About: Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint Article Book Section
seeAlso	HTML Summary of #69530 Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness
sameAs	Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness
http://eprints.org/ontology/hasAccepted	Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (PDF)
http://eprints.org/ontology/hasDocument	Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (PDF) Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (Other) Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (Other) Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (Other) Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (Other) Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (Other)
dc:hasVersion	Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness (PDF)
Title	Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-12-07
Creator	Jean-Francois Brichant Steven Laureys Vincent Bonhomme Melanie Boly Quentin Noirhomme Olivia Gosseries Konstantinos Patlatzoglou Srivas Chennu
status	peer reviewed published
Publisher	Springer
abstract	Despite the common use of anesthetics to modulate consciousness in the clinic, brain-based monitoring of consciousness is uncommon. We com-bined electroencephalographic measurement of brain activity with deep neural networks to automatically discriminate anesthetic states induced by propofol. Our results with leave-one-participant-out-cross-validation show that convolutional neural networks significantly outperform multilayer perceptrons in discrimination accuracy when working with raw time series. Perceptrons achieved comparable accuracy when provided with power spec-tral densities. These findings highlight the potential of deep convolutional networks for completely automatic extraction of useful spatio-temporo-spectral features from human EEG.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/69530#authors
presented at	11th International Conference on Brain Informatics
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #69530 Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

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