A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

Alexander Neergaard Olesen, Julie Anja Engelhard Christensen, Helge Bjarup Dissing Sørensen, Poul Jennum

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen’s kappa of 0.74 indicating substantial agreement between automatic and
manual scoring.
Original languageEnglish
Title of host publicationProceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2016
Pages3769-3772
Article numberThCT3.14
ISBN (Print)978-1-4577-0220-4
DOIs
Publication statusPublished - 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16) - Orlando, FL, United States
Duration: 16 Aug 201620 Aug 2016
Conference number: 38
http://embc.embs.org/2016/

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16)
Number38
CountryUnited States
CityOrlando, FL
Period16/08/201620/08/2016
Internet address

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