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 language | English |
|---|---|
| Title of host publication | Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing |
| Publisher | IEEE |
| Publication date | 2018 |
| Pages | 2551-2555 |
| ISBN (Print) | 9781538646588 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary Telus Convention Center, Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 Conference number: 43 https://www.2018.ieeeicassp.org/2018.ieeeicassp.org/Default.html |
Conference
| Conference | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| Number | 43 |
| Location | Calgary Telus Convention Center |
| Country/Territory | Canada |
| City | Calgary |
| Period | 15/04/2018 → 20/04/2018 |
| Internet address |
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