Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features

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...

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Bibliographic Details
Published in2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) pp. 112 - 115
Main Author Pham, Tuan D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Blood
Blood oxygen saturation
Entropy
Feature extraction
fuzzy recurrence plots
multiscale entropy
Pattern analysis
Sociology
support vector machines
Time series analysis
time-shift multiscale entropy
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DOI10.1109/BHI.2018.8333382

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Summary: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.
DOI:10.1109/BHI.2018.8333382