Abstract
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.
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Flexer, A., Gruber, G., Dorffner, G. (2002). Continuous Unsupervised Sleep Staging Based on a Single EEG Signal. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_164
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DOI: https://doi.org/10.1007/3-540-46084-5_164
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