End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.
Andreas Brink-KjaerKatarina Mary GunterEmmanuel MignotEmmanuel DuringPoul JennumHelge B. D. SørensenPublished in: EMBC (2022)
Keyphrases
- end to end
- deep learning
- machine learning
- unsupervised learning
- sleep stage
- restricted boltzmann machine
- pattern recognition
- text localization and recognition
- admission control
- congestion control
- text classification
- feature vectors
- supervised learning
- feature extraction
- decision trees
- feature space
- image classification
- image features
- weakly supervised
- reinforcement learning
- unsupervised feature learning
- e learning