About: Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #73784 Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging
sameAs	Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging
http://www.loc.gov...erms/relators/AUT	Philipp Koch Huy Phan Maarten De Vos Afred Mertins Oliver Y. Chén
http://eprints.org/ontology/hasAccepted	Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (PDF)
http://eprints.org/ontology/hasDocument	Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (PDF) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (Other) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (Other) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (Other) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (Other) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (Other)
dc:hasVersion	Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging (PDF)
Title	Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging
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	2019-10-07
Creator	Huy Phan
status	peer reviewed published
Publisher	IEEE
abstract	Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. The fusion model is composed of two base sleep-stage classifiers, SeqSleepNet and DeepSleepNet, both of which are state-of-the-art end-to-end deep learning models complying to the sequence-to-sequence sleep staging scheme. In addition, in the light of ensemble methods, we reason and demonstrate that these two networks form a good ensemble of models due to their high diversity. Experiments show that the fusion approach is able to preserve the strength of the base networks in the fusion model, leading to consistent performance gains over the two base networks. The fusion model obtain the best modelling results we have observed so far on the Montreal Archive of Sleep Studies (MASS) dataset with 200 subjects, achieving an overall accuracy of 88.0%, a macro F1-score of 84.3%, and a Cohen’s kappa of 0.828.
Is Part Of	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/73784#authors
presented at	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019)
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 #73784 Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging

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