Signal processing to remove spurious contributions to the assessment of respiratory mechanics

Signal disruptions in small animals during the realization of the Forced Oscillation Technique are a well-known cause of data loss as it leads to non-reliable estimations of the respiratory impedance. In this work, we assessed the effects of removing the disrupted epoch when a 3-seconds input signal composed of one and a half 2-seconds full cycle is used.

We tested our hypothesis in 25 SAMR1 mice under different levels of bronchoconstriction due to methacholine administration by iv bolus injections in different doses (15 animals) and by iv continuous infusion in different infusion rates (10 animals). Signal disruptions were computationally simulated as sharp drops in the pressure signal within a short timescale, and signal processing was performed using own developed algorithms.

We found that the model goodness of fit worsens when averaging techniques to estimate the input respiratory impedance are not used. However, no statistically significant differences were observed in the comparison between Constant Phase Model parameters of the full 3-s signal and the 2-s non disrupted epoch in all doses or infusion rates for both methacholine delivery strategies.

The proposed technique presents reliable outcomes that can reduce animal use in Forced Oscillation Technique realization.