SLUMBR: SLeep statUs estiMation from aBdominal Respiratory effort

Romero, H., Ma, N., Brown, G. orcid.org/0000-0001-8565-5476 et al. (1 more author) (2024) SLUMBR: SLeep statUs estiMation from aBdominal Respiratory effort. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 15-19 Jul 2024, Orlando, Florida. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9798350371505

Abstract

Accurately monitoring sleep for extended periods remains a challenge due to the cumbersome nature of conventional gold-standard techniques. We propose a novel deep learning method to estimate sleep status from an easily acquired abdominal respiratory effort signal. Our end-to-end convolutional neural network, developed on 476 hours of manually annotated polysomnography recordings from 53 participants, achieves an area under the curve of 0.90, and a more balanced performance across sensitivity and specificity than previous studies: 0.85 and 0.82, respectively. This method eliminates the need for obtrusive equipment and manual processing, paving the way for more accessible sleep monitoring solutions.

Metadata

Item Type:	Proceedings Paper
Authors/Creators:	Romero, H. Ma, N. Brown, G. https://orcid.org/0000-0001-8565-5476 Johnson, S.
Copyright, Publisher and Additional Information:	© 2024 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Keywords:	Deep learning; Sleep; Estimation; Manuals; Sensitivity and specificity; Particle measurements; Hardware; Recording; Monitoring; Standards
Dates:	Published: 17 December 2024 Published (online): 17 December 2024 Accepted: 1 May 2024
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:	Funder Grant number INNOVATE UK 10008165
Depositing User:	Symplectic Sheffield
Date Deposited:	03 May 2024 08:18
Last Modified:	07 Jan 2025 16:44
Status:	Published
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
Identification Number:	10.1109/EMBC53108.2024.10782490
Related URLs:	Conference
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:212162

Download

Accepted Version

Filename: slumbr.pdf

Licence: CC-BY 4.0

CLICK TO DOWNLOAD

CORE (COnnecting REpositories)

SLUMBR: SLeep statUs estiMation from aBdominal Respiratory effort

Abstract

Metadata

Download

Accepted Version

Export

Statistics