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
Abstract
Sleep apnea is a sleep disorder which is common in many children and adults. It is characterised by abnormal breath pauses or shallow breathing during sleep. Traditional diagnosis of apnea requires special equipment for data collection in clinical conditions and manual analysis by clinicians which is expensive and time-consuming. This paper presents a framework for autonomous detection of sleep apnea, using peripheral blood haemoglobin oxygen saturation (SpO 2 ) data based on the fusion of multiple features and Dirichlet process mixture model. The SpO 2 signals are segmented into overlapping sub-sequences and several features are extracted from each segment. The distributions of features extracted from disorder and normal segments are modelled by two Gaussian mixture models, respectively, with the Dirichlet process as the prior. The advantage of the framework is that the number of clusters within mixture models can be learned from training data without strong assumptions, which contributes to accurate estimation of the distributions. The proposed framework is subject-independent and it is trained and tested on two publicly available databases with 10-fold cross-validation. It obtains accuracy of 84.89% on the St. Vincent's University Hospital Sleep Apnea Database and accuracy of 97.01% on the Apnea-ECG Database, outperforming state-of-the-art approaches. The results show that the proposed model is capable of representing the distributions of features independently of subjects and can accurately classify segmented signals from patients with symptoms of different severity. The results show the potential of the developed classification framework to support clinicians in their decision making.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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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: | 09 Jun 2020 07:17 |
Last Modified: | 10 Sep 2021 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/FUSION45008.2020.9190411 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161392 |