About: Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #72663 Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks
sameAs	Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks
http://eprints.org/ontology/hasAccepted	Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (PDF)
http://eprints.org/ontology/hasDocument	Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (PDF) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (Other) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (Other) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (Other) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (Other) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (Other)
dc:hasVersion	Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks (PDF)
Title	Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-07-21
Creator	Navin Cooray Christine Lo Fernando Andreotti Michele T.M. Hu Maarten De Vos Huy Phan
status	peer reviewed published
Publisher	IEEE
abstract	Current sleep medicine relies on the analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. This analysis currently requires supervision of a trained expert. Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep epochs based on raw EEG, EOG and EMG waveforms. In this study, we apply CNN approaches from the literature to four databases from pathological and physiological subjects. The best performing model resulted in Cohen’s Kappa of k = 0.75 on healthy subjects and k = 0.64 on patients suffering from a variety of sleep disorder. Further, we show the advantages of using additional sensor data such as EOG and EMG. Last, to cope with smaller datasets of less prevalent diseases, we propose a transfer learning procedure using large freely available databases for pre-training. This procedure is demonstrated using a private REM Behaviour Disorder database, improving sleep classification by 24.4%.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/72663#authors
presented at	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2018)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #72663 Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

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