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Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-4255-5130
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

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Institute of Electrical and Electronics Engineers (IEEE), 2018. s. 112-115
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-147558DOI: 10.1109/BHI.2018.8333382ISBN: 978-1-5386-2405-0 (digital)ISBN: 978-1-5386-2406-7 (tryckt)OAI: oai:DiVA.org:liu-147558DiVA, id: diva2:1201555
Konferanse
2018 IEEE EMBS International Conference on Biomedical & Health Informatics
Tilgjengelig fra: 2018-04-26 Laget: 2018-04-26 Sist oppdatert: 2018-05-18

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  • de-DE
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  • sv-SE
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