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Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Pattern analysis of blood oxygen saturation is important for gaining insights into the cardiorespiratory control system, real-time monitoring during operations, identifying potential predictors for the diagnosis of disease severity, and improving the hospitalization of patients with critical chronic diseases. This paper investigates the use of nonlinear dynamics features for machine learning and classification of blood oxygen saturation signals in healthy young and healthy old subjects. The validation of the feature reliability for the signal variability analysis has a clinical implication for differentiating blood oxygen saturation in patients with respect to the particular influence of aging, when patient's data become available.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 112-115
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-147558DOI: 10.1109/BHI.2018.8333382ISBN: 978-1-5386-2405-0 (electronic)ISBN: 978-1-5386-2406-7 (print)OAI: oai:DiVA.org:liu-147558DiVA, id: diva2:1201555
Conference
2018 IEEE EMBS International Conference on Biomedical & Health Informatics
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-05-18

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Pham, Tuan

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