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

Alexander Neergaard Olesen, Julie A E Christensen, Helge B D Sorensen, Poul J Jennum

17 Citations (Scopus)

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
JournalI E E E Engineering in Medicine and Biology Society. Conference Proceedings
Volume2016
Pages (from-to)3769-3772
Number of pages4
ISSN1557-170X
DOIs
Publication statusPublished - 2016

Keywords

  • Journal Article

Fingerprint

Dive into the research topics of 'A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning'. Together they form a unique fingerprint.

Cite this