dc.contributor.author |
Abbasi, Seyed |
en |
dc.contributor.author |
Gunn, Alistair |
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dc.contributor.author |
Bennet, Laura |
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dc.contributor.author |
Unsworth, CP |
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dc.coverage.spatial |
Montreal, Canada |
en |
dc.date.accessioned |
2020-06-11T03:00:50Z |
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dc.date.available |
2020-04-11 |
en |
dc.date.issued |
2020-07-20 |
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dc.identifier.citation |
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2020-July: 1015-1018. 01 Jul 2020 |
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dc.identifier.uri |
http://hdl.handle.net/2292/51476 |
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dc.description.abstract |
Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%. Clinical relevance—The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult. |
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dc.description.uri |
https://embc.embs.org/2020/ |
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dc.publisher |
IEEE |
en |
dc.relation.ispartof |
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20) |
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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. |
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dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.subject |
Deep Neural Network |
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dc.subject |
Deep learning |
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dc.subject |
Convolutional Neural Network |
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dc.subject |
hypoxic-ischemic encephalopathy (HIE) |
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dc.subject |
EEG Biomarker |
en |
dc.subject |
Neonatal Seizure |
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dc.subject |
Automatic detection |
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dc.subject |
Brain injury |
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dc.subject |
Early diagnosis |
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dc.subject |
spike transients |
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dc.subject |
sharp wave |
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dc.subject |
pattern recognition |
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dc.subject |
wavelet |
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dc.subject |
spectral analysis |
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dc.subject |
machine learning |
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dc.subject |
preterm brain |
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dc.subject |
clinical recording |
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dc.subject |
biorthogonal basis |
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dc.subject |
Scalograms |
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dc.subject |
newborns |
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dc.subject |
signal processing |
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dc.subject |
image processing |
en |
dc.subject |
neuroscience |
en |
dc.subject |
sheep |
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dc.title |
Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG |
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dc.type |
Conference Item |
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dc.identifier.doi |
10.1109/EMBC44109.2020.9176499 |
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dc.rights.holder |
Copyright: IEEE |
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pubs.finish-date |
2020-07-24 |
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pubs.publication-status |
Accepted |
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pubs.start-date |
2020-07-20 |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
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pubs.subtype |
Conference Paper |
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pubs.elements-id |
800480 |
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pubs.org-id |
Bioengineering Institute |
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pubs.org-id |
Medical and Health Sciences |
en |
pubs.org-id |
Medical Sciences |
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pubs.org-id |
Physiology Division |
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pubs.record-created-at-source-date |
2020-04-29 |
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