System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An introduction to variational methods for graphical models
Learning in graphical models
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Continuous Unsupervised Sleep Staging Based on a Single EEG Signal
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A reliable probabilistic sleep stager based on a single EEG signal
Artificial Intelligence in Medicine
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We propose in this paper an entirely probabilistic approach to sleep analysis. The analyser uses features extracted from 6 EEG channels as inputs and predicts the probabilities that the sleeping subject is either awake, in deep sleep or in rapid eye movement (REM) sleep. These probability estimates are provided for different temporal resolutions down to 1second. The architecture uses a "divide and conquer" strategy, where the decisions of simple experts are fused by what is usually refered to as "naïve Bayes" classification. In order to show that the proposed method provides viable means for sleep analysis, we present some results obtained from recordings of good and bad sleep and the corresponding manual scorings.