Show simple item record

dc.contributor.authorYan, Rui
dc.contributor.authorLi, Fan
dc.contributor.authorWang, Xiaoyu
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.contributor.editorObaidat, Mohammad
dc.contributor.editorCallegari, Christian
dc.contributor.editorvan Sinderen, Marten
dc.contributor.editorNovais, Paulo
dc.contributor.editorSarigiannidis, Panagiotis
dc.contributor.editorBattiato, Sebastiano
dc.contributor.editorSerrano Sánchez de León, Ángel
dc.contributor.editorLorenz, Pascal
dc.contributor.editorDavoli, Franco
dc.date.accessioned2019-08-29T09:02:03Z
dc.date.available2019-08-29T09:02:03Z
dc.date.issued2019
dc.identifier.citationYan, R., Li, F., Wang, X., Ristaniemi, T., & Cong, F. (2019). An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing. In M. Obaidat, C. Callegari, M. van Sinderen, P. Novais, P. Sarigiannidis, S. Battiato, Á. Serrano Sánchez de León, P. Lorenz, & F. Davoli (Eds.), <i>ICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS</i> (pp. 301-309). SCITEPRESS Science And Technology Publications. <a href="https://doi.org/10.5220/0007925503010309" target="_blank">https://doi.org/10.5220/0007925503010309</a>
dc.identifier.otherCONVID_32289916
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65363
dc.description.abstractSleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with the capability of multi-signal processing. It allows the user to choose signal types and the number of target classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or prediction) and result correction will be performed. Finally, the application interface displays predicted sleep structure, related sleep parameters and the sleep quality index for reference. To improve the identification accuracy of minority stages, a layer-wise classification strategy is proposed according to the signal characteristics of sleep stages. The context of the current stage is taken into consideration in the correction phase by employing a Hidden Markov Model to study the transition rules of sleep stages in the training dataset. These transition rules will be used for logic classification results. The performance of proposed toolbox has been tested on 100 subjects with an average accuracy of 85.76%. The proposed automatic scoring toolbox would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.en
dc.format.extent386
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSCITEPRESS Science And Technology Publications
dc.relation.ispartofICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherpolysomnography
dc.subject.othermulti-modality analysis
dc.subject.otherMATLAB toolbox
dc.subject.otherautomatic sleep scoring
dc.titleAn Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201908293969
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-989-758-378-0
dc.description.reviewstatuspeerReviewed
dc.format.pagerange301-309
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 by SCITEPRESS
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Signal Processing and Multimedia Applications
dc.subject.ysouni (lepotila)
dc.subject.ysoMATLAB
dc.subject.ysosignaalianalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8299
jyx.subject.urihttp://www.yso.fi/onto/yso/p12929
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.5220/0007925503010309
jyx.fundinginformationThis work was supported by the scholarships from China Scholarship Council (Nos. 201606060227).


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY-NC-ND 4.0
Except where otherwise noted, this item's license is described as CC BY-NC-ND 4.0