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OFAI-TR-99-21 ( 86kB g-zipped PostScript file,  335kB PDF file)

Using Hidden Markov Models to build an automatic, continuous and probabilistic sleep stager

Arthur Flexer, Peter Sykacek, Iaed Rezek, Georg Dorffner

We report about an automatic continuous sleep stager which is based on probabilistic principles employing Hidden Markov Models (HMM). Our sleep stager offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 second instead of 30 second), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales). Results obtained for nine whole night sleep recordings are reported.

Keywords: Time series, Medicine, Unsupervised learning, Sleep analysis, Hidden Markov Models

Citation: Flexer A., Sykacek P., Rezek I., Dorffner G.: Using Hidden Markov Models to build an automatic, continuous and probabilistic sleep stager, in Amari S.-I. , et al.(eds.), Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Como, Italy, IEEE Computer Society, Vol. III, 6 27-631, 2000.