On seasonal functional modeling under strong dependence, with applications to mechanically ventilated breathing activity

Abstract

Breathing effort in mechanical ventilation is commonly estimated by airway pressure. More advanced methods involve transdiaphragmatic pressure measurements (Pdi) or surface electromyography (sEMG) of the respiratory muscles. To study whether inspiratory efforts may be predicted by the noninvasive sEMG method, a model is proposed for time series with a stochastically changing periodic component. The model can be interpreted as a functional time series or a process based on a state space representation, with a flexible temporal dependence structure in the parameter process and the residuals, including long memory, short memory or antipersistence. An application to Pdi and sEMG measurements shows the potential usefulness of the method in the context of monitoring patients undergoing mechanical ventilation.