Abstract:
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In cases of severe respiratory failure, mechanical ventilation is an essential life saving measure. Effective application of mechanical ventilation depends on suitable estimation and prediction of the breathing effort. Advanced techniques include transdiaphragmatic pressure measurement (Pdi) or electromyography (EMG) of the respiratory muscles. Invasive methods (e.g. Pdi) are however difficult to apply, costly and may hinder prolonged weaning. To clarify in how far noninvasive surface EMG (sEMG) methods may provide sufficient information for monitoring patients on mechanical ventilation, a statistical model is developed based on multi-layer stationary processes. The model can be understood as an extension of state space models to the non-Markovian domain, including long-range dependence, or as a functional time series model. Statistical inference is based on a functional limit theorem for estimated state processes. Applications include (nonlinear) sEMG-based prediction intervals for Pdi curves, detection of changes in breathing patterns and monitoring. This is joint work with Jeremy Naescher, Franziska Farquharson, Max Kustermann, Hans-Joachim Kabitz, and Stephan Walterspacher.
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