A Dirichlet process mixture model for autonomous sleep apnea detection using oxygen saturation data

Li, Z., Arvaneh, M., Elphick, H.E. et al. (2 more authors) (2020) A Dirichlet process mixture model for autonomous sleep apnea detection using oxygen saturation data. In: Proceedings of 2020 IEEE 23rd International Conference on Information Fusion (FUSION). 2020 IEEE 23rd International Conference on Information Fusion (FUSION), 06-09 Jul 2020, Rustenburg, South Africa. IEEE , pp. 1-8. ISBN 9781728168302

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Copyright, Publisher and Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Sleep apnea-hypopnea syndrome; oxygen saturation (SpO2) data; Dirichlet process mixture model; classification; sleep disorder diagnostics; decision making
Dates:
  • Accepted: 1 May 2020
  • Published (online): 10 September 2020
  • Published: 10 September 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 09 Jun 2020 07:17
Last Modified: 28 Oct 2020 12:03
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.23919/FUSION45008.2020.9190411
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Embargoed until: 10 September 2021

Filename: SleepApneaDet Fusion 2020.pdf

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