Bill Diong

Background: Is Impulse Oscillometry System (IOS) a valuable tool to measure respiratory system function in Children?Asthma (A) is the most prevalent chronic respiratory disease in children. Therefore, early and accurate assessment of respiratory function is of tremendous clinical interest in diagnosis, monitoring and treatment of respiratory conditions in this subpopulation.IOS has been successfully used to measure lung function in children with a high degree of sensitivity and specificity to small airway impairments (SAI) and asthma. IOS measures of airway function and equivalent electrical circuit models of the human respiratory system have been developed to quantify the severity of these conditions. Previously, we have evaluated several known respiratory models based on the Mead's model and moreparsimonious versionsbased on fitting IOS data known as extended RIC (eRIC) and augmented RIC (aRIC) models have emerged, which offer advantages over earlier models.Methods: IOS data from twenty-six children were collected and compared during pre-bronchodilation (pre-B) and post- bronchodilation (post-B) conditions over a period of 2 years.Results and Discussion: Are the IOS and model parameters capable of differentiating between healthy children and children with respiratory system distress?Children were classified into two main categories: Healthy (H) and Small Airway-Impaired (SAI). The IOS measures and respiratory model parameters analyzed differed consistently between H and SAI children. SAI children showed smaller trend of "growth" and larger trend of bronchodilator responses than H children.The two model parameters: peripheral compliance (Cp) and peripheral resistance (Rp) tracked IOS indices of small airway function well. Cp was a more sensitive index than Rp. Both eRIC and aRIC Cps and the IOS Reactance Area, AX, (also known as the "Goldman Triangle") showed good correlations.Conclusions: What are the most useful IOS and model parameters?In this work we demonstrate that IOS parameters such as resistance at 5 Hz (R5), frequency-dependence of resistance (fdR: R5-R20), reactance area (AX), and parameter estimates of respiratory system such as Cp and Rp provide sensitive indicators of lung function and have the capacity to differentiate between obstructed and non-obstructed airway conditions. They are also capable of demonstrating airway growth-related changes over a two-year period.We conclude that theIOS parameters AXand theeRIC model derived parameter Cpare the most reliable parameters to track lung function in children before and after bronchodilator and over a time period (2 years).Which model is more suitable for interpreting IOS data?IOS data are equally well-modelled by eRIC and aRIC models, based on the close correlations of their corresponding parameters - excluding upper airway shunt compliance. The eRIC model is a moreparsimoniousand equally powerful model in capturing the differences in IOS indices between SAI and H children. Therefore, it may be considered a clinically-preferred model of lung function.

This paper describes the augmented RIC model of respiratory impedance and analyzes its parameter values estimated—by a modified Newton method with least squares criterion—from impulse oscillometry data. The data were from asthmatic children, tested pre- and post-bronchodilator, and from healthy adults and a second group of adults with COPD. Our analyses show that the augmented RIC model was 13.7–66.6% more accurate than the extended RIC model at fitting these data, while its parameter estimates were within previously reported ranges, unlike the Mead1969, DuBois and Mead models, which typically yielded compliance estimates exceeding 200 l/kPa. Additionally, the augmented RIC model’s C _p parameter, representing peripheral airway compliance, is a statistically significant discriminator between unconstricted and constricted conditions (with p < 0.001) occurring in asthma and COPD. This corresponds well with current medical understanding, so the augmented RIC model is potentially useful for detection and treatment of airflow obstruction.

Arc faults can arise in any electrical system as a consequence of loose connections, inadvertent damage during maintenance and/or insulation aging. In this paper, a new approach to the detection of arc faults is discussed. By using the proposed parametric models of the fault signals, an arc fault can be identified quickly and reliably, so that action can be taken to prevent the arc from escalating into a serious fire and causing loss of life and/or property. One model was obtained for DC power systems and another model was obtained for AC power systems. These proposed models were able to distinguish arc faults from normal operation of the various electrical loads that were tested