  Mathematical modeling and real-time dynamics identification of the mean
arterial blood pressure (MAP) response of a patient to vasoactive drug infusion
can provide a reliable tool for automated drug administration and therefore,
reduce the emergency costs and significantly benefit the patient's MAP
regulation in an intensive care unit. To this end, a dynamic first-order linear
parameter-varying (LPV) model with varying parameters and varying input delay
is considered to capture the MAP response dynamics. Such a model effectively
addresses the complexity and the intra- and inter-patient variability of the
physiological response. We discretize the model and augment the state vector
with model parameters as unknown states of the system and a Bayesian-based
multiple-model square root cubature Kalman filtering (MMSRCKF) approach is
utilized to estimate the model time-varying parameters. Since, unlike the other
model parameters, the input delay cannot be captured by a random-walk process,
a multiple-model module with a posterior probability estimation is implemented
to provide the delay identification. Validation results confirm the
effectiveness of the proposed identification algorithm both in simulation
scenarios and also using animal experiment data.
