Dynamic Bayesian Networks for the Classification of Sleep Stages

0490307 - ÚTIA 2019 RIV CZ eng C - Conference Paper (international conference)
Vomlel, Jiří - Kratochvíl, Václav
Dynamic Bayesian Networks for the Classification of Sleep Stages.
Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18). Praha: MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, 2018 - (Kratochvíl, V.; Vejnarová, J.), s. 205-215. ISBN 978-80-7378-361-7.
[Workshop on Uncertainty Processing (WUPES’18). Třeboň (CZ), 06.06.2018-09.06.2018]
R&D Projects: GA ČR(CZ) GA16-12010S; GA ČR GA17-08182S
Institutional support: RVO:67985556
Keywords : Dynamic Bayesian Network * Sleep Analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2018/MTR/vomlel-0490307.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0284594