A Novel Fractional-Order Model of Medium-Frequency Respiratory Impedance

Respiratory impedance in the medium-frequency range reflects the dynamic properties of airways and lung tissues, and plays an important role in the early diagnosis of pulmonary diseases. Fractional-order models can characterize complex dynamics with fewer parameters compared to integer-order models,...

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Published in2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) pp. 1 - 6
Main Authors Wang, Hongwei, Li, Bin, Mo, Hongqiang, Xu, Yuanda, Xu, Zhaoxiang, Xie, Yanqing
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.10.2024
Subjects
Atmospheric modeling
Correlation
correlation analysis
Fitting
Fluctuations
fractional-order model
Impedance
impulse oscillometry
Lungs
medium-frequency range
Pulmonary diseases
Resistance
Respiratory impedance
Signal processing
Solid modeling
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DOI10.1109/CISP-BMEI64163.2024.10906135

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Summary:Respiratory impedance in the medium-frequency range reflects the dynamic properties of airways and lung tissues, and plays an important role in the early diagnosis of pulmonary diseases. Fractional-order models can characterize complex dynamics with fewer parameters compared to integer-order models, and have been used to represent the medium-frequency characteristics of the respiratory impedance. There have been multiple fractional-order models, but there is no consensus on which model is most reasonable. This paper proposes a novel fractional-order model, which consists of a viscous resistance, an inertia resistance, and an elastic resistance. Unlike existing models, in the proposed model, the inertial resistance is represent with a fractional-order term, while the other two resistances are represent with integer-order terms. The proposed model was employed to represent the medium-frequency respiratory impedance determined by appling impulse oscillometry to 8 healthy subjects. The results in terms of fitting error and determination coefficient indicate that the proposed model fits the data well. Furthermore, when examining the variation of the parameters of the model over time, the results indicate that the parameters are highly correlated with tidal volume. However, the parameters of other existing fractional-order models did not exhibit this correlation.
DOI:10.1109/CISP-BMEI64163.2024.10906135