Conference paper
Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2018 |
Pages: | 2551-2555 |
Proceedings: | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing |
ISBN: | 1538646579 , 1538646587 , 1538646595 , 9781538646571 , 9781538646588 and 9781538646595 |
ISSN: | 2379190x and 15206149 |
Types: | Conference paper |
DOI: | 10.1109/ICASSP.2018.8461982 |
ORCIDs: | Hansen, L. K. |
Brain modeling EEG Electroencephalography Gaussian mixture model Generalizable Gaussian mixture model Non-negative matrix factorizaton Semi-supervised learning Sleep Sleep stage scoring Spectrogram Training
automatic sleep stage classification electroencephalography fully-supervised model generalizable Gaussian mixture model learning (artificial intelligence) medical signal processing non-negative matrix factorizaton pattern classification semi-supervised learning semisupervised approach performs semisupervised sleep-stage scoring signal classification sleep sleep stage scoring sleep stages supervised classification algorithms supervised machine learning algorithms