title: A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.
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.
authors.forename: Alexander Neergaard
authors.initials: AN
authors.lastname: Olesen
authors.forename: Julie A E
authors.initials: JA
authors.lastname: Christensen
authors.forename: Helge B D
authors.initials: HB
authors.lastname: Sorensen
authors.forename: Poul J
authors.initials: PJ
authors.lastname: Jennum
mesh_terms: D000328:Adult; D000465:Algorithms; D004585:Electrooculography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D017286:Polysomnography / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*