Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG
Andreas Muff Munk, Kristoffer Vinther Olesen, Sirin Wilhelmsen Gangstad, Lars Kai Hansen
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.
BibTeX
@inproceedings{icassp2018_semisupervisedsl,
title = {Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG},
author = {Andreas Muff Munk and Kristoffer Vinther Olesen and Sirin Wilhelmsen Gangstad and Lars Kai Hansen},
booktitle = {ICASSP 2018},
year = {2018}
}