Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG

Andreas Muff Munk, Kristoffer Vinther Olesen, Sirin Wilhelmsen Gangstad, L. K. Hansen

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

Abstract

The field of automatic sleep stage classification based on EEG has enjoyed substantial attention during the last decade, which has resulted in several supervised classification algorithms with highly encouraging performance. Such supervised machine learning algorithms require large training sets that have been manually labelled, and are time- and resource-consuming to acquire. Here we present a semi-supervised approach that can learn to distinguish the sleep stages from a one-night data set where only a fraction has been manually labelled. We show that for fractions larger than 50%, our semi-supervised approach performs as good as a similar, fully-supervised model.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Publication date2018
Pages2551-2555
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary Telus Convention Center, Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing
LocationCalgary Telus Convention Center
CountryCanada
CityCalgary
Period15/04/201820/04/2018

Keywords

  • Sleep
  • Electroencephalography
  • Training
  • Brain modeling
  • Spectrogram
  • Gaussian mixture model
  • EEG
  • Semi-supervised learning
  • Sleep stage scoring
  • Non-negative matrix factorizaton
  • Generalizable Gaussian mixture model

Cite this

Munk, A. M., Olesen, K. V., Gangstad, S. W., & Hansen, L. K. (2018). Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG. In Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 2551-2555). IEEE. https://doi.org/10.1109/ICASSP.2018.8461982