patterns of expression. An improved understanding of preterm seizures will help facilitate diagnosis and prognosis and the implementation of treatments. This requires improved detection of seizures, including electrographic seizures. We have established a stable preterm fetal sheep model of HI that results in different types of post-HI seizures. These including the expression of epileptiform transients during the latent phase (0-6 h) of cerebral energy recovery, and bursts of high amplitude stereotypic evolving seizures (HAS) during the secondary phase of cerebral energy failure (~6-72 h). We have previously developed successful automated machine-learning strategies for accurate identification and quantification of the evolving micro-scale EEG patterns (e.g. gamma spikes and sharp waves), during the latent phase. The current paper introduces, for the first time, a real-time approach that employs a 15-layer deep convolutional neural network (CNN) classifier, directly fed with the raw EEG timeseries, to identify HAS in the 1024Hz and 256Hz down-sampled data in our preterm fetuses post-HI. The classifier was trained and tested using EEG segments during ~6 to 48 hours post-HI recordings. The classifier accurately identified HAS with 98.52% accuracy in the 1024Hz and 97.78% in the 256Hz data. Clinical relevance—Results highlight the promising ability of the proposed CNN classifier for accurate identification of HI related seizures in the neonatal preterm brain, if further applied to the current 256Hz clinical recordings, in real-world. |
en |
dc.description.uri |
https://embc.embs.org/2020/ |
en |
dc.relation.ispartof |
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20) |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Convolutional Neural Networks |
en |
dc.subject |
Neonatal seizures |
en |
dc.subject |
EEG Biomarker |
en |
dc.subject |
hypoxic-ischemic encephalopathy (HIE) |
en |
dc.subject |
Early diagnosis |
en |
dc.subject |
CNN classifier |
en |
dc.subject |
wavelet |
en |
dc.subject |
brain injury |
en |
dc.subject |
evolving seizures |
en |
dc.subject |
machine learning |
en |
dc.subject |
sharp waves |
en |
dc.subject |
preterm brain |
en |
dc.subject |
clinical recordings |
en |
dc.subject |
Deep learning |
en |
dc.subject |
Deep Neural Network |
en |
dc.subject |
pattern recognition |
en |
dc.subject |
signal processing |
en |
dc.subject |
neuroscience |
en |
dc.subject |
sheep |
en |
dc.title |
Deep Convolutional Neural Networks for the Accurate Identification of High-Amplitude Stereotypic Epileptiform Seizures in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep |
en |
dc.type |
Conference Item |
en |
dc.rights.holder |
Copyright: The authors |
en |
pubs.finish-date |
2020-07-24 |
en |
pubs.publication-status |
Accepted |
en |
pubs.start-date |
2020-07-20 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Conference Paper |
en |
pubs.elements-id |
800477 |
en |
pubs.org-id |
Bioengineering Institute |
en |
pubs.org-id |
Medical and Health Sciences |
en |
pubs.org-id |
Medical Sciences |
en |
pubs.org-id |
Physiology Division |
en |
pubs.record-created-at-source-date |
2020-04-29 |
en |