Applying Time-Frequency Image of Convolutional Neural Network to Extract Feature on Long-Term EEG Signals to Predict Depth of Anesthesia

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17826

Full metadata record

DC Field	Value	Language
dc.contributor.author	Huang, Y	-
dc.contributor.author	Chen, J	-
dc.contributor.author	Abbod, M	-
dc.contributor.author	Fan, S-Z	-
dc.contributor.author	Shieh, J-S	-
dc.contributor.author	Kung, Y-C	-
dc.coverage.spatial	London, UK	-
dc.date.accessioned	2019-04-01T13:01:07Z	-
dc.date.available	2019-04-01T13:01:07Z	-
dc.date.issued	2019-07-03	-
dc.identifier.uri	https://bura.brunel.ac.uk/handle/2438/17826	-
dc.description.sponsorship	The Lenovo Technology B.V. Taiwan Branch; Ministry of Science and Technology (Grant number: MOST 107-2221-E-155 -009 -MY2).	en_US
dc.language.iso	en	en_US
dc.publisher	World Congress on Engineering	-
dc.source	World Congress on Engineering 2019	-
dc.source	World Congress on Engineering 2019	-
dc.title	Applying Time-Frequency Image of Convolutional Neural Network to Extract Feature on Long-Term EEG Signals to Predict Depth of Anesthesia	en_US
dc.type	Conference Paper	en_US
pubs.finish-date	2019-07-05	-
pubs.finish-date	2019-07-05	-
pubs.publication-status	Published	-
pubs.start-date	2019-07-03	-
pubs.start-date	2019-07-03	-
Appears in Collections:	Brunel Centre for Advanced Solidification Technology (BCAST)

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