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

doi:10.1109/EMBC.2016.7591548

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

Clinical Neurophysiology, Department of

15 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 language	English
Journal	I E E E Engineering in Medicine and Biology Society. Conference Proceedings
Volume	2016
Pages (from-to)	3769-3772
Number of pages	4
ISSN	1557-170X
DOIs	https://doi.org/10.1109/EMBC.2016.7591548
Publication status	Published - 2016

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A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning. / Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D et al.

In: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Vol. 2016, 2016, p. 3769-3772.

Research output: Contribution to journal › Conference article › Research › peer-review

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

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