We report improvements on automatic continuous sleep staging using Hidden Markov Models (HMM). 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. Contrary to our previous efforts we trained the HMM on data from a single sleep lab instead of generalizing to data from diverse sleep labs. This solved our previous problem of detecting rem sleep.