Breathing-phase selective filtering of respiratory data improves analysis of dynamic respiratory mechanics

Technol Health Care. 2014;22(5):717-28. doi: 10.3233/THC-140843.

Abstract

Background: The analysis of non-linear respiratory system mechanics under the dynamic conditions of controlled mechanical ventilation is affected by systemic disturbances of the respiratory signals. Cardio-pulmonary coupling induces cardiogenic oscillations to the respiratory signals, which appear prominently in the second half of expiration.

Objective: We hypothesized that breathing phase-selective filtering of expiratory data improves the analysis of respiratory system mechanics.

Methods: We retrospectively analyzed data from a multicenter-study (28 patients with injured lungs, under volume-controlled ventilation) and from two additional studies (3 lung healthy patients and 3 with injured lungs, under pressure-controlled ventilation). Data streams were recorded at different levels of positive end-expiratory pressure. Using the gliding-SLICE method, intratidal dynamic respiratory mechanics were analyzed with and without low-pass filtering of expiratory or inspiratory data separately. The quality of data analysis was derived from the coefficient of determination R^2.

Results: Without filtering, R^2 lay below 0.995 for 87 of 280 investigated data streams. In 68 cases expiration-selective low-pass filtering improved the quality of analysis to R^2 ⩾ 0.995. In contrast, inspiration-selective filtering did not improve R^2.

Conclusions: The selective filtering of expiration data eliminates negative side-effects of cardiogenic oscillations thus leading to a significant improvement of the analysis of dynamic respiratory system mechanics.

Keywords: Controlled mechanical ventilation; cardiogenic oscillations; dynamic respiratory mechanics.

Publication types

  • Clinical Trial

MeSH terms

  • Equipment Design
  • Humans
  • Reproducibility of Results
  • Respiration, Artificial / instrumentation*
  • Respiratory Mechanics / physiology*
  • Retrospective Studies
  • Signal Processing, Computer-Assisted / instrumentation*