Original title: Dynamic Bayesian Networks for the Classification of Sleep Stages
Authors: Vomlel, Jiří ; Kratochvíl, Václav
Document type: Papers
Conference/Event: Workshop on Uncertainty Processing (WUPES’18), Třeboň (CZ), 20180606
Year: 2018
Language: eng
Abstract: Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy.
Keywords: Dynamic Bayesian Network; Sleep Analysis
Project no.: GA16-12010S (CEP), GA17-08182S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), ISBN 978-80-7378-361-7

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2018/MTR/vomlel-0490307.pdf
Original record: http://hdl.handle.net/11104/0284594

Permalink: http://www.nusl.cz/ntk/nusl-375670


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers