Yang, D., Bhargava, E., Elphick, H. et al. (1 more author) (2023) An adaptive CUSUM approach for automating sleep apnoea analysis based on pulse and oximetry data. In: 2023 IEEE International Conference on Mechatronics and Automation (ICMA) Proceedings. 2023 IEEE International Conference on Mechatronics and Automation (ICMA), 06-09 Aug 2023, Harbin, Heilongjiang, China. Institute of Electrical and Electronics Engineers (IEEE) , pp. 557-562. ISBN 9798350320855
Abstract
Sleep apnoea is a common sleep disorder during human sleep. It is usually diagnosed by a doctor after recording one nights’ sleep signals. Patients have to go to the hospital to record sleep signals, which is time-consuming and resource-intensive. The study focused on two signals, pulse data and oximetry data, with the aim of detecting apnoea using a single signal. This paper introduced an anomaly detection approach using the adaptive cumulative sum (ACUSUM) change point detection algorithm to monitor outliers in the signal. In addition, the test results of ACUSUM will be compared with the test results of classical CUSUM. Besides, the threshold selection has been changed from an unchanging constant to a value related to the standard deviation of the selected signal based on a rational subgroup process. The results of the comparison confirm that ACUSUM is better than classical CUSUM in the accuracy of automatic detection.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 IEEE International Conference on Mechatronics and Automation (ICMA) Proceedings 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: | Adaptive CUSUM; Sleep Apnoea |
Dates: |
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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: | 14 Jun 2023 14:16 |
Last Modified: | 15 Sep 2023 14:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ICMA57826.2023.10215850 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200328 |