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ÖFAI-TR-99-15 ( 80kB g-zipped PostScript file,  300kB PDF file)

Using Hidden Markov Models to build an automatic, continuous and probabilistic sleep stager for the SIESTA project

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 (1sec instead of 30sec), 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: Pattern Recognition, Hidden Markov Model, Signal Processing, EEG, Sleep

Citation: Flexer A., Sykacek P., Rezek I., Dorffner G.: Using Hidden Markov Models to build an automatic, continuous and probabilistic sleep stager for the SIESTA project (extended abstract), Medical & Biological Engineering & Computing, Supplement 2, Proceedings of EMBEC'99, p.1658-1659, 1999