Improvements on continuous unsupervised sleep staging

Abstract

We report improvements on automatic continuous sleep staging using hidden Markov models (HMM). Contrary to our previous efforts, we trained the HMMs on data from single sleep labs instead of generalizing to data from diverse sleep labs. Our totally unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and rem sleep) with around 80% accuracy based on data from a single EEG channel recorded at the sleep lab for which we already achieved the best results so far. Experiments with data from the worst sleep lab so far cannot be improved by training a separate model. This means that our previous problem of detecting rem sleep is not a general problem of our method but rather due to insufficient information in the data for some of the sleep labs.