A comparison of linear respiratory system models based on parameter estimates from PRN forced oscillation data.

Area of Science:

• Pulmonary Physiology
• Biomedical Engineering
• Respiratory System Modeling

Background:

• The forced oscillation technique (FOT) offers advantages over spirometry for assessing pulmonary function due to passive patient cooperation and frequency-dependent impedance data.
• Frequency-dependent impedance data from FOT is amenable to engineering analysis, enabling parameter estimation for electric circuit-based respiratory system models.
• Such models can aid in the detection and diagnosis of various respiratory diseases and pathologies.

Purpose of the Study:

• To compare the least-squares error performance of several electric circuit models in fitting respiratory impedance data.
• To evaluate model fitting accuracy across healthy subjects and patients with varying asthma severity.
• To identify models that provide accurate parameter estimates without unphysiological component values.

Main Methods:

• Respiratory impedance data were collected using pseudorandom noise forced oscillation in healthy subjects, mild asthmatics, and severe asthmatics.
• The fitting performance of the RIC, extended RIC, augmented RIC, augmented RIC+I(p), DuBois, Nagels, and Mead models was evaluated using least-squares error analysis.
• Model component estimates were assessed for physiological plausibility.

Main Results:

• The augmented RIC+I(p) model demonstrated the lowest fitting errors for the healthy subjects group.
• The DuBois model yielded the lowest fitting errors for both mild and severe asthmatic patient groups.
• Both the aRIC+I(p) and DuBois models avoided producing unphysiologically large component estimates in their respective best-fit scenarios.

Conclusions:

• The augmented RIC+I(p) and DuBois models are effective for analyzing respiratory impedance data in healthy and asthmatic individuals, respectively.
• These models offer accurate fitting and physiologically plausible parameter estimates, supporting their use in clinical respiratory diagnostics.
• The study highlights the utility of engineering-based circuit models in understanding respiratory system dynamics and disease states.