  This work provides a formalized model-invariant safety system for closed-loop
anesthesia that uses feedback from measured data for model falsification to
reduce conservatism. The safety system maintains predicted propofol plasma
concentrations, as well as the patient's blood pressure, within safety bounds
despite uncertainty in patient responses to propofol. Model-invariant formal
verification is used to formalize the safety system. This technique requires a
multi-model description of model-uncertainty. Model-invariant verification
considers all possible dynamics of an uncertain system, and the resulting
safety system may be conservative for systems that do not exhibit the
worst-case dynamical response. In this work, we employ model falsification to
reduce conservatism of the model-invariant safety system. Members of a model
set that characterizes model- uncertainty are falsified if discrepancy between
predictions of those models and measured responses of the uncertain system is
established, thereby reducing model uncertainty. We show that including
falsification in a model-invariant safety system reduces conservatism of the
safety system.
