Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager

We report about an automatic continuous sleep stager which is based on probabilistic principles employing hidden Markov models (HMMs). 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...

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Main Authors: Flexer, A., Sykacek, P., Rezek, I., Dorffner, G.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Electroencephalography
Electromyography
Electrooculography
Hidden Markov models
Humans
Intelligent robots
Probability distribution
Robotics and automation
Solids
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creator Flexer, A.
Sykacek, P.
Rezek, I.
Dorffner, G.
description We report about an automatic continuous sleep stager which is based on probabilistic principles employing hidden Markov models (HMMs). 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. Results obtained for nine whole night sleep recordings are reported.
doi_str_mv 10.1109/IJCNN.2000.861392
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ispartof Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000, Vol.3, p.627-631 vol.3
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1558-3902
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial intelligence
Electroencephalography
Electromyography
Electrooculography
Hidden Markov models
Humans
Intelligent robots
Probability distribution
Robotics and automation
Solids
title Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager
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